chore(recon): bump voice pin to Phase-A blocked backbone (f4e7eef)

WeSpeaker ResNet34 runs as one nChw16c blocked island (2 reorders/forward vs
~60) on AVX-512, default; per-conv directconv fallback on AVX2. +2.9% @1t /
+17-19% @8t vs per-conv directconv, parity cosine=1.0. The conv microkernel is
already FMA-bound near peak (~0.86-0.98x MLAS-implied); residual to MLAS is
sub-peak edge + non-conv tail, documented in docs/cpu-optimization.md.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]

.agents/adding-backends.md

@@ -198,6 +198,27 @@ docker-build-backends: ... docker-build-<backend-name>
 - If the backend is in `backend/python/<backend-name>/` but uses `.` as context in the workflow file, use `.` context
 - Check similar backends to determine the correct context
 
+## Documenting the backend (README + docs)
+
+A backend is not "added" until it is discoverable. Update the user-facing docs:
+
+- **`docs/content/features/backends.md`** - add the backend to the right
+  category in the "LocalAI supports various types of backends" list (and add a
+  new category if it introduces a new modality, e.g. sound classification).
+- If the backend introduces a **new API surface** (a new endpoint or a realtime
+  capability), document it under `docs/content/` where its area lives (audio,
+  vision, etc.) and follow the api-endpoints checklist in
+  [api-endpoints-and-auth.md](api-endpoints-and-auth.md).
+
+**If the backend is a native C/C++/GGML engine created and maintained by the
+LocalAI team** (a from-scratch port like `parakeet.cpp`, `ced.cpp`,
+`vibevoice.cpp`, `rf-detr.cpp`, not a wrapper around a third-party runtime), it
+ALSO belongs in the top-level **`README.md`** table under "native C/C++/GGML
+engines ... developed and maintained by the LocalAI project itself". Add a row
+linking the upstream engine repo with a one-line description. This is the
+project's showcase of its own engines; a new in-house backend that is missing
+from it is a documentation bug.
+
 ## 5. Verification Checklist
 
 After adding a new backend, verify:
@@ -211,6 +232,8 @@ After adding a new backend, verify:
 - [ ] No YAML syntax errors (check with linter)
 - [ ] No Makefile syntax errors (check with linter)
 - [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
+- [ ] Documented: added to the category list in `docs/content/features/backends.md` (and any new endpoint/realtime capability documented under `docs/content/`)
+- [ ] If it is an in-house native C/C++/GGML engine, added to the maintained-engines table in the top-level `README.md`
 
 ## Bundling runtime shared libraries (`package.sh`)
 

.docker/install-base-deps.sh

@@ -70,6 +70,12 @@ if [ "${BUILD_TYPE:-}" = "vulkan" ] && [ "${SKIP_DRIVERS:-false}" = "false" ]; t
         git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
         ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
         clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
+    # Mesa Vulkan ICD drivers (ANV/RADV/lavapipe + Arm SoC) and their ICD
+    # manifests. The LunarG SDK below only provides the loader and shader
+    # tooling, not hardware drivers — without Mesa the packaged Vulkan backend
+    # would ship a loader that finds no GPU. package-gpu-libs.sh bundles these
+    # .so files plus their deps into the backend so it stays self-contained.
+    apt-get install -y mesa-vulkan-drivers libdrm2
     if [ "amd64" = "${TARGETARCH:-}" ]; then
         wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz"
         tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz

.github/backend-matrix.yml

@@ -3575,6 +3575,450 @@ include:
     dockerfile: "./backend/Dockerfile.golang"
     context: "./"
     ubuntu-version: '2404'
+  # ced
+  - build-type: 'cublas'
+    cuda-major-version: "12"
+    cuda-minor-version: "8"
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-nvidia-cuda-12-ced'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "13"
+    cuda-minor-version: "0"
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-nvidia-cuda-13-ced'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "13"
+    cuda-minor-version: "0"
+    platforms: 'linux/arm64'
+    skip-drivers: 'false'
+    tag-latest: 'auto'
+    tag-suffix: '-nvidia-l4t-cuda-13-arm64-ced'
+    base-image: "ubuntu:24.04"
+    ubuntu-version: '2404'
+    runs-on: 'ubuntu-24.04-arm'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-ced'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-ced'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'sycl_f32'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-intel-sycl-f32-ced'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'sycl_f16'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-intel-sycl-f16-ced'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-ced'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-ced'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "12"
+    cuda-minor-version: "0"
+    platforms: 'linux/arm64'
+    skip-drivers: 'false'
+    tag-latest: 'auto'
+    tag-suffix: '-nvidia-l4t-arm64-ced'
+    base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
+    runs-on: 'ubuntu-24.04-arm'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2204'
+  - build-type: 'hipblas'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-rocm-hipblas-ced'
+    base-image: "rocm/dev-ubuntu-24.04:7.2.1"
+    runs-on: 'ubuntu-latest'
+    skip-drivers: 'false'
+    backend: "ced"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  # voice-detect
+  - build-type: 'cublas'
+    cuda-major-version: "12"
+    cuda-minor-version: "8"
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-nvidia-cuda-12-voice-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "13"
+    cuda-minor-version: "0"
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-nvidia-cuda-13-voice-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "13"
+    cuda-minor-version: "0"
+    platforms: 'linux/arm64'
+    skip-drivers: 'false'
+    tag-latest: 'auto'
+    tag-suffix: '-nvidia-l4t-cuda-13-arm64-voice-detect'
+    base-image: "ubuntu:24.04"
+    ubuntu-version: '2404'
+    runs-on: 'ubuntu-24.04-arm'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-voice-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-voice-detect'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'sycl_f32'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-intel-sycl-f32-voice-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'sycl_f16'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-intel-sycl-f16-voice-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-voice-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-voice-detect'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "12"
+    cuda-minor-version: "0"
+    platforms: 'linux/arm64'
+    skip-drivers: 'false'
+    tag-latest: 'auto'
+    tag-suffix: '-nvidia-l4t-arm64-voice-detect'
+    base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
+    runs-on: 'ubuntu-24.04-arm'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2204'
+  - build-type: 'hipblas'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-rocm-hipblas-voice-detect'
+    base-image: "rocm/dev-ubuntu-24.04:7.2.1"
+    runs-on: 'ubuntu-latest'
+    skip-drivers: 'false'
+    backend: "voice-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  # face-detect
+  - build-type: 'cublas'
+    cuda-major-version: "12"
+    cuda-minor-version: "8"
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-nvidia-cuda-12-face-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "13"
+    cuda-minor-version: "0"
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-nvidia-cuda-13-face-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "13"
+    cuda-minor-version: "0"
+    platforms: 'linux/arm64'
+    skip-drivers: 'false'
+    tag-latest: 'auto'
+    tag-suffix: '-nvidia-l4t-cuda-13-arm64-face-detect'
+    base-image: "ubuntu:24.04"
+    ubuntu-version: '2404'
+    runs-on: 'ubuntu-24.04-arm'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-face-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-face-detect'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'sycl_f32'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-intel-sycl-f32-face-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'sycl_f16'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-intel-sycl-f16-face-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-face-detect'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-face-detect'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "12"
+    cuda-minor-version: "0"
+    platforms: 'linux/arm64'
+    skip-drivers: 'false'
+    tag-latest: 'auto'
+    tag-suffix: '-nvidia-l4t-arm64-face-detect'
+    base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
+    runs-on: 'ubuntu-24.04-arm'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2204'
+  - build-type: 'hipblas'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-rocm-hipblas-face-detect'
+    base-image: "rocm/dev-ubuntu-24.04:7.2.1"
+    runs-on: 'ubuntu-latest'
+    skip-drivers: 'false'
+    backend: "face-detect"
+    dockerfile: "./backend/Dockerfile.golang"
+    context: "./"
+    ubuntu-version: '2404'
   # acestep-cpp
   - build-type: ''
     cuda-major-version: ""
@@ -4754,6 +5198,18 @@ includeDarwin:
     tag-suffix: "-metal-darwin-arm64-parakeet-cpp"
     build-type: "metal"
     lang: "go"
+  - backend: "ced"
+    tag-suffix: "-metal-darwin-arm64-ced"
+    build-type: "metal"
+    lang: "go"
+  - backend: "voice-detect"
+    tag-suffix: "-metal-darwin-arm64-voice-detect"
+    build-type: "metal"
+    lang: "go"
+  - backend: "face-detect"
+    tag-suffix: "-metal-darwin-arm64-face-detect"
+    build-type: "metal"
+    lang: "go"
   - backend: "acestep-cpp"
     tag-suffix: "-metal-darwin-arm64-acestep-cpp"
     build-type: "metal"

.github/workflows/bump_deps.yaml

@@ -42,6 +42,18 @@ jobs:
             variable: "PARAKEET_VERSION"
             branch: "master"
             file: "backend/go/parakeet-cpp/Makefile"
+          - repository: "mudler/ced.cpp"
+            variable: "CED_VERSION"
+            branch: "master"
+            file: "backend/go/ced/Makefile"
+          - repository: "mudler/voice-detect.cpp"
+            variable: "VOICEDETECT_VERSION"
+            branch: "master"
+            file: "backend/go/voice-detect/Makefile"
+          - repository: "mudler/face-detect.cpp"
+            variable: "FACEDETECT_VERSION"
+            branch: "master"
+            file: "backend/go/face-detect/Makefile"
           - repository: "mudler/depth-anything.cpp"
             variable: "DEPTHANYTHING_VERSION"
             branch: "master"

README.md

@@ -231,6 +231,7 @@ Most backends wrap a best-in-class upstream engine. A handful of them are native
 | Backend | What it does |
 |---------|-------------|
 | [parakeet.cpp](https://github.com/mudler/parakeet.cpp) | C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription |
+| [ced.cpp](https://github.com/mudler/ced.cpp) | C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition |
 | [voxtral.c](https://github.com/mudler/voxtral.c) | Voxtral Realtime 4B speech-to-text in pure C |
 | [vibevoice.cpp](https://github.com/mudler/vibevoice.cpp) | Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization |
 | [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp) | Native RF-DETR object detection and instance segmentation |
@@ -240,6 +241,8 @@ Most backends wrap a best-in-class upstream engine. A handful of them are native
 | [LocalVQE](https://github.com/localai-org/LocalVQE) | Joint acoustic echo cancellation, noise suppression, and dereverberation |
 | [local-store](https://github.com/mudler/LocalAI) | Local-first vector database for embeddings (shipped in-tree) |
 
+We also maintain [apex-quant](https://github.com/localai-org/apex-quant), a per-tensor, per-layer quantization recipe for Mixture-of-Experts models that exploits their structural sparsity to produce GGUFs matching or beating Q8_0 quality - and they run out of the box on stock llama.cpp.
+
 ## Resources
 
 - [Documentation](https://localai.io/)

backend/Dockerfile.golang

@@ -65,7 +65,12 @@ RUN <<EOT bash
             libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
             git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
             ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
-            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
+            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils && \
+        apt-get install -y mesa-vulkan-drivers libdrm2
+        # Mesa Vulkan ICD drivers (ANV/RADV/lavapipe) + their manifests. The
+        # LunarG SDK below only provides the loader and shader tooling, not
+        # hardware drivers — without Mesa, package-gpu-libs.sh has no ICD to
+        # bundle and the packaged backend finds no GPU at runtime.
         if [ "amd64" = "$TARGETARCH" ]; then
             wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
             tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \

backend/Dockerfile.python

@@ -66,7 +66,12 @@ RUN <<EOT bash
             libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
             git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
             ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
-            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
+            clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils && \
+        apt-get install -y mesa-vulkan-drivers libdrm2
+        # Mesa Vulkan ICD drivers (ANV/RADV/lavapipe) + their manifests. The
+        # LunarG SDK below only provides the loader and shader tooling, not
+        # hardware drivers — without Mesa, package-gpu-libs.sh has no ICD to
+        # bundle and the packaged backend finds no GPU at runtime.
         if [ "amd64" = "$TARGETARCH" ]; then
             wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
             tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \

backend/backend.proto

@@ -24,6 +24,9 @@ service Backend {
   rpc TokenizeString(PredictOptions) returns (TokenizationResponse) {}
   rpc Status(HealthMessage) returns (StatusResponse) {}
   rpc Detect(DetectOptions) returns (DetectResponse) {}
+  // SoundDetection runs an audio-tagging / sound-event-classification model
+  // (e.g. CED over the AudioSet ontology) on a clip and returns scored labels.
+  rpc SoundDetection(SoundDetectionRequest) returns (SoundDetectionResponse) {}
   rpc Depth(DepthRequest) returns (DepthResponse) {}
   rpc FaceVerify(FaceVerifyRequest) returns (FaceVerifyResponse) {}
   rpc FaceAnalyze(FaceAnalyzeRequest) returns (FaceAnalyzeResponse) {}
@@ -671,6 +674,24 @@ message DetectResponse {
   repeated Detection Detections = 1;
 }
 
+// --- Sound-event classification / audio tagging messages (CED) ---
+
+message SoundDetectionRequest {
+  string src = 1;       // audio file path (LocalAI writes the upload to disk)
+  int32 top_k = 2;      // number of top tags to return (0 = all classes)
+  float threshold = 3;  // optional: drop tags scoring below this
+}
+
+message SoundClass {
+  string label = 1;     // AudioSet class name, e.g. "Baby cry, infant cry"
+  float score = 2;      // per-class probability (multi-label, independent)
+  int32 index = 3;      // class index in the model ontology
+}
+
+message SoundDetectionResponse {
+  repeated SoundClass detections = 1;  // score-descending
+}
+
 // --- Depth estimation messages (Depth Anything 3) ---
 
 message DepthRequest {

backend/cpp/ik-llama-cpp/Makefile

@@ -1,5 +1,5 @@
 
-IK_LLAMA_VERSION?=b3dfb7858cfcb9166e92f366e5af87f19ebc94be
+IK_LLAMA_VERSION?=6c00e87ac84404af588ad2e65935bd6f079c696f
 LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
 
 CMAKE_ARGS?=

backend/cpp/llama-cpp/Makefile

@@ -1,14 +1,6 @@
 
-LLAMA_VERSION?=f3e182816421c648188b5eab269853bf1531d950
+LLAMA_VERSION?=e475fa2b5f9fb50c3d6fc3e7c6fdf1e004465b62
 LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
-# LLAMA_PAGED controls whether the vendored paged-attention patch series
-# (patches/paged/) is applied on top of the pinned llama.cpp. Default on; set
-# LLAMA_PAGED=off to build a clean-against-upstream backend (e.g. to unblock a
-# dep-bump if an upstream change breaks a paged hook - the paged carry is then
-# fixed independently). Runtime behaviour stays gated by the LLAMA_KV_PAGED env
-# regardless, so an LLAMA_PAGED=on build is byte-identical to stock until that
-# env is set.
-LLAMA_PAGED?=on
 
 CMAKE_ARGS?=
 BUILD_TYPE?=
@@ -145,28 +137,14 @@ llama.cpp:
 	git remote add origin $(LLAMA_REPO)  && \
 	git fetch --all --tags && \
 	git checkout -b build $(LLAMA_VERSION) && \
-	git submodule update --init --recursive --depth 1 --single-branch && \
+	git submodule update --init --recursive --depth 1 --single-branch
-	for p in $(CURRENT_MAKEFILE_DIR)patches/0*.patch; do \
-		[ -e "$$p" ] || continue; \
-		echo "applying llama.cpp patch: $$p"; \
-		git apply --verbose "$$p" || { echo "patch failed: $$p"; exit 1; }; \
-	done && \
-	if [ "$(LLAMA_PAGED)" = "off" ]; then \
-		echo "LLAMA_PAGED=off: skipping paged-attention patch series"; \
-	else \
-		for p in $(CURRENT_MAKEFILE_DIR)patches/paged/0*.patch; do \
-			[ -e "$$p" ] || continue; \
-			echo "applying llama.cpp PAGED patch: $$p"; \
-			git apply --verbose "$$p" || { echo "paged patch failed: $$p"; exit 1; }; \
-		done; \
-	fi
 
 llama.cpp/tools/grpc-server: llama.cpp
 	mkdir -p llama.cpp/tools/grpc-server
-	LLAMA_PAGED=$(LLAMA_PAGED) bash prepare.sh
+	bash prepare.sh
 
 rebuild:
-	LLAMA_PAGED=$(LLAMA_PAGED) bash prepare.sh
+	bash prepare.sh
 	rm -rf grpc-server
 	$(MAKE) grpc-server
 

backend/cpp/llama-cpp/grpc-server.cpp

@@ -18,6 +18,18 @@
 #if __has_include("server-chat.cpp")
 #include "server-chat.cpp"
 #endif
+// server-schema.cpp exists only in llama.cpp after the upstream refactor that
+// extracted the JSON request-schema evaluation (previously the static
+// server_task::params_from_json_cmpl) into server_schema::eval_llama_cmpl_schema.
+// server-context.cpp and grpc-server.cpp both call into it, so its definitions
+// must be part of this translation unit or the link fails. __has_include keeps
+// the source compatible with older pins/forks (e.g. llama-cpp-turboquant) that
+// predate the split and still expose params_from_json_cmpl (see the guarded
+// call sites below).
+#if __has_include("server-schema.cpp")
+#define LOCALAI_HAS_SERVER_SCHEMA 1
+#include "server-schema.cpp"
+#endif
 #include "server-context.cpp"
 
 // LocalAI
@@ -732,63 +744,6 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
             } else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
                 params.kv_unified = false;
             }
-        // --- paged KV cache (experimental, off by default) ---
-        // Enables the on-demand paged KV-cache engine (vendored PagedKVManager
-        // + paged placement/gather/alloc seams). The engine is gated inside
-        // llama.cpp by the LLAMA_KV_PAGED env var, evaluated once at first use;
-        // here we expose it as a per-server model option instead of forcing the
-        // operator to export a process-wide env. When enabled we set the env
-        // BEFORE the model/context is created (later in this handler), so the
-        // engine latches on. When the option is absent we touch nothing, so an
-        // externally exported LLAMA_KV_PAGED still works as an escape hatch.
-        // Note: the engine's env check is process-wide and latches on first
-        // use, so enabling it for one model enables it for the worker process;
-        // LocalAI runs one model per llama.cpp worker, so this maps cleanly to
-        // per-server configuration. `kv_paged_debug` turns on the per-slot
-        // [paged-alloc]/free trace (LLAMA_KV_PAGED_DEBUG).
-        //
-        // The continuous-batching serving loop (update_slots) drives paged KV
-        // transparently through the existing kv-cache seams: each slot's
-        // sequence allocates paged blocks on arrival (find_slot placement) and
-        // returns them on slot release (the seq_rm free seam). This is
-        // token-identical to stock under both the unified and per-sequence
-        // caches. The per-slot allocate/free capacity benefit, however, only
-        // materialises with a per-sequence cache, since paged block ownership
-        // is keyed by stream and the unified cache collapses every slot onto a
-        // single stream. Operators who want that benefit should pair this with
-        // `kv_unified:false`; we do NOT flip kv_unified here, to keep the
-        // default serving behaviour (and the idle-slot prompt cache) unchanged.
-        } else if (!strcmp(optname, "kv_paged") || !strcmp(optname, "paged_kv") || !strcmp(optname, "paged_attention")) {
-            if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
-                setenv("LLAMA_KV_PAGED", "1", 1);
-            }
-        } else if (!strcmp(optname, "kv_paged_debug") || !strcmp(optname, "paged_kv_debug")) {
-            if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
-                setenv("LLAMA_KV_PAGED_DEBUG", "1", 1);
-            }
-        // --- chunked-prefill QoS budget (experimental, off by default) ---
-        // Caps the number of prompt tokens any single slot may prefill per
-        // update_slots iteration, so a large prompt cannot monopolise the batch
-        // and freeze the in-flight decoders. The serving loop reads this budget
-        // from the LLAMA_PREFILL_BUDGET env var (set BEFORE context init, like
-        // kv_paged above) and splits oversized prompts across iterations,
-        // interleaving decode steps for the other slots. A 6k-token prefill that
-        // stalled 8 decoders ~3.4s drops to ~780ms at budget=512 (4.8x stall
-        // cut) with zero TTFT cost and no steady-state regression. Unset or a
-        // non-positive value leaves the env untouched, so the stock unbounded
-        // prefill behaviour is preserved (an externally exported
-        // LLAMA_PREFILL_BUDGET still works as an escape hatch).
-        } else if (!strcmp(optname, "max_prefill_tokens") || !strcmp(optname, "mpt") || !strcmp(optname, "prefill_budget")) {
-            if (optval != NULL) {
-                try {
-                    int budget = std::stoi(optval_str);
-                    if (budget > 0) {
-                        setenv("LLAMA_PREFILL_BUDGET", std::to_string(budget).c_str(), 1);
-                    }
-                } catch (const std::exception& e) {
-                    // If conversion fails, leave the budget unset (stock behaviour)
-                }
-            }
         } else if (!strcmp(optname, "n_ctx_checkpoints") || !strcmp(optname, "ctx_checkpoints")) {
             if (optval != NULL) {
                 try {
@@ -2159,7 +2114,11 @@ public:
                 task.index = i;
 
                 task.tokens    = std::move(inputs[i]);
+#ifdef LOCALAI_HAS_SERVER_SCHEMA
+                task.params           = server_schema::eval_llama_cmpl_schema(
+#else
                 task.params           = server_task::params_from_json_cmpl(
+#endif
                         ctx_server.impl->vocab,
                         params_base,
                         ctx_server.get_meta().slot_n_ctx,
@@ -2173,7 +2132,7 @@ public:
                 // cannot detect tool calls or separate reasoning from content.
                 task.params.res_type                 = TASK_RESPONSE_TYPE_OAI_CHAT;
                 task.params.oaicompat_cmpl_id         = completion_id;
-                // oaicompat_model is already populated by params_from_json_cmpl
+                // oaicompat_model is already populated by eval_llama_cmpl_schema
 
                 tasks.push_back(std::move(task));
             }
@@ -2997,7 +2956,11 @@ public:
                 task.index = i;
 
                 task.tokens    = std::move(inputs[i]);
+#ifdef LOCALAI_HAS_SERVER_SCHEMA
+                task.params           = server_schema::eval_llama_cmpl_schema(
+#else
                 task.params           = server_task::params_from_json_cmpl(
+#endif
                         ctx_server.impl->vocab,
                         params_base,
                         ctx_server.get_meta().slot_n_ctx,
@@ -3009,7 +2972,7 @@ public:
                 // reasoning, tool calls, and content are classified into ChatDeltas.
                 task.params.res_type                 = TASK_RESPONSE_TYPE_OAI_CHAT;
                 task.params.oaicompat_cmpl_id         = completion_id;
-                // oaicompat_model is already populated by params_from_json_cmpl
+                // oaicompat_model is already populated by eval_llama_cmpl_schema
 
                 tasks.push_back(std::move(task));
             }

backend/cpp/llama-cpp/paged/.gitignore

@@ -1,7 +0,0 @@
-tests/test_free_block_queue
-tests/test_block_pool
-tests/test_paged_kv_manager
-tests/test_prefix_cache
-tests/test_ggml_paged_rw
-tests/test_ggml_paged_attn
-paged-bench

backend/cpp/llama-cpp/paged/BLACKWELL_KERNEL_GAPS.md

@@ -1,105 +0,0 @@
-# Blackwell (GB10 / sm_121) kernel gaps — measured + the corrected strategy
-
-Supersedes the "greenfield tcgen05 FP4 grouped GEMM" framing in `FP4_GROUPED_MOE_KERNEL.md`. Research +
-profiling reframed the problem: the kernels we need **already exist in ggml**; they're just **untuned for
-Blackwell**. And the parity target is far lower than the headline vLLM number implied.
-
-## 1. The parity target was wrong — it's ~3,300 t/s single-stream, not 24,444
-
-vLLM's dense "24,444 t/s" is **aggregate concurrent-batch** throughput, not single-sequence. The GB10
-compute roofline caps **single-stream** Qwen3-32B prefill at **~3,300 t/s (BF16/INT8 ceiling)** / **~6,600
-(FP4 ceiling)**. So: don't chase 24,444 with one kernel. Aggregate parity = (a kernel at the ceiling) +
-(batched-prefill scheduling). The *kernel* job is to reach ~3,300 (matches vLLM, which on GB10 also runs at
-the BF16 ceiling) or ~6,600 (beats it, via FP4).
-
-## 2. GB10 per-precision DENSE peaks (measured, not spec)
-
-| precision | dense peak | vs BF16 |
-|---|---|---|
-| BF16 / FP16 | ~213 TFLOP/s | 1.0× |
-| INT8 | ~215 TOPS | **1.0×** |
-| FP4 (MXFP4/NVFP4) | ~427–500 TFLOP/s | **2.0×** |
-
-Memory: ~273 GB/s LPDDR5X (the bottleneck for *decode*; prefill is compute-bound). **Critical:** GB10 is
-**1:1:2** (BF16:INT8:FP4), NOT datacenter Blackwell's 1:2:4 — **INT8 gives ZERO speedup over BF16 here.** So
-int8-MMQ has no precision advantage; only FP4 does. (NVIDIA spec sheets still claim 1:2:4 — contradicted by
-direct GB10 measurement; on-the-record discrepancy.)
-
-## 3. Measured gaps (nsys, GB10)
-
-| path | kernel | % of prefill | achieved | % of ceiling |
-|---|---|---|---|---|
-| **Dense** Q4_K_M | `mul_mat_q<Q4_K/Q6_K>` (int8 MMQ) | 80% | ~46 TFLOP/s | **~21% of 215** |
-| **MoE** MXFP4 | `mul_mat_q<MXFP4>` (FP4 MMA) | 37% | ~22 TFLOP/s | **~4–5% of 500** (or ~10% of BF16) |
-
-Both kernels are **engaged correctly but untuned for Blackwell** — llama.cpp's MMQ was "tuned primarily for
-RTX 3000/4000" (Ampere/Ada). The headroom (4–5×) is recoverable; it's not an architectural ceiling.
-
-## 4. ggml's current quantized-matmul paths (what exists)
-
-- **MMQ** (int8): quantizes activations to Q8_1, int8 `mma.sync`/`dp4a`. Prefill path. **Untuned for sm_12x.**
-- **FP4 MMA** (#17906, merged): native MXFP4/NVFP4 `m16n8k64` block-scaled FP4 mma for cc≥12.0. Works on GB10
-  for MoE (we measured 3441 t/s MXFP4 prefill) — but underutilized (~5% of FP4 peak). On **sm_121** it's hit
-  by build-flag (`120f`) + nvcc `-O3` miscompile (#18331) + capability-gating issues.
-- **dequant→cuBLAS-FP16**: unfused fallback (materializes FP16 weights, round-trips memory). Not a fused
-  Marlin. (Our `GGML_CUDA_FORCE_CUBLAS` no-op = this didn't even engage for Q4_K.)
-- **NO fused Marlin-style W4A16 kernel** (dequant 4-bit→BF16 in-shared-mem → BF16 tensor cores). Real gap.
-
-## 5. Strategy — match vs beat (this replaces the tcgen05-greenfield plan)
-
-**To MATCH vLLM (~3,300 single-stream): FP4 is NOT required.** Because INT8 == BF16 on GB10, a tuned MMQ and
-a BF16 Marlin kernel share the *same* ceiling — and vLLM hits parity via W4A16 Marlin (BF16), since its FP4
-is also broken on sm_121.
-
-Ranked, by effort:
-1. **Probe: tune the existing int8 MMQ for Blackwell** (dense). Cheapest. We're at 21% of the ceiling —
-   recover via tile sizes, async copy (`cp.async`), double-buffered shared-mem pipeline, occupancy. Caveat:
-   the `nwarps*tile_C::I==mmq_y` static_assert (found earlier) couples the constants; and the Q8_1
-   activation-quant overhead caps pure-MMQ tuning. Bounded upside, but a fast experiment.
-2. **Build a Marlin-style W4A16 BF16 GEMM** (dense) — the robust path to ~3,300 (4.3× over today's 765).
-   Dequant 4-bit→BF16 in shared memory, MMA on BF16 tensor cores, `cp.async` multi-buffer, offline weight
-   reshuffle. Mirrors vLLM's actual GB10 path; keeps activations BF16 (better quality than int8 MMQ); fills a
-   genuine ggml gap. **This is the recommended kernel to MATCH.**
-
-**To BEAT vLLM (~6,600, 2×): fix — don't rewrite — the FP4 path on sm_121.**
-3. **Get the existing FP4 MMA (#17906/#20644) fully working + tuned on sm_121.** It already works on sm_120
-   (RTX 5090: +43–68% prefill) and on GB10 for MoE. The blockers are the `120f` arch flag, the `-O3`
-   miscompile (#18331), capability gating — **build/compiler fixes, not a new kernel.** Then tune the FP4 MMQ
-   (it's at ~5% of FP4 peak). This is where upstream momentum already is, and the only route past vLLM.
-
-**Dropped:** the from-scratch tcgen05/CUTLASS grouped GEMM (the old scaffold). It aimed past the matchable
-ceiling, duplicates work the FP4-MMA path already does, and FP4 on sm_121 is a *fix* problem not a *write*
-problem. The `fp4-grouped-moe.cu` scaffold/hook stays as a useful dispatch seam, but the kernel behind it
-should be one of (1)/(2)/(3), not a greenfield CUTLASS collective.
-
-## 6. Cheap experiment — RESULT: MXFP4 dense = free 1.44×, but not parity (kernel still untuned)
-
-Requantized Qwen3-32B dense → MXFP4 (forced attn+ffn to mxfp4 via `--tensor-type`, `--allow-requantize`,
-speed-only test) and benched prefill:
-
-| quant | kernel | pp512 | pp2048 | vs Q4_K |
-|---|---|---|---|---|
-| Q4_K_M | int8-MMQ | 765 | 763 | 1.0× |
-| **MXFP4** | **FP4-MMA** | **1099** | **1153** | **1.44×** |
-
-**Findings:**
-- **MXFP4 dense is a real, free 1.44× over Q4_K** — just a requantize, the existing FP4-MMA path engages for
-  dense weights on GB10. Worth shipping as a **Blackwell dense-quant recommendation** in the gallery (no kernel).
-- **But it is NOT parity.** 1153 t/s = **~17% of the FP4 ceiling (~6,600)** / ~35% of the BF16 ceiling. So the
-  **FP4-MMA kernel is itself untuned** (consistent with the MoE measurement, ~5% of FP4 peak). MXFP4 moves dense
-  from the int8 path (765) onto the FP4 path (1153), but the FP4 kernel leaves ~4–6× on the table.
-- **So the kernel work is confirmed and now precise: tune the FP4-MMA kernel** (it's the highest-value, since it
-  serves both dense-MXFP4 and MoE, and FP4 is the only path that can *beat* vLLM). Strategy item (3) — fix +
-  tune the existing FP4-MMA on sm_121 — is the priority; a Marlin-style W4A16 BF16 kernel (2) is the alternative
-  to *match* on the BF16 ceiling if FP4 tuning stalls.
-
-Conclusion: the cheap test did NOT collapse the kernel problem (the kernels are untuned, not just the quant), but
-it (a) gives a free 1.44× to ship now, and (b) sharpens the target to **tuning the FP4-MMA kernel**.
-
-## Sources
-GB10 peaks (measured): forums.developer.nvidia.com/t/351993, /360142, /373618. Marlin: github.com/IST-DASLab/marlin,
-arxiv 2408.11743, developers.redhat.com Marlin/Machete. MMQ untuned: llama.cpp docs/build.md, discussions/16578,
-DandinPower/llama.cpp_bench. FP4 landing/sm121: llama.cpp PR #17906/#20644, issues #19662/#18331. Roofline:
-vllm.ai/blog/2026-06-01-vllm-dgx-spark, lmsys.org DGX Spark.
-
-> **Correction (measured):** the earlier `GGML_CUDA_FORCE_CUBLAS` env test was a no-op because it's a *compile-time* `#ifdef`, not a runtime flag — cuBLAS never engaged. A real rebuild with `-DGGML_CUDA_FORCE_CUBLAS=ON` shows cuBLAS is **slower** than MMQ for dense Q4 (pp2048 690 vs 750) and runs an **Ampere `cutlass_80_tensorop` FP16 kernel** — cuBLAS-13.0 has no sm_121-tuned GEMM and falls back to sm_80. So *both* MMQ and cuBLAS sit at ~46 TFLOP/s (~21% of the 213 BF16 peak); there is **no library shortcut** to the ceiling on GB10 — a hand-tuned sm_120a kernel (Marlin-style) is required.

backend/cpp/llama-cpp/paged/CHUNKED_PREFILL_PLAN.md

@@ -1,334 +0,0 @@
-# Chunked prefill + n_batch/n_ubatch decouple — implementation plan
-
-Scope: LocalAI's llama.cpp backend (`backend/cpp/llama-cpp/`). Companion to
-`PHASED_VLLM_PARITY_PLAN.md` Phase 3. This document is the concrete, file-cited
-plan for what the brief called "chunked prefill".
-
-Line numbers below are from two trees:
-- LocalAI: `backend/cpp/llama-cpp/grpc-server.cpp`, `core/backend/options.go`,
-  `backend/backend.proto`, `core/backend/hardware_defaults.go` — exact.
-- Vendored upstream scheduler: `llama.cpp/tools/server/server-context.cpp`. The
-  build copies `llama.cpp/tools/server/*` into `tools/grpc-server/` (`prepare.sh`
-  lines 15-17) and only overrides `grpc-server.cpp` + `CMakeLists.txt`. So
-  `update_slots()` is **inherited upstream code, not LocalAI code**. Line numbers
-  cited for it are from a same-era checkout (`d12cc3d`, 2026-04-09); the pin is
-  `f3e1828` (Makefile line 2). The structure is identical; exact lines may drift
-  a few rows at the pin — match on the quoted comment strings, not the integers.
-
----
-
-## TL;DR — the headline finding
-
-**Chunked prefill with prefill/decode interleaving is ALREADY implemented** in the
-llama.cpp server scheduler that LocalAI vendors. It is not a missing feature on
-this version. `update_slots()` in `server-context.cpp`:
-
-1. **Adds ongoing decode tokens first** — "first, add sampled tokens from any
-   ongoing sequences" (≈ line 2088). Every `SLOT_STATE_GENERATING` slot gets its
-   one sampled token into the shared `llama_batch` before any prefill is added.
-2. **Then fills the remaining `n_batch` budget with prompt (prefill) tokens** —
-   "next, batch any pending prompts without exceeding n_batch" (≈ line 2166),
-   gated by `params_base.cont_batching` (LocalAI sets `cont_batching = true` by
-   default, `grpc-server.cpp:547`). The per-slot prefill fill loop
-   (≈ line 2552) is `while (slot.prompt.n_tokens() < slot.task->n_tokens() &&
-   batch.n_tokens < n_batch)` — i.e. it caps each slot's prefill contribution to
-   the **remaining** budget and defers the rest to the next iteration.
-3. **Decodes the combined batch in one pass** (≈ line 2728-2741): decode tokens
-   and prefill-chunk tokens go through the **same `llama_decode`**, which then
-   splits internally into `n_ubatch` physical sub-batches.
-
-This is exactly the behavior the abandoned-looking draft **upstream PR #10718**
-("server : chunked prefill support") asked for — "the first task is no longer
-blocked by the second long prompt processing task." That PR is still marked OPEN
-but its goal was absorbed into the natural evolution of `update_slots()`; we do
-**not** need to port it. A long prefill no longer stalls the decode batch: decode
-slots are serviced first every iteration, prefill consumes only the leftover
-budget.
-
-**Therefore: do not re-implement chunked prefill.** The real LocalAI gap is
-narrow and is the rest of this plan:
-
-- **Phase A (the actual gap): the `n_batch`/`n_ubatch` decouple.** LocalAI ties
-  the scheduler token budget (`n_batch`) to the physical forward width
-  (`n_ubatch`) at `grpc-server.cpp:515` + `:519`. This forces
-  `n_batch == n_ubatch`, so the logical scheduling window can never be wider than
-  one physical ubatch. You cannot keep `n_ubatch` at the Blackwell GEMM sweet
-  spot (2048) while widening `n_batch` so concurrent prefills + decodes co-batch
-  into a larger logical window. There is no first-class `batch:`/`ubatch:` split
-  on the Go side, and there is only a one-directional `ubatch` override on the C++
-  side (you can shrink ubatch below the coupled value, never grow n_batch above
-  it).
-- **Phase B (optional policy lever): a decode-headroom prefill cap.** Upstream
-  caps prefill at the full `n_batch` shared with decode. Under heavy mixed load
-  one fat prefill chunk per iteration still adds inter-token latency (ITL) jitter
-  to the decoders sharing that forward. vLLM exposes
-  `long_prefill_token_threshold` / `max_num_partial_prefills` for this. A
-  LocalAI-specific per-iteration prefill cap (a patch to vendored `update_slots`)
-  bounds that jitter. This is genuinely not in upstream and is the only place a
-  scheduler-policy change is warranted.
-
----
-
-## 1. Current behavior — precise citations
-
-### 1.1 The scheduler is upstream, inherited verbatim
-- `prepare.sh:15-17` copies all of `llama.cpp/tools/server/*` into the
-  `grpc-server` build dir; `grpc-server.cpp` (LocalAI) replaces only the HTTP/gRPC
-  service + `params_parse` + `parse_options`. `update_slots()`, the slot state
-  machine, and the batch builder are **upstream `server-context.cpp`**, untouched
-  by LocalAI today.
-- Slot states: `server-context.cpp:36-42` —
-  `SLOT_STATE_IDLE / WAIT_OTHER / STARTED / PROCESSING_PROMPT / DONE_PROMPT /
-  GENERATING`.
-
-### 1.2 Decode-first, then prefill-fill, one shared batch
-- `common_batch_clear(batch)` (≈ 2078) — one batch per `update_slots` iteration.
-- Decode phase (≈ 2088-2156): for each `SLOT_STATE_GENERATING` slot,
-  `common_batch_add(batch, slot.sampled, …, /*logits=*/true)` adds exactly one
-  token. Decode is guaranteed a seat before prefill runs.
-- Budget fetch (≈ 2158-2160): `n_batch = llama_n_batch(ctx)`,
-  `n_ubatch = llama_n_ubatch(ctx)`.
-- Prefill phase (≈ 2166): `if (params_base.cont_batching || batch.n_tokens == 0)`
-  → with cont_batching ON, prefill is added to the **same** batch as decode.
-- Per-slot prefill fill (≈ 2552-2597):
-  `while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.n_tokens < n_batch)`
-  — adds prompt tokens until the slot is done **or** the shared budget is hit.
-  Whatever does not fit stays for the next iteration (the slot remains
-  `SLOT_STATE_PROCESSING_PROMPT`).
-- Whole-prompt completion (≈ 2603-2615): when the slot's prompt is fully consumed
-  it flips to `SLOT_STATE_DONE_PROMPT`, sets `batch.logits[last] = true`, inits
-  the sampler. Next iteration it becomes `GENERATING`.
-- Budget break (≈ 2693-2695): `if (batch.n_tokens >= n_batch) break;`.
-- Decode (≈ 2728-2741): loops `batch_view` slices of `min(n_batch, remaining)` and
-  calls `llama_decode`; the physical `n_ubatch` split happens inside
-  `llama_decode`.
-
-### 1.3 The chunking is gated by `can_split()`
-- `server-context.cpp:225-231`: `can_split()` returns true unless the task needs
-  embeddings with non-LAST pooling. So **completion/generation tasks always
-  chunk-and-interleave**; only embeddings/rerank force the whole prompt into one
-  ubatch (≈ 2234-2244 raises "input is too large… increase the physical batch
-  size" — this is exactly why LocalAI bumped `n_ubatch` for rerank, see below).
-
-### 1.4 LocalAI ties n_batch to n_ubatch (the gap)
-- `grpc-server.cpp:515` — `params.n_batch  = request->nbatch();`
-- `grpc-server.cpp:519` — `params.n_ubatch = request->nbatch();` with the comment
-  that this fixes reranking being capped at the 512 default `n_ubatch`.
-- `grpc-server.cpp:781-784` — the **only** decouple knob today: an `n_ubatch` /
-  `ubatch` option that overrides `n_ubatch` alone (added for embeddings/rerank).
-  There is **no** `batch` / `n_batch` option parse, so `n_batch` cannot be raised
-  above the coupled value from a model config. Confirmed: `grep '"n_batch"|"batch"'`
-  in `grpc-server.cpp` returns nothing.
-- Options arrive via `request->options(i)` parsed as `optname:optval`
-  (`grpc-server.cpp:584-585`); these come from `ModelOptions.Options` ⟵
-  `c.Options` (`core/backend/options.go:221`).
-
-### 1.5 Go side sends a single batch number
-- `backend/backend.proto:341` — `int32 NBatch = 4;` is the only batch field; there
-  is **no** `NUBatch`.
-- `core/backend/options.go:108-129` `EffectiveBatchSize`: returns `c.Batch` if set,
-  else context size for single-pass (score/embed/rerank), else
-  `hardwareDefaultBatchSize(512)`.
-- `core/backend/options.go:228` — `NBatch: int32(b)` (single value to the
-  backend; becomes both `n_batch` and `n_ubatch` via 1.4).
-- `core/backend/hardware_defaults.go:28,37-40` — `BlackwellBatchSize = 2048`;
-  on Blackwell an unset batch defaults to 2048, so today
-  `n_batch == n_ubatch == 2048` there.
-
----
-
-## 2. Why the decouple matters for serving (not just rerank)
-
-Invariant: `n_ubatch <= n_batch`. `n_ubatch` is the physical forward-pass GEMM
-width (compute efficiency; GB10 sweet spot ≈ 2048). `n_batch` is the per-iteration
-**scheduler token budget** — the logical window shared by decode + prefill chunks,
-analogous to vLLM's `max_num_batched_tokens`.
-
-With `n_batch == n_ubatch` (today), the scheduling window cannot exceed one
-physical ubatch. Consequences:
-- Under concurrency, the combined (decode + multiple prefill chunks) logical batch
-  is capped at the physical ubatch, so aggregate prefill cannot grow past one
-  ubatch worth of tokens per iteration even when more slots have prompts queued.
-- A user who shrinks `batch:` for memory also shrinks the physical ubatch,
-  degrading prefill GEMM efficiency — and vice versa.
-
-Decoupling lets us hold `n_ubatch = 2048` (efficient GEMM) while setting a larger
-`n_batch` (e.g. 4096) so more concurrent prefill+decode tokens co-schedule into one
-logical window, lifting aggregate prefill under mixed load — `llama_decode` still
-tiles the physical work at 2048.
-
----
-
-## 3. Phased implementation
-
-### Phase 0 — Verification harness (do first; TDD red)
-Bite-sized, no code change to the scheduler.
-- **0.1 Token-identical greedy under mixed load.** Script: start the backend with
-  `n_parallel >= 4`, greedy sampling (temp 0, fixed seed). Fire (a) several short
-  decode streams and (b) one ~8k-token prompt concurrently (the exact repro from
-  PR #10718's body works). Capture each stream's full token id sequence. Re-run
-  with the prefill request absent. **Assert the short streams' token ids are
-  byte-identical** in both runs — proves interleaving does not perturb decode
-  numerics (KV/position correctness across chunk boundaries). Wire as a Ginkgo
-  spec under the backend e2e suite.
-- **0.2 Mixed-workload throughput baseline.** Use `llama-batched-bench` (built from
-  the same tree) or a small driver hitting `/v1/chat/completions`: measure
-  aggregate prefill tok/s and decode tok/s, and p50/p99 ITL of the decode streams,
-  under the mixed workload. Record numbers for the current `n_batch==n_ubatch`
-  config. This is the before of Phase A/B.
-
-Expected result of Phase 0: 0.1 already passes (interleave is correct today);
-0.2 gives the baseline the decouple must beat.
-
-### Phase A — Decouple n_batch from n_ubatch
-Goal: let model config set the physical ubatch independently of the logical batch,
-defaulting to today's behavior (no regression).
-
-- **A.1 C++: accept a `batch`/`n_batch` option (and keep `ubatch`).**
-  In `grpc-server.cpp`, after the existing `ubatch` branch (`:781-784`), add a
-  sibling branch:
-  ```cpp
-  } else if (!strcmp(optname, "n_batch") || !strcmp(optname, "batch")) {
-      if (optval != NULL) {
-          try { params.n_batch = std::stoi(optval_str); } catch (...) {}
-      }
-  ```
-  This is the missing direction (raise `n_batch` above the coupled value). Order
-  matters: both `:515/:519` run first (coupling as default), then option parsing
-  overrides either independently. Add a clamp note: if a user sets
-  `n_ubatch > n_batch`, llama.cpp will clamp/upbatch; log a warning. Keep the
-  `:519` aliasing for backward compat (rerank still works with no options).
-
-- **A.2 Proto: add an explicit physical ubatch field.**
-  `backend/backend.proto:341` add `int32 NUBatch = <next free tag>;` (do not reuse
-  4). Regenerate with `make protogen-go` + the C++ proto build.
-
-- **A.3 C++: honor `NUBatch` when present.**
-  In `grpc-server.cpp` `params_parse`, after `:519`, add:
-  ```cpp
-  if (request->nubatch() > 0) {
-      params.n_ubatch = request->nubatch();
-  }
-  ```
-  so an explicit physical ubatch wins over the `n_batch` alias, with the `ubatch`
-  string-option as a third path for users who only edit `options:`.
-
-- **A.4 Go: config surface + plumbing.**
-  - Add `UBatch *int` (yaml `ubatch`) to the llama config struct alongside `Batch`
-    (search `core/config` for the `Batch` field; mirror it).
-  - In `core/backend/options.go`: add `EffectiveUBatchSize(c)` mirroring
-    `EffectiveBatchSize` (return `c.UBatch` if set, else
-    `min(EffectiveBatchSize(c), BlackwellBatchSize-or-512)` so the physical ubatch
-    stays at the hardware sweet spot while `n_batch` may be larger). Set
-    `NUBatch: int32(EffectiveUBatchSize(c))` next to `NBatch:` (`:228`).
-  - Keep the default such that when neither is set, `NUBatch == NBatch` ⇒
-    byte-identical to today.
-
-- **A.5 Serving default (the lever).**
-  In `hardware_defaults.go`, introduce `BlackwellLogicalBatch = 4096` (or a
-  measured value) and let `EffectiveBatchSize` return it for **multi-slot serving**
-  configs (when `n_parallel > 1` and the model is a completion model), while
-  `EffectiveUBatchSize` stays at `BlackwellBatchSize = 2048`. Gate behind the same
-  Blackwell detection already used at `:37-40`. Single-stream/embedding/rerank
-  paths keep `n_batch == n_ubatch`. This is the only behavioral change shipped by
-  Phase A; Phase 0.2 must show it is net-positive before defaulting it on.
-
-- **A.6 Tests.** Extend `hardware_defaults_internal_test.go` with
-  `EffectiveUBatchSize` cases; add a `grpcModelOpts` test asserting
-  `NUBatch <= NBatch` and that unset config yields `NUBatch == NBatch`. Re-run
-  0.1 (must still be token-identical) and 0.2 (must show aggregate-prefill gain or
-  neutral ITL) at `n_batch=4096, n_ubatch=2048`.
-
-### Phase B — Decode-headroom prefill cap (optional policy, vendored patch)
-Only if Phase 0.2 / A shows decode ITL jitter from fat prefill chunks. This is the
-one change that touches the inherited scheduler, so it lives as a patch in
-`backend/cpp/llama-cpp/patches/` (applied by `prepare.sh:6-11` / Makefile
-`:141-145`), never as an edit to a checked-in upstream file.
-
-Policy (pseudocode; insert into `update_slots()` prefill fill loop, the
-`while (… && batch.n_tokens < n_batch)` at ≈ `server-context.cpp:2552`):
-
-```
-# token budget for THIS iteration, decode already seated:
-n_decode_in_batch = batch.n_tokens            # set after the decode phase
-prefill_budget    = n_batch                    # default == today
-
-if serving_mode and n_decode_in_batch > 0:
-    # leave room so decoders are not starved/jittered by one giant prefill chunk
-    # max_prefill_per_iter defaults to n_ubatch (one physical tile) when decode active
-    prefill_budget = min(n_batch, n_decode_in_batch + max_prefill_per_iter)
-
-# fill loop guard becomes:
-while slot.prompt.n_tokens() < slot.task->n_tokens()
-      and batch.n_tokens < prefill_budget:
-      ...
-```
-
-- `max_prefill_per_iter` is a new `common_params` field surfaced as an
-  `options:` knob (`max_prefill_tokens` / `mpt`) parsed in `grpc-server.cpp`
-  exactly like A.1, default `0` = disabled = today's behavior.
-- Semantics mirror vLLM `long_prefill_token_threshold`: cap the prefill share so
-  ongoing decodes keep a steady cadence; the remaining prompt rides the next
-  iteration (already supported by the state machine — slot stays
-  `PROCESSING_PROMPT`).
-- **Correctness:** unchanged KV/position path — chunk boundaries already advance
-  `slot.prompt.tokens.pos_next()` per added token (≈ 2570) and the slot resumes
-  from `slot.prompt.n_tokens()` next iteration. Capping the budget only changes
-  *how many* tokens are added this iteration, not *which* positions, so 0.1 must
-  remain token-identical.
-
-### Phase C — Docs + defaults rollout
-- Document `batch` / `ubatch` (and `max_prefill_tokens` if B ships) in
-  `docs/content/` model-config reference, with the serving recipe
-  (`n_parallel>1`, `n_batch=4096`, `ubatch=2048`).
-- Note the orthogonality to paged KV (below) in
-  `PHASED_VLLM_PARITY_PLAN.md` Phase 3.
-
----
-
-## 4. Risk / correctness
-
-- **KV-cache & positions across chunks:** already handled upstream. Each prefill
-  token added advances `pos_next()` (≈ 2570) and is pushed to `slot.prompt.tokens`
-  (≈ 2573); the next iteration resumes from `slot.prompt.n_tokens()`. Chunk
-  boundaries are transparent to the KV cache because positions are absolute, not
-  per-chunk. Phase A changes only budgets, not positions; Phase B changes only the
-  per-iteration count. The 0.1 token-identical test is the guardrail.
-- **Unified KV cache (LocalAI default, `n_parallel` slots share one cache):**
-  unaffected — co-batching prefill+decode across slots is what the unified cache is
-  for; positions are per-`seq_id` (`{ slot.id }` in `common_batch_add`).
-- **`n_ubatch > n_batch`:** invalid; A.4 clamps `EffectiveUBatchSize <=
-  EffectiveBatchSize` and A.1 logs a warning if options violate it.
-- **Embeddings / rerank:** must keep `n_ubatch >= prompt length` (single pass,
-  `can_split()==false`). The existing `:519` alias + `EffectiveBatchSize`
-  context-sizing for single-pass usecases (`options.go:119-124`) must be preserved
-  — do not let the serving `BlackwellLogicalBatch` default leak into single-pass
-  configs (A.5 gates on completion + `n_parallel>1`).
-- **Turboquant fork:** the fork lacks some `common_params` fields (see
-  `LOCALAI_LEGACY_LLAMA_CPP_SPEC` precedent at `grpc-server.cpp:755`). `n_batch` /
-  `n_ubatch` are ancient fields and safe; if Phase B adds `max_prefill_per_iter`,
-  guard the new field behind a `#ifndef` like the checkpoint block does.
-
-## 5. Orthogonality to paged KV (Phase 2)
-
-Keep them independent. Paged KV (the `-kvp` / block-manager effort, draft #22569,
-and `paged/`) changes **where** KV blocks live (allocation/utilization). Chunked
-prefill / this decouple changes **how many tokens per iteration** the scheduler
-batches (the `n_batch` budget and decode/prefill interleave). They compose: paged
-KV raises the concurrency ceiling (more slots), the decouple widens the per-iter
-scheduling window to feed those slots; neither touches the other's data structures.
-The only contact point is `update_slots()` — if both ship a vendored patch to it,
-land them as separate, ordered patches in `patches/` and keep the hunks disjoint
-(paged touches allocation/seq_rm; chunked-prefill Phase B touches the prefill fill
-budget).
-
----
-
-## 6. Bottom line
-
-- Chunked prefill + decode interleave: **already present and correct** on the
-  pinned llama.cpp — verify (Phase 0.1), do not rebuild.
-- Real work: the **n_batch/n_ubatch decouple** (Phase A) — small, additive,
-  default-preserving — plus an **optional decode-headroom prefill cap** (Phase B)
-  if measurements show ITL jitter. Both are LocalAI-side: A in `grpc-server.cpp`
-  + proto + `options.go`; B as a vendored `patches/` hunk.

backend/cpp/llama-cpp/paged/DECODE_OVERHEAD.md

@@ -1,215 +0,0 @@
-# llama.cpp multi-user decode overhead on DGX Spark (GB10, sm_121)
-
-Investigation of the Qwen3-32B concurrent-decode throughput gap (llama.cpp ~547 t/s
-vs vLLM ~667 t/s) on the GB10 box, build `~/llama.cpp-pr24423/build` (Release,
-sm_121, `LLAMA_MAX_SEQ=256`, flash-attn on), model
-`~/bench/q3-32b-gguf/Qwen3-32B-Q4_K_M.gguf`.
-
-## TL;DR (the result overturns the brief's premise)
-
-On **this** build the prime suspect is wrong and the host-overhead premise does not
-hold:
-
-1. **CUDA graphs are NOT disabled at high concurrency.** At npl=128, 94 of 98
-   decode `graph_compute` calls **replay a captured CUDA graph** (0 resets, stable
-   key, no property churn post-warmup). The keyed-warmup gate works.
-2. **There is no ~170ms/step host hotspot here.** The GPU is **~96% active during
-   decode with graphs ON and ~96% active with graphs OFF**. Decode at npl=128 is
-   **GPU-compute-bound**, not host-bound.
-3. The brief's "20% GPU util / 66ms GPU / 170ms host per step" was measured on a
-   different/earlier build (mainline without these graph fixes). It is not
-   reproducible on `llama.cpp-pr24423`.
-4. Because the GPU is the bottleneck, re-enabling graphs cannot lift the number:
-   the clean A/B shows graphs ON vs OFF = **+1.5% at npl=128** (and +2.9% at
-   npl=32 - the benefit shrinks as concurrency rises and the GPU saturates).
-5. The real gap to vLLM is the **quantized decode GEMM kernel**: `mul_mat_q`
-   (Q4_K + Q6_K) is ~68% of decode GPU time and runs ~2.1x above the GB10
-   memory-bandwidth floor. Closing the gap requires Marlin/Machete-style int4
-   GEMM kernels, not host-side work. This is a kernel project (the direction the
-   prior session's uncommitted `marlin-w4a16.cu` / `fp4-grouped-moe.cu` already
-   started, though those target w4a16/GPTQ-int4, not the K-quants this GGUF uses).
-
-## 1. Why CUDA graphs are (not) disabled - exact code + measurement
-
-### The gate (code)
-
-PR24423 refactored the CUDA-graph path into a keyed, warmup-based scheme in
-`~/llama.cpp-pr24423/ggml/src/ggml-cuda/ggml-cuda.cu`:
-
-- `ggml_cuda_graph_get_key(cgraph)` (~L3343) keys the cached CUDA graph by
-  `cgraph->nodes[0]` (first-node pointer).
-- `ggml_cuda_graph_check_compability(cgraph)` (~L3301) disables graphs only for:
-  - **split buffers** (`ggml_backend_buft_is_cuda_split`), and
-  - **`GGML_OP_MUL_MAT_ID`** when `src0` is non-quantized **or**
-    `ne[2] > get_mmvq_mmid_max(...)` (MoE expert routing needs a stream sync).
-  Qwen3-32B is **dense** -> no `MUL_MAT_ID` -> this condition never fires.
-- `ggml_backend_cuda_graph_compute` (~L4514) warmup gate: a graph is used only
-  after **2 consecutive calls with no property change** (`warmup_complete`); any
-  property change resets warmup. `ggml_cuda_graph_update_required` (~L3347)
-  detects change by `memcmp` of the full `ggml_tensor` struct + per-src
-  data-ptr/ne/nb, with a fast path when `cgraph->uid` is unchanged.
-
-### Why it stays enabled across decode steps
-
-The graph stays stable because llama.cpp's host-side graph reuse holds during
-decode, so node pointers/props (and `cgraph->uid`) do not churn:
-
-- `llama_kv_cache::get_n_kv` (`src/llama-kv-cache.cpp` L1223-1233) **pads n_kv to
-  a multiple of 256** ("so that the graph remains constant across batches and can
-  be reused"). For ntg<=256 within the first KV block, n_kv is constant.
-- `can_reuse_kq_mask` (`src/llama-graph.cpp` L43) keeps the KQ-mask dims stable:
-  `ne=[n_kv, n_tokens/n_stream, 1, n_stream]` = `[256,1,1,128]` every decode step
-  at npl=128.
-- `can_reuse` (`src/llama-context.cpp` L1283) therefore returns true, so the
-  scheduler is **not** reset/re-split. `graph->uid` is only reassigned inside
-  `ggml_backend_sched_split_graph` (`ggml/src/ggml-backend.cpp` L1033, L1485),
-  which is skipped on the reuse path -> stable uid -> CUDA graph replays.
-
-### Measurement (instrumented build, npl=128, ntg=96)
-
-Env-gated counters added to `ggml_backend_cuda_graph_compute` /
-`ggml_cuda_graph_update_required` (since `GGML_LOG_DEBUG` is compiled out in
-Release / NDEBUG). End-of-run summary:
-
-```
-[GTRACE-SUMMARY] calls=98 notenab=0 warming=3 warmdone=1 RESET=0 USED=94 incompat=0 distinct_keys=1
-```
-
-94/98 decode `graph_compute` calls **replayed** a captured CUDA graph; **0**
-warmup resets; a **single** distinct graph key for the whole decode; no node
-property churn after warmup. Graphs are fully engaged at npl=128.
-
-(The instrumentation was reverted afterwards; the checkout is back to its
-pre-task state and the `.so` rebuilt clean.)
-
-## 2. The per-step CPU "hotspot" - there isn't one on this build
-
-GPU utilization during npl=128 decode (ntg=256):
-
-- **Graphs ON** - `nvidia-smi` sampled every 0.7s through the decode phase:
-  steady **96% GPU util**, SM clock **2184 MHz** (not throttled), 45-47 W.
-- **Graphs OFF** (`GGML_CUDA_DISABLE_GRAPHS=1`) - nsys CUDA trace, 8s window:
-  total GPU kernel time = `3,983,292,128 ns / 0.516` = **~7.72s of the 8s
-  window = ~96% GPU-active**. Even with every kernel launched individually from
-  the host, the GPU is still ~96% busy. There are essentially **no host gaps**.
-
-Per-step wall = 60.6s / 256 steps = **~237 ms/step**, and the sum of one decode
-graph's kernel times (nsys, graphs-on capture) is ~244 ms -> GPU kernel time per
-step ~= wall time per step. The host work between steps is in the low single-digit
-ms (the ~4% idle), consistent with graphs ON giving only +1.5% at npl=128.
-
-This directly contradicts the brief's 66ms-GPU / 170ms-host split, which must have
-come from a pre-graphs build.
-
-### Per-step GPU breakdown (nsys, npl=128 decode, graphs off, 8s window)
-
-| Kernel | % GPU time | ~ms/step |
-|--------|-----------:|---------:|
-| `mul_mat_q` Q4_K (type 12) | 51.6 | ~118 |
-| `flash_attn_ext_f16` | 19.3 | ~44 |
-| `mul_mat_q` Q6_K (type 14) | 16.2 | ~37 |
-| `unary_gated` silu | 4.1 | ~9 |
-| mmq stream-k fixup + quantize_q8_1 | ~5 | ~12 |
-| rms_norm / rope / set_rows / add | ~4 | ~10 |
-
-Quantized matmul = **~68%** of decode GPU time (~155 ms/step). Attention ~19%.
-
-`perf` could not profile the host (kernel `perf_event_paranoid=4`), but it is moot:
-the host is ~4% of the wall, so there is no ~170ms host hotspot to chase.
-
-## 3. Fix attempt + measured result
-
-### The requested fix (re-enable graphs / pad the decode batch) is a no-op here
-
-Graphs are already enabled and the batch is already stable (n_kv padded to 256,
-kq_mask dims constant). The clean cold A/B (cooldowns between every run):
-
-| npl | graphs ON (t/s) | graphs OFF (t/s) | delta |
-|----:|----------------:|-----------------:|------:|
-| 32  | 242.60 | 235.75 | +2.9% |
-| 64  | 398.59 | 389.06 | +2.5% |
-| 128 | 543.95 | 535.71 | +1.5% |
-
-Baseline (separate cold runs, original non-instrumented build):
-npl=32 243.9, npl=64 397.1, **npl=128 544.95** (matches the ~546 baseline).
-
-Graphs help, but the benefit **monotonically shrinks** as concurrency rises and
-the GPU saturates. At npl=128 there is only ~1.5% of host launch overhead left to
-remove, and GPU util is ~96% in both columns. **You cannot lift npl=128 decode
-toward 667 by working on graphs/host overhead - the GPU is the bottleneck.**
-
-### Where the number actually is, and the real lever
-
-- vLLM 667 t/s at this concurrency = **192 ms/step**; llama.cpp 547 = **237
-  ms/step**. The ~45 ms/step gap maps almost entirely onto the quantized matmul.
-- GB10 memory-bandwidth floor for a 32B Q4_K_M (~19.8 GB of weights, read once
-  per step and shared across the 128 sequences) at ~273 GB/s is **~72 ms/step**.
-  llama.cpp's `mul_mat_q` spends ~155 ms/step on matmul = **~2.1x the bandwidth
-  floor**. vLLM's Marlin/Machete int4 GEMMs run much closer to the floor; that
-  efficiency difference is the ~547 -> 667 gap.
-- The Q6_K matmul (`mul_mat_q` type 14) also shows pathological tail latency
-  (median 0.89 ms, max 5.5 ms) - the MMQ kernel is not well-tuned for the skinny
-  n=128 decode shape.
-
-**The lever to beat 547 is a faster quantized decode GEMM**, i.e. a Marlin-style
-int4 kernel for the decode shapes. This is exactly the direction of the prior
-session's uncommitted `ggml/src/ggml-cuda/marlin-w4a16.cu` and
-`fp4-grouped-moe.cu` (already wired via
-`if (!split && ggml_cuda_w4a16_mul_mat(...)) return;` in `ggml_cuda_mul_mat`).
-Note those target **w4a16 / GPTQ-int4**, while this GGUF is **K-quant (Q4_K/Q6_K)**,
-so they are inert for this model - a Marlin path for K-quants (or shipping the
-model in a Marlin-friendly int4 format) would be required. That is a multi-day
-kernel effort, out of scope for this session, but it is the only lever that can
-move the number.
-
-### Why the "bump LLAMA_MAX_SEQ to 1024 -> 377" data point is consistent
-
-`llama_batch_allocr` keeps `seq_cpl` as an `LLAMA_MAX_SEQ x LLAMA_MAX_SEQ` table
-(`src/llama-batch.cpp`), so per-batch seq bookkeeping scales ~O(MAX_SEQ^2). At
-MAX_SEQ=1024 that host cost becomes large enough (~70 ms/step) to dominate and
-drop decode to 377. At MAX_SEQ=256 the same term is ~4.4 ms/step (the ~1.5% that
-graphs reclaim); lowering to 128 would save ~3 ms/step (~1%). So MAX_SEQ tuning
-confirms the host term is real but tiny at 256 - not a path to 667.
-
-## How this would land in LocalAI
-
-- **No host/graph patch is warranted** for this build: graphs already engage and
-  the decode is GPU-bound. A "pad the decode batch / force graph capture" patch
-  would change nothing measurable at high concurrency.
-- The actionable upstream/vendored work is a **Marlin-style int4 decode GEMM**
-  (extend the prior `marlin-w4a16.cu` to cover K-quants, or quantize the served
-  model into a Marlin-friendly int4 layout). That is where the ~547 -> 667+ lives.
-- If a small host win is still wanted, keep `LLAMA_MAX_SEQ` no larger than the max
-  concurrency actually used (the per-batch `seq_cpl` table is O(MAX_SEQ^2)).
-
-## Reproduction
-
-```
-# baseline / A/B (cold, 30s cooldowns)
-llama-batched-bench -m Qwen3-32B-Q4_K_M.gguf -npp 16 -ntg 128 -npl 32,64,128 \
-  -ngl 99 -b 2048 -ub 2048 -fa on            # graphs on
-GGML_CUDA_DISABLE_GRAPHS=1 ...same...        # graphs off
-
-# GPU util (graphs on): sample nvidia-smi during decode -> ~96%, 2184 MHz
-# GPU active (graphs off): nsys profile -t cuda --delay=6 --duration=8 ...
-#   nsys stats --report cuda_gpu_kern_sum  -> sum/0.516 ~= 7.72s of 8s = ~96%
-```
-
-## UPDATE: NVFP4 closes most of the decode gap (no Marlin-for-K-quants needed)
-
-The diagnosis above said the lever is "a more bandwidth-efficient int4 decode GEMM"
-and feared a multi-day Marlin-for-K-quants kernel. But the FP4-MMA path is already
-that kernel. Measured (npl=128, cold A/B, npp=16 ntg=128):
-
-| quant | decode S_TG (t/s) | vs Q4_K | vs vLLM 667 |
-|---|---|---|---|
-| Q4_K_M | 547 (548/546) | - | 82% |
-| **NVFP4** | **619 (617/622)** | **+13%** | **93%** |
-
-NVFP4's `mul_mat_q<NVFP4>` runs closer to the GB10 bandwidth floor at the thin n=128
-decode shape than Q4_K's int8-MMQ (which ran ~2.1x above it). So shipping the model
-as NVFP4 closes the decode gap from ~22% to ~7% AND wins prefill (1209 vs Q4 767 /
-vLLM 800). Net on GB10: llama.cpp+NVFP4 is ahead on prefill (1.5x) and within ~7% on
-decode. The remaining ~7% would be incremental FP4-MMA decode-kernel tuning, NOT a
-from-scratch Marlin kernel - a much smaller, optional effort. NVFP4 is the answer to
-both the prefill and the decode gap.

backend/cpp/llama-cpp/paged/DGX_BLACKWELL_PLAN.md

@@ -1,253 +0,0 @@
-# Closing the vLLM Gap on Blackwell (GB10 / DGX Spark) — Living Plan & Results
-
-Target hardware: NVIDIA **GB10** (Grace-Blackwell, `sm_121a`, 119 GiB unified LPDDR5X), `dgx.casa`.
-Model under test: **Qwen3-Coder-30B-A3B-Instruct** (MoE, 128 experts, top-8, ~3B active).
-Engines: llama.cpp (CUDA, `~/llama.cpp-pr24423`, build `7a6ddc5`, `CMAKE_CUDA_ARCHITECTURES=121`) vs vLLM 0.23.0 (`~/vllm-bench`, torch 2.11.0+cu130).
-
-> This is a working document. Each phase appends measured numbers, what was learned, and what's next.
-> Methodology: `llama-bench` (single-stream pp/tg, built-in reps) and `llama-batched-bench` (`-npl` sweep,
-> decode-phase aggregate `S_TG`, prefill aggregate `S_PP`); vLLM via `~/bench/vllm_conc.py` (decode-phase
-> aggregate matched to `S_TG`). Same model/prompt/seed. Precision matched where possible.
-
----
-
-## Baseline results (established)
-
-### Single-stream (B=1), matched ~8-bit
-| Engine / precision | prefill pp512 (t/s) | decode tg128 (t/s) |
-|---|---|---|
-| llama.cpp **Q8_0** | 2215 ± 15 | **54.8 / 62.2** * |
-| llama.cpp **F16** | 700 ± 24 | 32.9 ± 0.05 |
-| vLLM **FP8** | 9155 ± 308 | 52.45 ± 0.05 |
-
-\* two sessions; ~55 right after worker-stop (clocks settling), ~62 steady state. Both ≥ vLLM → **single-stream parity holds**.
-
-### Concurrency sweep (decode-phase aggregate `S_TG`, prefill aggregate)
-| B | llama Q8 prefill | vLLM FP8 prefill | llama Q8 decode | vLLM FP8 decode |
-|---|---|---|---|---|
-| 1 | 1080 | 9644 | 60.1 | 48.0 |
-| 8 | 2189 | 33373 | 160.8 | 312.4 |
-| 32 | 2198 | 99398 | 357.1 | 1171 |
-| 64 | 2194 | 151990 | 519.2 | 2064 |
-
-llama F16 prefill also flat: B=1 452 → B=8 723 → B=32 778. **Prefill flat at both precisions = kernel-throughput ceiling.**
-
-### Our paged patch (LLAMA_KV_PAGED) — concurrency effect: NONE
-Same Q8 binary, paged branch confirmed firing (137 placements at B=8), throughput identical within noise:
-| | B=1 | B=8 | B=32 |
-|---|---|---|---|
-| stock decode | 61.2 | 171.7 | 377.0 |
-| paged decode | 62.7 | 170.8 | 376.8 |
-
-Patch is placement-only correctness prototype; doesn't implement concurrency mechanics. Single-stream-neutral, concurrency-neutral.
-
----
-
-## Root-cause diagnosis (nsys + code audit)
-
-- **74.5% of GPU compute = `mul_mat_q`** (Q8_0 int8 MMQ GEMM, the MoE experts). Only cutlass kernel seen is `cutlass_80_tensorop` = **Ampere (sm_80)**, not Blackwell.
-- ggml-cuda has **NO FP8 path** (no e4m3/e5m2 GEMM, no cuBLASLt FP8). Q8_0 runs the **Ampere-class int8 `mma.sync s8.s8.s32`** even on GB10 (`mma.cuh:924`, dispatched unconditionally `mmq.cu:307`).
-- ggml-cuda **DOES** have a **native Blackwell FP4 path** (MXFP4 + NVFP4, `mma...kind::mxf4...e2m1`, `mma.cuh:1126`, gated `BLACKWELL_MMA_AVAILABLE`). Merged via #17906/#20644/#21074.
-- **No fused MoE grouped GEMM**, no tcgen05/wgmma (warp-level `mma.sync` only).
-- **Small per-expert GEMMs**: 512-tok ubatch → ~32 tok/expert (128 exp, top-8) → thin GEMMs, memory-bound, can't fill tensor-core tiles. vLLM processes 8192 tok/step → ~512 tok/expert → compute-bound + FP8.
-- **The 45–69× gap is partly apples-to-oranges**: we compared llama Q8 (Ampere int8) vs vLLM FP8 (Blackwell). Upstream/NVIDIA benches put the *real* FP4-vs-FP8 prefill gap at **~25–50% long-context**, not 45–69×.
-
-Key upstream refs: discussion #22042 (FP8 design: `ggml_mul_mat_ext` + scale tensors), #17906 (native MXFP4), #18250 (NVFP4-MoE closed not-planned).
-
----
-
-## The levers (cheap → expensive) — execution log
-
-### Lever 1 — NVFP4/MXFP4 model (use existing Blackwell FP4 path) + ubatch bump
-Status: **IN PROGRESS** — single-stream done, concurrency next.
-Quant: `llama-quantize F16 -> MXFP4_MOE` (type 38), 15.9 GiB / 4.47 BPW. (No NVFP4 in llama-quantize; MXFP4_MOE puts experts in MXFP4 = Blackwell FP4 MMA.)
-
-Single-stream (llama-bench), MXFP4 vs Q8 vs vLLM-FP8:
-| metric | llama Q8 | **llama MXFP4** | vLLM FP8 |
-|---|---|---|---|
-| prefill pp512 (ub512) | 2215 | **3061 ± 22** | 9155 |
-| prefill pp2048 (ub512) | ~2200 | 3137 ± 7 | — |
-| prefill pp2048 (**ub2048**) | — | **3441 ± 14** | — |
-| decode tg128 | 62.2 | **86.4 ± 0.3** | 52.45 |
-
-Findings:
-- **MXFP4 decode 86.4 beats vLLM FP8 52.45 by 1.65×** (4-bit = less memory traffic; decode is memory-bound). llama wins decode outright.
-- MXFP4 prefill +38% over Q8; **ub2048 lifts prefill +10%** (3137→3441). Single-stream prefill gap to vLLM: 4.1× (Q8) → **2.7× (MXFP4)**.
-- Caveat: MXFP4 is 4-bit vs vLLM FP8 8-bit — not precision-matched. Fair match = vLLM NVFP4 (4-bit); pending.
-Concurrency (decode-phase aggregate `S_TG`, ub2048), MXFP4 vs Q8 vs vLLM-FP8:
-| B | Q8 dec | **MXFP4 dec** | vLLM dec | Q8 pp | **MXFP4 pp** | vLLM pp |
-|---|---|---|---|---|---|---|
-| 1 | 60.1 | **83.4** | 48.0 | 1080 | 1625 | 9644 |
-| 8 | 160.8 | **267.4** | 312.4 | 2189 | 3634 | 33373 |
-| 32 | 357.1 | **551.2** | 1171 | 2198 | 3651 | 99398 |
-| 64 | 519.2 | **770.2** | 2064 | 2194 | 3648 | 151990 |
-
-**Lever-1 verdict:** MXFP4 is a large, free win — decode +50–66% over Q8, prefill plateau +66% (2200→3650). MXFP4 decode **wins at B=1, near-parity at B=8** vs vLLM; only falls behind at high concurrency. **Prefill still plateaus (~3650)** — the MoE prefill GEMM doesn't scale with batch (no fused grouped GEMM; ubatch-limited). That plateau is the real remaining structural gap → Levers 2–3. Quality caveat unchanged (MXFP4 4-bit vs vLLM FP8 8-bit; quality not yet evaluated).
-
-### Lever 2 — `n_ubatch` / `n_batch` tuning (standalone)
-Status: **DONE + SHIPPED (auto-default implemented)**
-MXFP4 pp4096 vs ubatch: ub512=2994, **ub2048=3316**, ub4096=2820(noisy), ub8192=3180.
-**Verdict:** prefill saturates at ub=2048; larger ubatch gives nothing. The ~3300–3650 ceiling is the **MoE GEMM kernel**, not batch size. → No more free config wins; the rest is kernel work (Levers 3–5).
-**Implemented:** `core/backend/hardware_defaults.go` — `EffectiveBatchSize` now defaults the physical batch
-(n_batch→n_ubatch alias) to **2048 on Blackwell** (`xsysinfo.IsNVIDIABlackwell`, cc≥12 / sm_120/121) when the
-config leaves `batch:` unset; explicit `batch:` always wins. Detection is a shared Go helper; placed at the
-common ModelOptions builder so it covers the C++ llama.cpp backend too. Tests: `hardware_defaults_internal_test.go`.
-
-### Lever 1b — Standard Q4 vs MXFP4 (what's actually MXFP4-specific)
-**Q4_K_M** (17.3 GiB) vs **MXFP4** (15.9 GiB), ub2048:
-| metric | Q4_K_M | MXFP4 | Q8 |
-|---|---|---|---|
-| decode tg128 | **93.5** | 86.4 | 62.2 |
-| prefill pp512 | 2164 | **3061** | 2215 |
-| prefill pp2048 | 2953 | **3441** | ~2200 |
-**Verdict:** the **decode win is just "4-bit"** — plain Q4_K_M matches/beats MXFP4 on decode (both memory-bound).
-MXFP4's *only* real edge is **prefill (+41% over Q4_K_M)** via Blackwell FP4 tensor cores. So for shipping,
-**"4-bit quant + ubatch=2048" captures most of the win portably**; MXFP4 is a Blackwell-only prefill extra.
-
-### Lever 3 — Fused FP4/FP8 MoE grouped GEMM (+ activation-quant fusion)
-Status: **DESIGNED + PROFILED, not built** (multi-week kernel R&D). The single biggest remaining prefill win.
-
-**Decisive measurements:**
-- Prefill does NOT scale with bigger single prompts (attention O(N²) confounds): MXFP4 pp2048=3295, pp8192=1524,
-  pp16384=2051. So the plateau is not a batch-size fix.
-- Real gap is batched many-sequence prefill: B=32 llama 3651 vs vLLM 99398 = **27×**. llama.cpp MoE prefill runs
-  at only **~22 effective TFLOP/s** on the GB10 — far below the GPU. Large headroom.
-- **nsys (MXFP4 pp2048):** `mul_mat_q<type39>` (MoE FP4 GEMM) = **37.2%**, `quantize_mmq_mxfp4` (act-quant) = 8.0%,
-  `mul_mat_q<type8>` (dense/attn, still Q8) = 10.1%, flash_attn = 8.8%. The native FP4 MMA *is* engaged — the
-  inefficiency is the **per-expert thin-tile MMQ scheduler** + **un-fused activation quant**.
-
-**Target (precise):** the ~45% in `mmq.cu`'s grouped MoE path (`ggml_cuda_mul_mat_q` + `ids`, `mmid.cu`). Replace
-the per-expert thin-tile scheduler with a CUTLASS-style grouped GEMM (full tiles regardless of tokens/expert) and
-fuse `quantize_mmq_mxfp4` into the permute/gather. Dense Q8 matmuls (10%) are the separate Lever-4 (FP8) target.
-Problem (measured): the prefill ceiling is the MoE expert GEMM. Today `ggml_cuda_mul_mat_q` with `ids`
-(`mmq.cu:127`) launches one grouped MMQ over a 3D grid (z = expert), but each expert's tile is thin
-(~tokens/expert columns) so int8/FP4 tensor cores run underfilled; throughput is memory-bound on weight
-streaming and flat vs batch.
-Approach:
-- Replace the per-expert thin-tile scheduler with a **CUTLASS-style grouped GEMM** that concatenates all
-  experts' token-blocks into one problem with per-group offsets, so tiles are always full (m16n8k64 FP4 /
-  m16n8k32 FP8) regardless of per-expert token count. Mirrors vLLM's `fused_moe` + cutlass grouped GEMM.
-- **Fuse activation quantization into the permute/gather** (the `quantize_mmq_q8_1`/FP4 quantize currently a
-  separate 3.3% kernel) so the routed activations are quantized as they're scattered into expert order.
-- Files: new kernel under `ggml/src/ggml-cuda/` (e.g. `moe-grouped-gemm.cu`) + dispatch hook in
-  `ggml_cuda_mul_mat_id` (`ggml-cuda.cu:2622`); reuse `mmid.cu` routing/`expert_bounds`.
-- Effort: high (2–4 wks expert CUDA). Risk: numerics + sm_121 tile tuning. Expected payoff: the bulk of the
-  prefill gap (vLLM's MoE prefill advantage is mostly this). Upstream: #18250 (NVFP4-MoE) was closed
-  not-planned, so this would be a LocalAI patch or a fresh upstream proposal.
-
-### Lever 4 — FP8 (e4m3) GEMM for dense layers
-Status: **DESIGNED, not built** (blocked on a core ggml API change).
-Problem: ggml-cuda has no FP8 matmul (only int8/FP4). vLLM runs qkv/o_proj/lm_head in FP8 on Blackwell
-tensor cores. Our dense layers run int8-MMQ or f16-cuBLAS.
-Approach (two options):
-- (a) **cuBLASLt FP8**: route dense `mul_mat` through `cublasLtMatmul` with `CUDA_R_8F_E4M3` A/B and FP32
-  compute + scale pointers. Lowest kernel effort; gets library-tuned Blackwell FP8 immediately. Needs the
-  scale-tensor plumbing below.
-- (b) **Hand-written sm_121 `mma.sync ...e4m3.e4m3.f32`** kernels in `mma.cuh`/`mmf.cu`. More control, more work.
-- Prerequisite (both): the **`ggml_mul_mat_ext` / scale-tensor API** from upstream discussion #22042 —
-  per-tensor FP8 scales don't fit the block-scaled quant struct; `MUL_MAT`/`MUL_MAT_ID` must accept optional
-  scale tensors. This is a cross-cutting ggml change (graph + ops + all backends' fallbacks).
-- Effort: high (API change is the hard part; cuBLASLt path is then moderate). Payoff: closes dense-layer
-  prefill/compute gap; complements Lever 3. Note: for *this* MoE model the experts dominate, so Lever 3 > 4.
-
-### Lever 5 — tcgen05 / wgmma-class kernels for large-prefill tiles
-Status: **DESIGNED, not built** (very high effort; last increment).
-Problem: ggml's tensor-core path is warp-level `mma.sync` only (no `wgmma`/`tcgen05`). Blackwell's
-tensor-memory `tcgen05` MMA (what CUTLASS uses) extracts substantially more throughput at large prefill tiles.
-Approach: introduce warpgroup/tcgen05 GEMM main-loops for the FP4/FP8 paths (effectively adopting CUTLASS
-3.x collective mainloops for sm_120/121), used when tile size is large enough (prefill). Decode (thin) keeps
-`mma.sync`.
-- Effort: very high (CUTLASS-class engineering). Payoff: the final slice of large-prefill throughput; only
-  worth it after Levers 3–4 land. Realistically: depend on/upstream CUTLASS kernels rather than hand-roll.
-
----
-
-## Paged attention — complete implementation (after kernels are fair)
-The placement prototype is insufficient (measured: zero concurrency benefit). A real implementation needs all
-four gaps. CPU foundation already built & verified (`PagedKVManager` P0–P3, `README.md`); the in-model parts
-are unbuilt. **Build order and concrete design:**
-
-1. **On-demand block allocation from a shared pool** (capacity win — more concurrent seqs before OOM).
-   - Replace `find_slot`'s ring-buffer (`llama-kv-cache.cpp:818`) with `PagedKVManager` block allocation; the
-     KV tensor becomes a shared block pool `[n_embd, block_size*num_blocks]`, sequences draw blocks on demand
-     (already prototyped on CPU: `paged_kv_manager.{h,cpp}`, `test_ggml_paged_rw.cpp`).
-   - Win measured where it counts: max concurrent sequences before OOM (not yet benchmarked — needs this).
-2. **Gather-read** so each seq attends only its own blocks (`get_k`/`get_v` `:1145/1165` → `ggml_get_rows`
-   gather into scratch, then existing attention). Numerically proven on CPU (`test_ggml_paged_attn.cpp`,
-   7.5e-08 vs reference). Needs `build_attn_paged` branch in `llama-graph.cpp` + Gate 0 in a real model.
-3. **Continuous batching / scheduler** (no head-of-line blocking on mixed-length traffic). New scheduler in
-   the server slot path; admit/evict at block granularity; the dimension where paging beats llama.cpp's
-   current static batching. This is where the *real* concurrency win lives (vs our synthetic uniform test).
-4. **Automatic prefix sharing** (block-hash dedup; `PagedKVManager::{compute_block_hashes,get_computed_blocks}`
-   already implemented & tested). Cross-tenant shared system prompts reuse physical blocks.
-
-Status: design in `2026-06-19-paged-attention-llamacpp-design.md`; CPU P0–P3 done; in-model #1–#4 unbuilt.
-**Then** measure concurrency in paging's real scenarios — **memory-pressured (max seqs before OOM)** and
-**mixed-length continuous batching** — on the MXFP4 (fair-quant) footing, not the uniform/over-provisioned
-test that (correctly) showed no benefit.
-
-> Reality check from this session's data: paged attention is a **capacity + scheduling** win, not a per-token
-> speed win. On GB10 with 119 GB unified memory and uniform requests we are not memory-bound at B≤64, so the
-> placement prototype showed nothing. Paging's value appears under memory pressure (many/long sequences) and
-> bursty mixed-length traffic. The per-token throughput gap is a **kernel** problem (Levers 1–3), separate
-> from paging.
-
----
-
-## Implementation plan A — Lever 3: FP4 MoE GEMM to vLLM parity
-
-Goal: lift batched MoE prefill from ~3.65k t/s (B=32) toward vLLM's ~99k. Root cause (profiled):
-`mul_mat_q<MXFP4>` runs at ~22 effective TFLOP/s — warp-level `mma.sync`, not Blackwell tcgen05.
-Cheap knobs are exhausted (ubatch saturates at 2048; `GGML_CUDA_FORCE_CUBLAS` is a no-op 3419↔3423;
-tile width already full at mmq_x=128). So parity needs kernel work, done iteratively on the DGX
-(`~/llama.cpp-pr24423`, editable + rebuildable; diffs captured as `patches/`).
-
-Phases (each: hypothesis → edit `ggml/src/ggml-cuda/` → `cmake --build build --target llama-bench` →
-`llama-bench` MXFP4 pp/concurrency → record):
-1. **Cheap kernel tweaks (low confidence, fast).** nwarps (occupancy), `mmq_y` tile, stream-k on/off,
-   FP4 load-tile path. Measure each. Likely small (<1.3x) — these don't change the warp-MMA ceiling.
-   - **Result (nwarps):** DEAD END. `nwarps` is locked by `static_assert(nwarps*tile_C::I == mmq_y)`
-     (mmq.cuh:3234) → nwarps=8 for mmq_y=128. Can't raise occupancy without co-scaling mmq_y to 256
-     (nwarps=16), which blows Blackwell shared-memory limits. The MMQ constants are tightly coupled;
-     it is not freely tunable. Confirms parity needs the kernel rewrite (phase 3), not knobs.
-2. **Fuse activation quant** (`quantize_mmq_mxfp4`, 8%) into the permute/gather. Removes a kernel +
-   a global round-trip. Tractable, ~1.1x.
-   - **Result:** NOT AVAILABLE as a cheap patch. `quantize_mmq_fp4_cuda` (mmq.cu:200) *already* takes
-     `ids_src1` — the gather is already fused into the quant. The only remaining fusion is quantize-on-load
-     *inside* the GEMM hot loop (intricate, ~8% ceiling, risky). ORippler's #24481 fuses the decode (MMVQ)
-     post-scale and intends a "BS>1" (prefill) follow-up — unwritten. Marginal; skip.
-
-**Upstream survey (2026-06):** there is NO tcgen05/CUTLASS grouped-GEMM MoE kernel in ggml — not merged,
-not in-flight, not a draft (Discussion #18369 is talk, no PR; #18250 closed not-planned). CUTLASS is not a
-dependency (the profile's `cutlass_80_tensorop` is cuBLAS-internal). No fork has a portable MoE kernel
-(croll83/llama.cpp-dgx is GatedDeltaNet-focused). Maintainer signal (woachk on #17906): "the path forward
-is to wait for cuTile C++." So **nothing to cherry-pick; phase 3 is genuinely from-scratch.**
-3. **The real lever — tcgen05 / CUTLASS FP4 grouped GEMM.** Replace the per-expert MMQ scheduler with a
-   CUTLASS 3.x collective-mainloop grouped GEMM (sm_120a, `e2m1` block-scaled, tcgen05 tensor-memory MMA),
-   one problem over all experts with per-group offsets, fused act-quant. This is what vLLM/FlashInfer use.
-   Multi-week; the honest path to parity. Prefer **upstream ggml** (issue drafted) over a private patch.
-4. **Full-model low precision.** Quantize dense layers (qkv/o_proj/lm_head, the 10% Q8) to FP4/FP8 too so
-   the whole prefill runs on FP4 tensor cores, not int8-MMQ.
-Exit per phase: measured t/s recorded here; stop a phase when it's a dead end (recorded as such).
-Matching vLLM realistically requires phase 3; phases 1–2 are the warm-up + de-risking.
-
-## Implementation plan B — Complete paged attention (the pivot)
-
-CPU foundation done (P0–P3, `README.md`): vLLM-parity block manager + ggml write/gather + attention
-numerics + placement Gate 0 (token-identical in-model). Remaining = make it deliver the multi-tenant wins.
-Phases:
-1. **On-demand shared-block pool** — replace `find_slot` ring buffer (`llama-kv-cache.cpp:818`) with
-   `PagedKVManager` block allocation; KV tensor = `[n_embd, block_size*num_blocks]` shared pool. Win:
-   fit more concurrent seqs before OOM. Test: max concurrent seqs at fixed budget vs contiguous.
-2. **Gather-read** (`get_k/get_v` `:1145/1165` → `ggml_get_rows` into scratch) + `build_attn_paged` branch
-   in `llama-graph.cpp`. Numerically proven on CPU (7.5e-08). Gate 0: token-identical multi-seq.
-3. **Continuous batching / scheduler** — admit/evict at block granularity in the server slot path. The
-   real concurrency win on mixed-length traffic (where the placement prototype showed nothing).
-4. **Automatic prefix sharing** — block-hash dedup (`PagedKVManager::{compute_block_hashes,get_computed_blocks}`
-   already implemented + tested). Cross-tenant shared system prompts reuse physical blocks.
-Then benchmark in paging's real regimes — **memory-pressured** + **mixed-length continuous batching** — on
-the MXFP4 (fair-quant) footing. Note: GB10's 119 GB unified memory means win-1 needs genuine pressure
-(long/many seqs) to show; the win is capacity + scheduling, not per-token speed.
-
-## Honest scope note
-Levers 3–5 and the complete paged implementation are each substantial (weeks of expert CUDA/systems work). This doc tracks what is **measured** vs **designed** vs **not-yet-built**, and never claims a number that wasn't run on the box.

backend/cpp/llama-cpp/paged/FP4_GROUPED_MOE_KERNEL.md

@@ -1,59 +0,0 @@
-# FP4 grouped-GEMM MoE kernel (Lever 3) — scaffold + implementation plan
-
-The one piece of work that actually closes the vLLM gap on Blackwell (GB10/sm_121). Both phases are
-bottlenecked by the same kernel: `mul_mat_q<MXFP4>` (warp-level `mma.sync` grouped MMQ, ~22 TFLOP/s) is
-**37%** of prefill and **54.6%** of decode-at-B=64 GPU time (`BENCHMARKS.md`). Paged attention can't touch
-it (proven). The fix is a CUTLASS-3.x collective-mainloop grouped GEMM with block-scaled `e2m1` operands via
-tcgen05 tensor-memory MMA — what vLLM/FlashInfer/TRT-LLM use.
-
-## Scaffold (DONE — builds clean, default byte-identical)
-
-Lives in the DGX checkout `~/llama.cpp-pr24423/ggml/src/ggml-cuda/` (to be rebased onto the pin as a patch /
-upstreamed). Captured diff: `patches/kernel/0001-fp4-grouped-moe-scaffold.patch`.
-
-- `fp4-grouped-moe.{cuh,cu}` — entry `ggml_cuda_fp4_grouped_moe(ctx, src0, src1, ids, dst) -> bool`
-  (true = handled, false = fall back to MMQ). Gated behind env `GGML_CUDA_FP4_GROUPED`. Currently always
-  returns false → **default build unchanged**.
-- Hook in `ggml_cuda_mul_mat_id` (the MoE dispatch), before the `ggml_cuda_mul_mat_q(...ids...)` call:
-  `if (ggml_cuda_fp4_grouped_moe(...)) return;`. Builds via the `file(GLOB "*.cu")` (re-run cmake configure
-  after adding the file — GLOB is configure-time).
-
-This is the integration seam. The kernel fills the stub.
-
-## Implementation phases (each: build on GB10 → numerical parity vs `mul_mat_q<MXFP4>` → bench)
-
-1. **Reference grouped GEMM (correctness first, slow OK).** Per-expert problem sizes + offsets from `ids`;
-   dequant `e2m1`+scales → BF16; loop CUTLASS (or cuBLAS) per group. Gate: output matches MMQ within fp tol
-   on a 2-expert toy + the real model (token-identical greedy). Establishes the harness + the data plumbing.
-2. **CUTLASS GemmGrouped, sm_120a, BF16 operands.** Replace the loop with one `cutlass::gemm::device::
-   GemmGrouped` launch over all experts (per-group offsets). Measures the grouping win alone.
-3. **Block-scaled FP4 operands (the real lever).** `e2m1` A/B with `e8m0`(MX)/`e4m3`(NV) block scales via the
-   Blackwell scaled-MMA collective (tcgen05 tensor-memory). This is where the TFLOP/s jumps. Needs CUTLASS
-   3.x + sm_120a; verify the block-scale layout matches ggml's MXFP4/NVFP4 packing.
-4. **Fuse activation quant** (the F32→FP4 of src1) into the gather/permute prologue.
-5. **Enable by default** on sm_120/121 when parity holds + faster; keep the env as an escape hatch.
-
-## Dependencies / decisions
-
-- **CUTLASS is not currently a ggml dependency** (the profile's `cutlass_80_tensorop` is cuBLAS-internal).
-  Adding it = submodule/fetch + include dir, gated to CUDA sm_120+. Float the approach with ggml maintainers
-  early (Discussion #18369 is the home; JohannesGaessler asked to discuss arch before big kernel work).
-- Target sm_120a/121a (consumer Blackwell). Datacenter Blackwell (sm_100) is a separate tile config.
-- Risk: needs ncu-driven iteration on the GB10; this is multi-week, expert-CUDA. No upstream base to fork
-  (exhaustive search confirmed). Net-new value upstream.
-
-## DENSE scope — RESOLVED (TODO #28, benchmarked): dense needs an FP4 GEMM too
-
-Benchmarked Qwen3-32B dense, vLLM W4A16 vs llama.cpp Q4_K_M (`BENCHMARKS.md`). **Dense prefill is 7.6–32×
-behind** (llama int8-MMQ plateaus ~765 t/s; vLLM FP4 scales to 24.4k); decode ~parity at B=1, 2.2× at B=64.
-So the kernel track is **two kernels, not one**:
-
-- **(a) Dense FP4 GEMM** — a plain non-grouped CUTLASS/tcgen05 block-scaled FP4 GEMM. **Simpler than grouped;
-  land this FIRST** — it's the easier first kernel, benefits every dense model, and de-risks the FP4 collective
-  before the grouped variant. Hook: the non-MoE `ggml_cuda_mul_mat_q` (no `ids`) path.
-- **(b) MoE grouped FP4 GEMM** — the scaffold above (`ggml_cuda_fp4_grouped_moe`), per-expert offsets.
-
-Both share the same block-scaled `e2m1` collective; (a) is (b) with one group. Suggested order: build (a),
-prove the FP4 collective + parity harness, then generalize to (b). (Aside: full W4A4 NVFP4 doesn't run on
-GB10 today — FlashInfer ships no FP4 cubins for sm_121, so the dense `mm_fp4` kernel hangs/returns zeros; the
-W4A16 Marlin path is the fast, correct one and is the fair comparison. See `BENCHMARKS.md` for the root cause.)

backend/cpp/llama-cpp/paged/MXFP4_QUALITY.md

@@ -1,140 +0,0 @@
-# MXFP4-dense vs Q4_K_M quality check (Qwen3, GB10 / DGX Spark)
-
-## Question
-
-MXFP4-quantized **dense** Qwen3-32B is measurably faster on GB10 (Blackwell) than
-Q4_K_M: ~1.58x concurrent prefill, ~1.2x decode, for free (just a requantize that
-routes onto the FP4-MMA kernel). Before LocalAI recommends MXFP4-dense as a Blackwell
-default, we must confirm its **quality is acceptable versus Q4_K** (Q4_K is normally the
-stronger 4-bit format).
-
-Critical caveat going in: the pre-existing `~/bench/q3-32b-mxfp4-dense.gguf` was built
-with `--allow-requantize`, so it was suspected to be **double-quantized** (Q4_K_M ->
-MXFP4), which would unfairly penalize MXFP4. The goal here was a *fair* answer.
-
-## Verdict
-
-**Do NOT recommend MXFP4-dense as a quality-equivalent replacement for Q4_K on
-Blackwell.** A clean apples-to-apples test (same BF16 source, both 4-bit, no imatrix)
-shows MXFP4-dense carries a **large** quality penalty that Q4_K does not:
-
-- Q4_K_M costs **+2.6%** perplexity vs the BF16 baseline.
-- MXFP4-dense costs **+30.8%** perplexity vs the BF16 baseline (i.e. **+27.5% worse
-  than Q4_K**).
-
-The double-quant suspicion was correct but it was **not** the main culprit: even a clean
-MXFP4-from-BF16 is dramatically worse than Q4_K. The ~1.58x prefill / ~1.2x decode
-speedup is real, but it is not free on quality. MXFP4-dense output is still coherent (not
-gibberish), so it is usable where raw throughput dominates and a quality hit is
-acceptable, but it must not be presented as a drop-in, quality-neutral Q4_K replacement.
-
-## Evidence
-
-### 1. Provenance of the existing 32B MXFP4 (it is double-quant)
-
-`~/dense_mxfp4.sh` (mtime matches the `q3-32b-mxfp4-dense.gguf` mtime, Jun 20 09:47)
-created it:
-
-```
-SRC=$HOME/bench/q3-32b-gguf/Qwen3-32B-Q4_K_M.gguf      # <-- source is Q4_K_M, not F16/BF16
-OUT=$HOME/bench/q3-32b-mxfp4-dense.gguf
-$QB --allow-requantize --tensor-type "attn=mxfp4" --tensor-type "ffn=mxfp4" \
-    "$SRC" "$OUT" MXFP4_MOE
-```
-
-Confirmed **double-quantized** (Q4_K_M -> MXFP4). Any PPL measured on this file
-overstates MXFP4's true penalty, so the 32B number below is a loose upper bound, not the
-fair answer.
-
-### 2. 32B quick read (wikitext-2-raw test, 50 chunks, ctx 512, ngl 99)
-
-`llama-perplexity`, PR build `~/llama.cpp-pr24423/build` (sm_121):
-
-| 32B model | PPL | vs Q4_K |
-|---|---|---|
-| Qwen3-32B-Q4_K_M | **7.3865** +/- 0.177 | - |
-| q3-32b-mxfp4-dense (double-quant) | **8.4638** +/- 0.206 | +14.6% |
-
-MXFP4 is much worse than Q4_K here, **and** it is double-quant, so the quick read is
-unfair -> escalated to a clean small-model comparison.
-
-### 3. Fair comparison: clean small dense model (Qwen3-4B BF16)
-
-The MXFP4-vs-Q4_K delta is a *format* property and roughly model-size-independent, so a
-small model gives a fast, clean answer. Downloaded `Qwen3-4B-BF16.gguf` (unsloth, ~7.7
-GiB) and quantized it **from that same BF16 source** to both formats with the identical
-recipe used for the 32B (no `--allow-requantize` needed, no imatrix on either side):
-
-```
-llama-quantize  q3-4b-bf16.gguf  q3-4b-q4km.gguf   Q4_K_M
-llama-quantize --tensor-type attn=mxfp4 --tensor-type ffn=mxfp4 \
-               q3-4b-bf16.gguf  q3-4b-mxfp4.gguf  MXFP4_MOE
-```
-
-Perplexity (wikitext-2-raw test, 50 chunks, ctx 512, ngl 99):
-
-| Qwen3-4B | size | PPL | vs BF16 | vs Q4_K |
-|---|---|---|---|---|
-| BF16 (baseline) | 7672 MiB | **13.3188** +/- 0.416 | - | - |
-| Q4_K_M | 2497 MiB | **13.6605** +/- 0.426 | **+2.57%** | - |
-| MXFP4 (clean) | 2236 MiB (4.66 BPW) | **17.4183** +/- 0.561 | **+30.78%** | **+27.5%** |
-
-This is the apples-to-apples quality answer: **clean MXFP4-from-BF16 is ~12x more lossy
-than Q4_K relative to the BF16 baseline** (30.8% vs 2.6%). Notably the clean-4B MXFP4-vs-
-Q4_K gap (+27.5%) is *wider* than the 32B double-quant gap (+14.6%), consistent with
-smaller models being more quantization-sensitive - the double-quant did not invent the
-problem, it is intrinsic to the format as quantized by `llama-quantize`.
-
-### 4. Coherence spot-check (32B, llama-simple, n=60)
-
-MXFP4-dense 32B is fully coherent, not degraded gibberish:
-
-- "The capital of France is" -> MXFP4: "...Paris, is located near the Seine River..."
-  (correct); Q4_K similar.
-- "Q: What is 17 multiplied by 23? A:" -> MXFP4 reasons via the distributive property
-  (sound); Q4_K answers 391 directly (correct).
-- "def fibonacci(n):" -> both emit valid Python.
-
-So the quality cost shows up as measurably higher perplexity (and would surface on harder
-/ longer tasks), not as obviously broken text at short generation lengths.
-
-## Why
-
-`MXFP4_MOE` is a 4-bit float format (E2M1 values, shared E8M0 scale per block of 32,
-round-to-nearest) designed for MoE expert tensors (gpt-oss et al.) with a coarse
-per-block scale. Q4_K uses 6-bit superblock scales plus per-sub-block mins - materially
-better for dense attention/FFN weights. Forcing MXFP4 onto dense layers to reach the FP4
-kernel trades ~1.58x prefill for a large accuracy loss. The FP4-MMA speed path is real,
-but the weights it accepts (MXFP4 here) are lossy for dense.
-
-## Caveat, stated precisely
-
-This measures **llama.cpp's `llama-quantize` MXFP4** (OCP MX FP4, RTN, **no imatrix**)
-against **llama.cpp's Q4_K_M** (k-quant superblocks, also no imatrix here). It is a fair
-format-vs-format comparison of exactly what LocalAI would ship if it routed a requantize
-through this path. It does **not** claim FP4 is fundamentally unviable on Blackwell:
-
-- An imatrix-aware MXFP4, or a better FP4 format with two-level scaling
-  (**NVFP4** - there are already `q3-32b-nvfp4` / `q3-32b-nvfp4a16` dirs on the box),
-  may close much of this gap and is the more promising Blackwell FP4 path to evaluate.
-- The result is for Qwen3 dense; other families may differ in magnitude but the
-  format-level disadvantage of plain MXFP4 RTN vs Q4_K is expected to hold.
-
-## Recommendation
-
-- **Do not** ship a blanket "use MXFP4-dense on Blackwell" recommendation as a Q4_K
-  quality equivalent. The ~1.58x prefill / ~1.2x decode win comes with a real ~30% PPL
-  inflation (vs ~2.6% for Q4_K). Q4_K_M stays the right dense default on Blackwell.
-- If exposing MXFP4-dense at all, gate it as an explicit **throughput-over-quality**
-  option with the perplexity caveat surfaced, not a default.
-- MXFP4/FP4 remains correct where the model is trained for it (MoE / gpt-oss-style).
-  Pursue **NVFP4** (and/or imatrix-aware FP4) as the quality-competitive Blackwell FP4
-  format before making any FP4-dense recommendation.
-
-## Reproduction (DGX Spark, GB10, build `~/llama.cpp-pr24423/build`, sm_121)
-
-- Dataset: `~/wikitext-2-raw/wiki.test.raw` (wikitext-2-raw-v1 test).
-- 32B: `~/ppl32b.sh` -> `~/ppl32b.out`; coherence `~/coh32b.sh` -> `~/coh32b.out`.
-- Clean 4B: `~/fair4b.sh` -> `~/fair4b.out` (quantize + 3x perplexity).
-- All runs `-ngl 99`, `--chunks 50`, `-c 512`. GB10 thermal-throttles but PPL is a
-  correctness metric, so thermal state does not affect these numbers.

backend/cpp/llama-cpp/paged/Makefile

@@ -1,41 +0,0 @@
-CXX ?= g++
-CXXFLAGS ?= -std=c++17 -O2 -Wall -Wextra -I.
-
-TESTS = test_free_block_queue test_block_pool test_paged_kv_manager test_prefix_cache
-BINS  = $(addprefix tests/,$(TESTS))
-
-all: $(BINS)
-
-tests/%: tests/%.cpp paged_kv_manager.cpp paged_kv_manager.h
-	$(CXX) $(CXXFLAGS) -o $@ $< paged_kv_manager.cpp
-
-check: all
-	@for t in $(BINS); do echo "== $$t =="; ./$$t || exit 1; done
-
-paged-bench: paged-bench.cpp paged_kv_manager.cpp paged_kv_manager.h
-	$(CXX) $(CXXFLAGS) -o $@ paged-bench.cpp paged_kv_manager.cpp
-
-bench: paged-bench
-	./paged-bench
-
-# --- Optional ggml integration test (Phase 1: paged write/gather mechanism) ---
-# Requires a built ggml. Override these to point at your checkout / build:
-#   make ggml-check GGML_SRC=<llama.cpp>/ggml GGML_BUILD=<ggml-build>
-GGML_SRC   ?= ../../llama-cpp-fallback-build/llama.cpp/ggml
-GGML_BUILD ?= /tmp/ggml-build
-GGML_LIBDIR = $(GGML_BUILD)/src
-
-GGML_TESTS = test_ggml_paged_rw test_ggml_paged_attn
-GGML_BINS  = $(addprefix tests/,$(GGML_TESTS))
-
-tests/test_ggml_%: tests/test_ggml_%.cpp paged_kv_manager.cpp paged_kv_manager.h
-	$(CXX) $(CXXFLAGS) -I$(GGML_SRC)/include -o $@ $< paged_kv_manager.cpp \
-		-L$(GGML_LIBDIR) -lggml -lggml-base -lggml-cpu -Wl,-rpath,$(GGML_LIBDIR)
-
-ggml-check: $(GGML_BINS)
-	@for t in $(GGML_BINS); do echo "== $$t =="; ./$$t || exit 1; done
-
-clean:
-	rm -f $(BINS) $(GGML_BINS) paged-bench
-
-.PHONY: all check ggml-check clean

backend/cpp/llama-cpp/paged/NVFP4_TEST.md

@@ -1,114 +0,0 @@
-# NVFP4-dense on DGX Spark (GB10, sm_121): is it the quality-preserving FP4 win MXFP4 wasn't?
-
-Test rig: DGX Spark GB10 (sm_121), `~/llama.cpp-pr24423/build` (PR #24423, FP4 MMA + NVFP4
-kernel), wikitext-2-raw, clean BF16 source `q3-4b-bf16.gguf` (the same source used for the
-established MXFP4 / Q4_K fair test). NVFP4 and all comparison quants were produced clean from
-BF16, no imatrix.
-
-## Verdict (short)
-
-YES on all the load-bearing questions, with one honest caveat:
-
-1. llama.cpp CAN produce an NVFP4 GGUF.
-2. NVFP4 quality is Q4_K-class, NOT MXFP4-class: +7.4% PPL vs BF16 (MXFP4 was +30.8%). It is
-   slightly behind Q4_K (+4.8% relative) but in the same ballpark, not on the quality cliff.
-3. NVFP4 routes onto the FP4 MMA kernel and gets the FP4 prefill speedup: ~1.29x Q4_K on the
-   4B, tracking MXFP4 to within 5% (MXFP4 hit 1.58x on the 32B; NVFP4 should track it there too).
-4. Output is coherent.
-
-Bottom line: NVFP4-dense IS the quality-preserving FP4 win MXFP4 wasn't. It delivers
-essentially the full FP4 prefill speedup at roughly Q4_K quality, where MXFP4 paid a 27% quality
-tax for the same speed. LocalAI can support/recommend NVFP4-dense on Blackwell for prefill-bound
-workloads, with the caveat that it is marginally (~5%) behind Q4_K on perplexity; an imatrix-guided
-NVFP4 quant would likely close most of that remaining gap.
-
-## 1. Feasibility: can llama-quantize produce an NVFP4 GGUF? YES
-
-- The type exists with a full quantize path, not just a kernel:
-  - `GGML_TYPE_NVFP4 = 40` (`ggml.h`), `GGML_FTYPE_MOSTLY_NVFP4 = 26`
-  - `quantize_nvfp4` / `quantize_row_nvfp4_ref` / `dequantize_row_nvfp4` registered in `ggml.c`
-  - type_name is `"nvfp4"`, block `QK_NVFP4` (per-16 FP8/E4M3 block scale + global scale)
-- NVFP4 is NOT a top-level `llama-quantize` ftype (no `NVFP4` entry in the allowed-types list,
-  no reference in `tools/quantize/quantize.cpp` or `src/llama-quant.cpp`), BUT
-  `--tensor-type name=nvfp4` resolves it: `parse_ggml_type` matches the arg against
-  `ggml_type_name(...)`, which returns `"nvfp4"`. This is the exact same mechanism that produced
-  MXFP4-dense.
-- Recipe used (mirrors the MXFP4-dense GGUF byte-for-byte in structure: token_embd Q8_0, all
-  norms F32, all 2D attn+ffn weights to FP4):
-
-  ```
-  llama-quantize --tensor-type "attn=nvfp4" --tensor-type "ffn=nvfp4" \
-                 q3-4b-bf16.gguf q3-4b-nvfp4.gguf Q8_0
-  ```
-
-  Result: `q3-4b-nvfp4.gguf`, 2343.93 MiB, 4.89 BPW, ~5 s. (MXFP4-dense was 2350 MiB; same shape.)
-  Every `blk.N.attn_*` and `blk.N.ffn_*` reported `converting to nvfp4`; token_embd Q8_0; norms F32.
-
-The on-box `~/bench/q3-32b-nvfp4*` dirs are vLLM HF safetensors (already 4-bit), not GGUF, and
-do not feed llama.cpp - confirmed and irrelevant.
-
-## 2. Quality (decisive): NVFP4 is Q4_K-class, not MXFP4-class
-
-`llama-perplexity -f wiki.test.raw --chunks 50 -c 512 -ngl 99`, all clean from the same BF16 4B:
-
-| Quant   | PPL    | vs BF16  | vs Q4_K  |
-|---------|--------|----------|----------|
-| BF16    | 13.32  | -        | -        |
-| Q4_K_M  | 13.66  | +2.6%    | -        |
-| NVFP4   | 14.31  | +7.4%    | +4.8%    |
-| MXFP4   | 17.42  | +30.8%   | +27.6%   |
-
-(NVFP4 measured this run: Final PPL = 14.3097 +/- 0.4457.)
-
-NVFP4 lands much closer to Q4_K (gap 0.65 PPL) than to MXFP4 (gap 3.11 PPL). MXFP4's finer
-sibling delivers: the two-level scaling (per-16 FP8 block scale + global scale) recovers almost
-all of the quality MXFP4's coarse per-32 E8M0 scale threw away. It is not quite Q4_K, but it is
-firmly in the "acceptable 4-bit" regime, not the lossy one.
-
-## 3. Speed: NVFP4 routes onto the FP4 MMA kernel
-
-No clean BF16 32B was on the box (only the vLLM NVFP4 safetensors and the Q4_K/MXFP4 32B GGUFs),
-so per the brief this is the 4B speed signal - a 3-way cold A/B on the SAME 4B model, 45 s
-cooldowns between runs (`-npp 512 -ntg 128 -npl 8,32,64 -b 2048 -ub 2048 -ngl 99`):
-
-Prefill S_PP (t/s):
-
-| B   | Q4_K   | NVFP4  | MXFP4  | NVFP4 / Q4_K | NVFP4 / MXFP4 |
-|-----|--------|--------|--------|--------------|---------------|
-| 8   | 4862   | 6313   | 6602   | 1.30x        | 0.96x         |
-| 32  | 5020   | 6497   | 6836   | 1.29x        | 0.95x         |
-| 64  | 5031   | 6490   | 6831   | 1.29x        | 0.95x         |
-
-- NVFP4 prefill is within ~5% of MXFP4 at every batch size -> both land on the same FP4 MMA
-  kernel. NVFP4 does NOT fall back to a slow path.
-- NVFP4 beats Q4_K's int8-MMQ prefill by ~1.29x on the 4B. The established 32B figures were
-  Q4_K S_PP ~767 and MXFP4 ~1209 (1.58x); since NVFP4 tracks MXFP4 to within 5%, NVFP4 on the
-  32B should likewise approach ~1.5x. (The 4B shows a smaller multiplier than the 32B because a
-  smaller model spends proportionally less time in the matmul the FP4 kernel accelerates.)
-- Token-gen (S_TG) is comparable across all three (memory-bound), as expected.
-
-## 4. Coherence
-
-`llama-simple` (llama-cli hangs - avoided), NVFP4 4B:
-- "The capital of France is" -> "...Paris. ...Germany is in Berlin. ...Italy is in Rome.
-  ...Spain is in Madrid. ...Netherlands is in Amsterdam." (all correct)
-- "Q: What is 17 plus 25? A:" -> "42." (correct)
-
-Coherent and factually accurate.
-
-## Recommendation for LocalAI on Blackwell
-
-Support and recommend NVFP4-dense as the FP4 prefill option on Blackwell (sm_120/121), produced
-via `--tensor-type attn=nvfp4 --tensor-type ffn=nvfp4` over a BF16 source (token_embd Q8_0,
-norms F32). It gives ~the full FP4 prefill speedup (FP4 MMA kernel, ~1.3x Q4_K on 4B and
-expected ~1.5x on larger models) at roughly Q4_K quality (+7.4% PPL vs BF16). This is the win
-MXFP4 failed to deliver: MXFP4 paid a +30.8% quality tax for the same speed and was rejected.
-
-Caveats / follow-ups:
-- NVFP4 is still ~4.8% behind Q4_K on PPL. For quality-first deployments where the prefill win
-  does not matter, Q4_K_M remains the better pick.
-- These NVFP4/Q4_K numbers are clean (no imatrix). An imatrix-guided NVFP4 quant is the obvious
-  next step and would likely close most of the remaining gap to Q4_K - worth measuring before a
-  blanket recommendation.
-- A direct 32B NVFP4-vs-Q4_K speed run (needs a clean BF16 32B GGUF, not on the box) would
-  confirm the projected ~1.5x; the 4B signal plus the MXFP4-tracking already make this very likely.

backend/cpp/llama-cpp/paged/PAGED_KV_HIGH_CONCURRENCY.md

@@ -1,115 +0,0 @@
-# Paged KV at high concurrency on a single GB10 - the datacenter-scale test
-
-Closes the open question left by `PR22569_EVAL.md`: that eval could not test the
-"paged KV unlocks thousands of sequences" thesis because **both** KV paths hit the
-`LLAMA_MAX_SEQ=256` compile cap, and the 32B-dense model it used is compute-bound
-(plateaus by npl=128 for an unrelated reason). This run removes both confounders:
-**recompiled `LLAMA_MAX_SEQ=2048`** and used a **bandwidth-bound model (Qwen3-1.7B-Q8_0)**
-where decode aggregate is free to keep climbing with concurrency.
-
-Hardware: NVIDIA GB10 (sm_121, 119 GiB unified LPDDR5X, ~273 GB/s). Build:
-`~/llama.cpp-pr22569` (PR #22569 paged path + the reshape fix), `LLAMA_MAX_SEQ=2048`,
-sm_121 Release. Contiguous = `llama-batched-bench` (unified KV) `S_TG`. Paged =
-`llama-paged -kvp --fit off` `aggregate tps`. `npp=16, ntg/n_predict=128, b=ub=2048,
--ngl 99`. Cold runs, 12 s cooldowns.
-
-## TL;DR for the decision
-
-**On a single GB10, paged KV does NOT deliver a throughput or concurrency win - the
-aggregate-decode ceiling is set by the hardware, not the KV layout, and contiguous KV
-already reaches it.** Measured across two model regimes and concurrency up to 2048
-sequences:
-
-- Aggregate decode **plateaus** once the GPU saturates - for both KV layouts:
-  - 32B-dense (compute-bound): ~540 t/s, flat from npl=128 (prior eval).
-  - 1.7B (bandwidth-bound): ~3,200-3,700 t/s, flat from npl=512 (this run).
-- Paged and contiguous land at the **same ceiling**; PR #22569's paged op was 12-13%
-  *slower* than the mature contiguous flash-attention path at equal concurrency on 32B.
-- Pushing concurrency past the plateau is **actively harmful to UX**: per-sequence
-  throughput collapses (23 -> 1.9 tok/s) and TTFT explodes (0.6 s -> 4.3 s avg, **64 s
-  max**) while aggregate stays flat.
-
-**vLLM's ~24k aggregate headline is unreachable on a single GB10 with these models
-regardless of KV layout** - it needs aggregate memory bandwidth / compute that one GB10
-does not have (i.e. many GPUs). Paged KV is a **memory-capacity / anti-fragmentation /
-prefix-sharing** feature, not a single-node throughput-ceiling feature. The static
-single-model benchmark deliberately does not create the memory-pressure regime where
-paging pays off, which is exactly why no win appears.
-
-## The numbers
-
-### Aggregate decode vs concurrency, Qwen3-1.7B-Q8_0 (bandwidth-bound), `LLAMA_MAX_SEQ=2048`
-
-| npl | contiguous `S_TG` (t/s) | paged `aggregate tps` (t/s) | paged per-seq tps | paged TTFT avg / max |
-|----:|------------------------:|----------------------------:|------------------:|---------------------:|
-| 128 | 2,643 | 2,887 | 23-25 | - |
-| 256 | 2,925 | - | - | - |
-| 512 | 3,215 | 3,637 | 7.2-7.8 | 0.57 s / 0.90 s |
-| 1024 | 3,118 | 3,695 | 3.7-4.2 | 1.17 s / 2.37 s |
-| 2048 | (not run) | 3,608 | 1.9-14.6 | 4.28 s / **63.8 s** |
-
-Both paths flatten by npl~512. 8x more concurrency (128->1024) buys contiguous only
-**+18%** and paged **+28%**, then both stop. (The two tools meter slightly differently -
-`llama-paged` aggregate vs `batched-bench` decode-only `S_TG` - so the small paged-vs-
-contiguous offset is not a real paged advantage; the prior apples-to-apples 32B eval had
-paged 12-13% *behind*.)
-
-### Why it plateaus (the hardware ceiling, not the KV layout)
-
-Decode is memory-bandwidth-bound: each step reads the model weights once and shares that
-read across the whole batch. Once concurrency is high enough that the shared weight-read
-is amortized, the per-step cost is dominated by KV reads + attention + host work, none of
-which paging makes cheaper. The GB10's ~273 GB/s sets the floor; at the plateau the GPU
-is ~saturated. Adding sequences past that point cannot raise aggregate - it only divides
-the same throughput across more users (per-seq tps falls, TTFT rises). The 32B-dense case
-plateaus even earlier (npl=128) because it saturates on **compute** (weight matmuls), not
-bandwidth - the kernel decomposition is in `VLLM_DECOMPOSITION.md`.
-
-## What paged KV is actually for (the honest, deliverable value)
-
-Paging never helps a static, uniform-length, single-model benchmark on a GPU with memory
-to spare - there is no fragmentation and no over-reservation to reclaim. Its real wins,
-which require the regime this hardware+benchmark does not exercise, are:
-
-1. **Concurrent-tenant capacity under memory pressure.** Block KV fits more *diverse*
-   in-flight sequences (variable, dynamically arriving/leaving contexts) without the
-   contiguous path's per-slot reservation/fragmentation. Pays off when KV memory, not
-   compute/bandwidth, is the binding constraint - i.e. at multi-GPU datacenter scale or
-   with very long/variable contexts.
-2. **Cross-request prefix sharing.** A chained-hash block cache shares identical system
-   prompts / RAG preambles across requests (vLLM's `block_pool.py` + block-hash map). A
-   real token-budget win for shared-prefix workloads; PR #22569 defers this to a
-   non-existent Phase 2 (our from-scratch P0 has the machinery).
-
-These are measured as **max concurrent distinct tenants** and **KV memory saved**, not as
-aggregate tok/s on one model. They do not move the single-GB10 throughput ceiling.
-
-## Recommendation
-
-- **Do not pitch paged KV as a single-GB10 throughput lever** - it is measured flat to
-  the contiguous ceiling (and PR #22569 is slower). Doing so would not survive a
-  benchmark.
-- **The single-GB10 throughput story is already strong without paging:** llama.cpp is
-  ahead of vLLM single-stream (MXFP4 1153 > 800) and at ~70-81% of vLLM aggregate at
-  npl<=128 with a near-identical batching multiplier (`VLLM_DECOMPOSITION.md`). Ship the
-  MXFP4/NVFP4-dense prefill win (`NVFP4_TEST.md`) - that is the cheap, real, defensible
-  Blackwell number.
-- **If datacenter-scale (thousands of concurrent tenants) is the genuine target,** the
-  lever is **multiple GPUs** plus paged KV's **capacity + prefix-sharing** features -
-  framed and measured as concurrent-tenant capacity and KV memory saved, on a
-  variable-context / shared-prefix workload. A single GB10 cannot produce the ~24k
-  aggregate regardless of KV layout; that is a fleet-level result.
-
-## Reproduction (DGX, `~/llama.cpp-pr22569`, `LLAMA_MAX_SEQ=2048`)
-
-```sh
-M=~/bench/draft17/Qwen3-1.7B-Q8_0.gguf
-# contiguous
-for NPL in 128 256 512 1024; do
-  ./build/bin/llama-batched-bench -m $M -npp 16 -ntg 128 -npl $NPL -ngl 99 \
-    -b 2048 -ub 2048 -fa on -c $((NPL*160)); done
-# paged
-for NPL in 512 1024 2048; do
-  ./build/bin/llama-paged -m $M -kvp --fit off -ngpub 32768 -ncpub 128 \
-    -np $NPL -ns $NPL -n 128 -b 2048 -ub 2048 -ngl 99; done
-```

backend/cpp/llama-cpp/paged/PAGED_KV_TARGET_READINESS.md

@@ -1,170 +0,0 @@
-# Paged KV: target-readiness (correctness, dynamic benchmark, 2xH200 projection)
-
-Target hardware: **~2x H200** (281 GB HBM3e total, ~4.8 TB/s per GPU). The GB10 box is
-the *test* rig, not the target - and several earlier "no win" findings are GB10-specific
-artifacts (low bandwidth caps throughput before KV memory ever binds). This document
-delivers the three things needed to push paged KV toward the real target:
-
-1. **Correctness** of the paged path - verified (and a blocking bug found + fixed).
-2. **A dynamic-load benchmark** that actually exercises where paging wins (`paged-loadgen.cpp`).
-3. **A projection** of the paged-KV payoff on 2x H200, grounded in measured GB10 numbers.
-
----
-
-## 1. Correctness: PASS (after fixing the auto-fit OOM)
-
-`test-paged-kv-e2e` checks the paged decode path against the contiguous reference
-(greedy argmax + top-5 set overlap >= 4). On the box it was previously **unverified** -
-it aborted at context creation. Root cause found:
-
-- `common_fit_paged_kv_blocks` (`common/common.cpp:1144`) **unconditionally overrides**
-  `n_gpu_blocks` from `ggml_backend_dev_memory`, which **over-reports free VRAM on the
-  GB10 integrated/unified device** (it sized **~245 GB of KV on a 119 GB box** ->
-  `cudaMalloc` OOM -> `GGML_ASSERT` abort in `llama-kv-cache-paged.cpp:74`). The test's
-  explicit `n_gpu_blocks=64` was being clobbered because `params.fit_params` defaults on.
-
-**Fix (item-1 patch, applied on the box):**
-
-```diff
---- a/tests/test-paged-kv-e2e.cpp
-+++ b/tests/test-paged-kv-e2e.cpp
-@@ run_paged()
-     params.kv_paged      = true;
-+    params.fit_params    = false;  // honor explicit n_gpu_blocks; GB10 dev_memory over-reports free VRAM
-     params.n_gpu_blocks  = 64;
-```
-
-**Result (Qwen3-0.6B-Q8_0, GB10):**
-
-```
-test-paged-kv-e2e: top-5 argmax match: ref=3743 paged=3743
-test-paged-kv-e2e: top-5 set overlap: 5/5 (require >= 4)
-test-paged-kv-e2e: PASSED
-```
-
-The paged op is **numerically greedy-equivalent to the contiguous path**. The reshape
-bug from `PR22569_EVAL.md` (decoupled head_dim) is already applied in the checkout.
-
-**Target-readiness caveat (the durable fix, not just the test):** the auto-fit itself is
-brittle and must be hardened before it runs on a real serving box - even though
-`ggml_backend_dev_memory` reports correctly on a discrete H200, the function should still
-(a) early-return when `!params.fit_params`, (b) **clamp** the computed `n_gpu_blocks` so
-`n_gpu_blocks * block_bytes <= free_vram - margin` using the *actual* KV element size, and
-(c) not override an explicitly-set value. One-screen change in `common_fit_paged_kv_blocks`.
-
----
-
-## 2. Dynamic-load benchmark - `paged-loadgen.cpp`
-
-**Why the existing tools show no paged win:** `llama-batched-bench` and the stock
-`examples/paged/paged.cpp` both run **fixed-length, all-arrive-at-once, single-prompt**
-load. That has no over-reservation and no fragmentation, so contiguous KV is already
-memory-optimal and paging has nothing to reclaim (`PAGED_KV_HIGH_CONCURRENCY.md`). The
-paged win only exists under **variable lengths + continuous arrival + shared prefixes** -
-the real serving regime. No tool in the tree creates it.
-
-`paged-loadgen.cpp` (committed here) does, via the confirmed `llama_paged_scheduler_*`
-API:
-
-- **shared system prefix** (`LG_PREFIX` tokens) prepended to every request -> exercises
-  cross-request prefix sharing,
-- **variable prompt length** (`LG_SUFMIN..LG_SUFMAX` unique suffix),
-- **bimodal generation length** (`LG_GENLONG` for `LG_LONGPCT`% of requests, else
-  `LG_GENSHORT`) - the over-reservation driver,
-- **continuous arrival**: keeps `LG_INFLIGHT` requests live, admitting a new one each time
-  one finishes.
-
-It reports the load-bearing number for the buy decision - the **capacity ratio**:
-
-```
-paged peak KV      = sum over live seqs of ceil(used/block)*block * kv_bytes_per_token
-contiguous reserve = peak_inflight * max_ctx * kv_bytes_per_token   (worst-case per slot)
-CAPACITY RATIO     = contiguous_reserve / paged_peak   (+ prefix sharing on top)
-```
-
-`kv_bytes_per_token = 2 * n_layer * n_head_kv * head_dim * sizeof(f16)` - confirmed against
-`llama-kv-cache-paged.cpp` (e.g. Qwen3-32B: 2*64*8*128*2 = **256 KiB/token**).
-
-**How to run (on the target):** drop into PR #22569's `examples/paged/`, add to its
-CMakeLists next to `llama-paged`, build, then e.g.
-`LG_INFLIGHT=2048 LG_LONGPCT=15 paged-loadgen -m <model> -kvp --fit off -ngpub <N> -ncpub <M> -ngl 99`.
-Sweep `LG_INFLIGHT` to the throughput plateau and read the capacity ratio at that point.
-It is written to run on the target (2x H200) where the regime exists; on GB10 it runs but
-the ratio is uninteresting because throughput plateaus before memory binds (see below).
-
----
-
-## 3. Projection to 2x H200 (grounded in measured GB10 numbers)
-
-### Measured on GB10 (this work)
-
-| model | decode plateau (aggregate) | plateau concurrency | bound by |
-|---|---|---|---|
-| Qwen3-32B-Q4_K_M (dense) | ~540 t/s | npl ~128 | compute |
-| Qwen3-1.7B-Q8_0 | ~3,200 t/s | npl ~512 | bandwidth |
-
-### Hardware ratios (per GPU, then 2x TP at ~85% scaling)
-
-| | GB10 | H200 | per-GPU x | 2x H200 (TP) x |
-|---|---|---|---|---|
-| mem bandwidth | 273 GB/s | ~4.8 TB/s | 17.6 | ~30 |
-| BF16 compute | ~213 TFLOP | ~989 TFLOP | 4.6 | ~8 |
-| HBM | 119 GB | 141 GB | 1.18 | 2.4 (281 GB) |
-
-Decode is bandwidth-bound, so **both the aggregate ceiling and the concurrency at which it
-is reached scale with bandwidth (~30x on 2x H200)**:
-
-- **32B-dense aggregate decode ceiling:** 540 x 30 ~= **16,000 t/s**, reached at
-  ~128 x 30 ~= **3,800 concurrent sequences**.
-
-### Why paged KV becomes the binding lever on 2x H200 (and didn't on GB10)
-
-To reach that ~16k t/s ceiling you must hold **~3,800 sequences** of KV. The memory math:
-
-- 32B weights (FP8) ~= 32 GB, sharded over 2 GPUs -> ~250 GB HBM free for KV.
-- 32B KV = 256 KiB/token. At an avg held context of 2,000 tokens, **per seq = 512 MiB**.
-- Contiguous unified KV (reserve for the live set) fits ~250 GB / 512 MiB ~= **~490
-  sequences** - **8x short of the 3,800 needed to reach the throughput ceiling.**
-
-So on 2x H200 **KV memory is the binding constraint at the throughput-optimal concurrency**,
-and contiguous KV strands most of the bandwidth (you'd run at a fraction of 16k t/s). This
-is the gap paged KV closes. On GB10 it never appeared because GB10's 30x-lower bandwidth
-caps decode at npl ~128, whose KV fits in memory trivially - the constraint order is
-inverted on the real target.
-
-### Magnitude of the paged win
-
-Paging recovers concurrency two ways, both multiplicative on achievable throughput:
-
-1. **No over-reservation.** Contiguous must back `max_ctx` per slot; paging uses
-   `ceil(actual/block)`. For a realistic bimodal workload (most generations short, ~15%
-   long, prompts ~512) the average held context is several-fold below `max_ctx` ->
-   `paged-loadgen` capacity ratio typically **~4-10x** (it measures the exact number for
-   your workload's length distribution).
-2. **Cross-request prefix sharing** of shared system prompts / RAG preambles - additional,
-   workload-dependent (chained-hash block cache; vLLM's `block_pool.py`).
-
-Net: on 2x H200, paged KV is plausibly the difference between serving **~500 and ~3,800**
-concurrent 32B sequences in HBM, i.e. between a fraction of and ~all of the **~16k t/s**
-decode ceiling. **That is the datacenter payoff, and it is real on the target even though
-GB10 cannot exhibit it.**
-
-### Honest caveats for the buy case
-
-- These are **projections** from GB10 + spec ratios; the capacity multiplier depends on the
-  workload's context-length distribution (more variable -> bigger paged win) and TP
-  efficiency. `paged-loadgen` measures it directly once you have target-GPU time.
-- The **paged op itself still needs work**: PR #22569's `ggml_paged_attn` was 12-13%
-  *slower* than the mature contiguous flash-attention path at equal concurrency
-  (`PR22569_EVAL.md`), lacks prefix sharing (deferred to a non-existent Phase 2), and has
-  the fit-robustness bug above. Adopting paged KV for the target means either hardening
-  #22569 or finishing the from-scratch P4 - the capacity win above assumes a *correct,
-  competitive* op, which is the remaining engineering.
-- Prefill on either KV layout is compute-capped, not a paged concern.
-
-**Bottom line for the decision:** paged KV **is** the right lever for the 2x H200 target -
-the GB10 "no win" result is a bandwidth artifact, not a verdict. The paged path is now
-**correctness-verified**, the **benchmark to size the win exists**, and the projection
-says the payoff is **~5-10x concurrent-tenant capacity -> several-fold higher aggregate
-decode** on the target. The remaining work is hardening/finishing the paged op, not
-proving the thesis.

backend/cpp/llama-cpp/paged/PHASED_VLLM_PARITY_PLAN.md

@@ -1,55 +0,0 @@
-# Making llama.cpp/LocalAI a viable vLLM alternative — phased plan
-
-Goal: close the practical gap to vLLM for both single-user *speed* and multi-user *throughput*, while keeping
-quality (no lossy quant). Grounded in measured benchmarks + research (`BENCHMARKS.md`, `BLACKWELL_KERNEL_GAPS.md`,
-`VLLM_THROUGHPUT_GAP.md`). The gap is NOT one thing — each phase targets a distinct, independent lever.
-
-## Where vLLM actually leads (measured, GB10 / Qwen3-32B)
-
-- **Single-user decode:** ~parity (10.2 vs 11.7) — bandwidth-bound. vLLM's edge is **spec-dec** (lossless).
-- **Multi-user decode:** gap grows to ~2.2× at B=64 (kernel + scheduler).
-- **Prefill aggregate:** llama plateaus ~765, vLLM scales to 24k — **paged KV + chunked prefill + kernel**.
-- Note: on GB10 vLLM's FP4 trump card is *broken* (falls back to Marlin); llama.cpp runs reliably — a real
-  viability point. vLLM is structurally ahead mainly via **paged KV, chunked prefill, cross-request prefix cache**.
-
-## Phases
-
-### Phase 1 — Hardware-tuned config (PR #10411) — DONE
-Folded into the hardware-defaults path (`core/config/hardware_defaults.go`):
-- Blackwell physical batch (n_ubatch) = 2048.
-- **VRAM-scaled `n_parallel` default** (>=32GiB→8, >=8→4, >=4→2): turns on concurrency + continuous batching,
-  which the backend leaves OFF at its `n_parallel=1` default. Unified KV → slots share the budget (no extra
-  KV memory). Single-host (local GPU) + distributed router (per node). Already-good defaults confirmed:
-  flash-attn=auto, context=4096.
-
-### Phase 2 — Paged / block KV cache  ← biggest structural multi-user lever
-vLLM's PagedAttention lifts KV utilization ~20-38% → ~96%. llama.cpp's own A10G data (draft PR #22569):
-contiguous OOMs at 26 seqs / 496 t/s → paged 247 seqs / 1256 t/s (**~9.5× concurrency, 2.5× aggregate**).
-- Build on / complete **upstream draft PR #22569** (`-kvp`, block manager + paged-attn ggml op, FCFS scheduler)
-  rather than the from-scratch series we prototyped (`paged/`). Our CPU-verified block manager + gather-read
-  design informs the review/port; the upstream momentum is the place to land it.
-- Phase 2b: cross-request prefix sharing (block-hash dedup) — our `PagedKVManager` already implements it.
-
-### Phase 3 — Prefill amortization (chunked prefill + n_batch/n_ubatch split)
-llama aggregate prefill plateaus because (a) one prompt saturates compute, (b) the per-forward GEMM M-dim is
-capped at `n_ubatch`=512, (c) no scheduler chunked prefill (draft #10718 abandoned).
-- Split logical `n_batch` from physical `n_ubatch` (LocalAI ties them today) so concurrent prefills batch into
-  a larger logical batch while keeping ubatch at the Blackwell sweet spot (2048).
-- Chunked prefill + prefill/decode co-batching in the server slot scheduler.
-
-### Phase 4 — Batched-GEMM kernel tuning (the decode 2.2× + prefill height)
-Per `BLACKWELL_KERNEL_GAPS.md`: dense int8-MMQ at ~21% of ceiling, MoE FP4-MMA at ~5%. Both untuned for
-Blackwell. To MATCH: tune MMQ or a Marlin-style W4A16 BF16 GEMM (FP4 not required — GB10 is INT8==BF16). To
-BEAT (2×): fix+tune the existing FP4-MMA on sm_121 (build-flag/`-O3`-miscompile, not greenfield).
-
-### Phase 5 — Backend GPU sampling
-CPU per-sequence sampling caps GPU util ~60% beyond n_parallel ~8-16 (upstream PR #17004). Track/adopt.
-
-### Cross-cutting — Speculative decoding (single-user speed, quality-preserving)
-Dense ≥14B: lossless ~1.8-3×. llama.cpp has `-md`/`--spec-draft-*`. Wire a draft-model field in the model
-config + ship Qwen3 target+draft (1.7B) pairs in the gallery. NOT for MoE-A3B (nothing to amortize).
-
-## Sequencing rationale
-Phase 1 (config) ships now — biggest immediate multi-user win for zero kernel work (concurrency was OFF).
-Phase 2 (paged KV) is the highest-leverage structural build and has upstream momentum. Phases 3-4 are deeper
-(scheduler + kernel). Spec-dec is independent and can land any time for single-user speed.

backend/cpp/llama-cpp/paged/PR17004_EVAL.md

@@ -1,90 +0,0 @@
-# PR #17004 (backend / GPU sampling) evaluation on DGX Spark (GB10, sm_121)
-
-Date: 2026-06-21. Hardware: NVIDIA GB10 (GB10, sm_121), CUDA 13.0, cmake 3.28.
-Model: `Qwen3-32B-Q4_K_M.gguf`. LocalAI pin: `LLAMA_VERSION=f3e182816421c648188b5eab269853bf1531d950` (2026-06-17).
-
-## TL;DR (clean negative)
-
-1. **PR #17004 is MERGED and is ALREADY present in our pinned llama.cpp `f3e1828`.** There is nothing to apply / cherry-pick / patch. The `-bs/--backend-sampling` CLI arg, the `llama_set_sampler` / `llama_get_sampled_*` API, and the GPU argsort/top-k/cumsum/softmax kernels are all in the pin.
-2. **The prescribed benchmark cannot test the fix.** `llama-batched-bench` does ZERO sampling - it feeds random tokens (`std::rand() % n_vocab`). Its ~540 t/s plateau is therefore **not** sampling-bound, and enabling backend sampling does nothing to it. The valid tool is `llama-batched` (examples/batched), which the PR updated to drive per-sequence sampler chains and which actually exercises `-bs`.
-3. **In a controlled real-sampling A/B (same `llama-batched` harness, CPU vs GPU sampler), GPU sampling gave only +25% at np=32, +3% at np=64, and CRASHED (`GGML_ASSERT(obj_new)`, graph-context alloc) at np=128 and np=256** - exactly the multi-user regime the investigation cares about.
-4. **nsys at np=64: GPU kernel profile and GPU-busy time are essentially identical with and without the fix** (CPU 392.5 t/s / GPU 404.2 t/s; total GPU kernel+memop time ~4.05 s in both). Sampling kernels do not even appear among the top GPU contributors. GPU utilization did **not** rise.
-5. **Conclusion: PR #17004, in the state shipped by our pin, does NOT break the ~540 plateau and does not move decode aggregate toward the ~2700 GPU-bound ceiling or past vLLM's 667.** It is modest at low parallelism and unusable (crash) at the high parallelism in question. The PR's own guidance ("recommended `--parallel 1`", "will take time to mature") matches what we measured.
-
-## 1. What PR #17004 does + state
-
-- Title: "sampling : add support for backend sampling". **State: MERGED** into `master` (PR head branch `gpu-sampling`). 44 files, +4133/-296.
-- `libllama`: new `llama_context_params.samplers` / `n_samplers`, `llama_set_sampler`, `llama_get_sampled_*`, `llama_sampler_seq_config`, updated `llama_sampler_i`. Sampler chain can now run inside the compute graph on the backend (GPU) instead of on the CPU after `llama_decode`.
-- CUDA: optimized/new `argsort`, `top-k`, `cumsum`, `softmax` kernels; CMake option `-DGGML_CUDA_CUB_3DOT2=ON` (builds a CCCL v3.2 prerelease for faster top-k).
-- Tools: new `-bs, --backend-sampling` arg in `common/arg.cpp` (line 1921); server (`server-context.cpp`) per-slot wiring; `examples/batched/batched.cpp` updated.
-- Supported backend samplers: `top-k`, `top-p`, `min-p`, `temp` (+ dist). **Limitations (from the PR): not compatible with grammar sampling; single output per sequence per batch; no save/load of sampling state; recommended only with `--parallel 1` and CUB_3DOT2.** Open follow-ups: #18547 (avoid graph reallocations), #18550 (skip inactive samplers in parallel decode).
-- It DOES target the CPU-side per-sequence sampling stall we hypothesised - the mechanism is correct. Maturity is the problem.
-
-Note: the GitHub API reports `mergedAt: 2026-01-04`, but the PR contains June 2026 upstream-merge commits and the feature is verified present in our 2026-06-17 pin, so treat the date field as a metadata quirk. What matters: the code is in `f3e1828`.
-
-## 2/3. Apply + build
-
-No apply needed (already in pin). Built from a clean `git worktree` at `f3e1828` (`~/llama-pr17004`), to avoid disturbing the existing diffusion build:
-
-```
-cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON \
-  -DCMAKE_CUDA_ARCHITECTURES=121 -DLLAMA_MAX_SEQ=256 \
-  -DGGML_CUDA_CUB_3DOT2=ON -DLLAMA_CURL=OFF
-cmake --build build --target llama-batched llama-batched-bench -j20
-```
-
-**Build: SUCCESS** (CUB_3DOT2=ON FetchContent fetched and compiled despite flaky net; sm_121; LLAMA_MAX_SEQ=256). `-bs/--backend-sampling` confirmed present in `llama-batched --help`.
-
-## 4. Decode aggregate: fix vs baseline vs vLLM
-
-### 4a. `llama-batched-bench` (NO sampling - reconfirms the plateau, unaffected by the fix)
-`-npp 16 -ntg 128 -npl 32,64,128,256 -c 40960 -b 2048 -ub 2048`
-
-| npl | S_TG t/s |
-|-----|----------|
-| 32  | 241.8 |
-| 64  | 395.1 |
-| 128 | 542.6 |
-| 256 | 567.2 |
-
-Reproduces the ~540 plateau. Because this tool never samples, `-bs` is irrelevant here - the plateau is decode/host-overhead-bound, not sampling-bound.
-
-### 4b. `llama-batched` real-sampling A/B (CPU sampler vs `-bs` GPU sampler, identical harness)
-`-kvu -n 128 -np {32,64,128,256} -c 40960 --seed 1` (samplers: top-k 40 / top-p 0.95 / temp 0.8)
-
-| np  | CPU sampling t/s | GPU `-bs` sampling t/s | delta |
-|-----|------------------|------------------------|-------|
-| 32  | 174.1 | 217.5 | +25% |
-| 64  | 390.5 | 403.4 | +3.3% |
-| 128 | 497.9 | **CRASH** `GGML_ASSERT(obj_new) ggml.c:1768` | - |
-| 256 | 396.7 | **CRASH** `GGML_ASSERT(obj_new) ggml.c:1768` | - |
-
-(`llama-batched` absolute t/s is lower than `batched-bench` because it does real sampling plus per-token detokenize/string/stream work; the A/B *within* this harness isolates the sampler cost.)
-
-**Does the fix break the plateau? No.** GPU sampling helps only at low parallelism and the gain shrinks as np rises (+25% -> +3%), then the path crashes at np>=128 - i.e. it fails in exactly the multi-user regime where the plateau matters. It does not approach the ~2700 ceiling and does not pass vLLM's 667. The CPU-sampling curve itself peaks at np=128 (498) and *drops* at np=256 (397), confirming CPU sampling is a scaling wall - but PR #17004 as shipped does not lift it because the GPU path is unstable there.
-
-## 5. GPU-utilization mechanism (nsys, np=64, the highest np where `-bs` survives)
-
-`nsys profile -t cuda ... -n 96 -np 64`
-
-| mode | decode t/s | total GPU kernel+memop time | top GPU contributors |
-|------|-----------|------------------------------|----------------------|
-| CPU sampling | 392.5 | ~4.07 s | mul_mat_q (55%+17%), flash_attn (5.7%), mul_mat_vec (2%) |
-| GPU `-bs`    | 404.2 | ~4.04 s | identical set; sampling kernels not in top contributors |
-
-GPU-busy time and the kernel mix are **essentially unchanged** between modes. The argsort/top-k/cumsum/softmax sampling kernels are negligible in the timeline; the only visible difference is H2D memcpy *instances* rising 1,495 -> 7,076 (pinned-memory sampler transfers) at ~unchanged total memcpy time. **GPU utilization did not rise.** This directly refutes the idea that, at this workload, the GPU idle is dominated by CPU sampler arithmetic - moving the sampler onto the GPU barely changed throughput (+3%) and did not raise GPU occupancy. The ~80% idle measured elsewhere is dominated by something other than the sampler math (host-side batch construction / synchronization / detokenize), which PR #17004 does not address.
-
-(np=256 nsys "with fix" could not be captured: `-bs` aborts there. Fixing the crash needs the unmerged follow-ups #18547/#18550, not in our pin.)
-
-## LocalAI adoption path
-
-**The code arrives transparently with a version bump; enabling it is not transparent.**
-
-- `backend/cpp/llama-cpp/prepare.sh` copies all of upstream `llama.cpp/tools/server/*` (including the #17004-modified `server-context.cpp` / `server-task.cpp` / `server-common.cpp`) into `tools/grpc-server/`, and `grpc-server.cpp` `#include`s them. So once `LLAMA_VERSION` points at a commit containing #17004 (our pin `f3e1828` already does), the backend-sampling machinery compiles into `grpc-server` automatically. **No vendored patch in `patches/` is required for the code.**
-- The vendored `server-context.cpp` already does the per-slot wiring (around line 1615): `backend_sampling &= task.params.sampling.backend_sampling`, also disabled for speculative decode and for pre-sampling logits (`n_probs>0`), then `llama_set_sampler(ctx_tgt, slot.id, common_sampler_get(slot.smpl))`.
-- **But it is OFF unless `task.params.sampling.backend_sampling == true`.** LocalAI's `grpc-server` builds `params` itself from the gRPC request and never sets this flag (and does not pass the upstream `--backend-sampling` CLI arg). So as-is, LocalAI compiles the feature but never uses it. **A small grpc-server change is needed**: read a LocalAI model option / env and set `params.sampling.backend_sampling = true` (global or per-request).
-- For performant CUDA top-k, add `-DGGML_CUDA_CUB_3DOT2=ON` to the llama-cpp CUDA `CMAKE_ARGS` in the Makefile (optional; a non-CUB fallback exists).
-- **Caveats that blunt the benefit for LocalAI specifically:** grammar-constrained requests (JSON-schema / tool calls - a large share of LocalAI traffic), `logprobs`/`n_probs>0`, and speculative decoding all fall back to CPU sampling by the gating above; and the GPU path crashes at np>=128 in this pin. So even after wiring the flag, the multi-user throughput case would not benefit (and would crash) until the follow-up PRs (#18547/#18550) land and stabilise high-parallelism backend sampling.
-
-### Recommendation
-Do **not** adopt PR #17004 as the multi-user throughput fix yet. It is already in the tree but is immature at the parallelism that matters (crashes at np>=128, modest gains below). The measured bottleneck at this workload is not the sampler arithmetic (nsys shows GPU-busy unchanged when sampling moves to GPU). Re-evaluate after #18547/#18550 merge into a future pin; revisit the host-side decode/batch-construction overhead as the more likely real lever.

backend/cpp/llama-cpp/paged/PR22569_EVAL.md

@@ -1,136 +0,0 @@
-# Evaluation: llama.cpp PR #22569 (paged KV cache, `-kvp`) on DGX Spark (GB10, sm_121)
-
-Question: is upstream draft PR #22569 the right base to give LocalAI vLLM-class
-high-concurrency GPU throughput, or should we finish our own from-scratch P4
-(`backend/cpp/llama-cpp/paged/`)?
-
-Date: 2026-06-21. Hardware: NVIDIA GB10 (compute 12.1 / sm_121), 122502 MiB unified
-memory, CUDA 13.0, gcc 13.3. Models: `Qwen3-32B-Q4_K_M.gguf` (18.4 GB, 64 layers,
-n_head 64 / n_head_kv 8 / head_dim 128 / n_embd 5120) and `Qwen3-0.6B-Q8_0.gguf` for
-the correctness gate.
-
-## TL;DR verdict: DO NOT adopt #22569. Finish our own P4.
-
-On GB10 with a 32B dense model, PR #22569 delivers **no throughput win and no concurrency
-win** - it is ~12% *slower* than the existing contiguous path and hits the *same*
-256-sequence ceiling. The "scale to thousands of sequences like vLLM" premise does not
-hold for this PR or this hardware/model. On top of that it is broken out of the box,
-wired to the wrong integration surface, and a contested draft.
-
-## 1. Builds? Correct?
-
-- **Builds: YES.** Cloned `matiaslin/llama.cpp@paged_attention` (PR #22569, single commit
-  `0b0f7bd...`, base = current master). Clean CUDA build for sm_121
-  (`-DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=121 -DCMAKE_BUILD_TYPE=Release`).
-  `llama-paged`, `llama-batched-bench`, `test-paged-kv`, `test-paged-kv-e2e` all link.
-  It is self-contained (ships its own CPU+CUDA `ggml_paged_attn` op) and does **not**
-  depend on the competing CUDA PR #17579 (ericcurtin, `--pagedattention`).
-
-- **Runs out of the box: NO.** `llama-paged -kvp` on Qwen3-32B *and* Qwen3-0.6B crashes
-  at context creation:
-  `build_attn(llm_graph_input_attn_kv_paged*) -> ggml_reshape_2d ->`
-  `GGML_ASSERT(ggml_nelements(a) == ne0*ne1)` (src/llama-graph.cpp:2556). Same crash with
-  `--fit off` (so it is the real graph, not just the memory probe).
-  **Root cause:** the paged path hardcodes `ggml_reshape_2d(cur, hparams.n_embd, ...)`,
-  wrong for any model where `n_head*head_dim != n_embd`. Qwen3 decouples head_dim:
-  32B = 64*128 = **8192** vs n_embd 5120; 0.6B = 16*128 = **2048** vs 1024. The PR's
-  "qwen3 verified" claim does **not** hold against current Qwen3 GGUFs. Fix is ~1 line
-  (use the real attention width `cur->ne[0]*cur->ne[1]`); applied for the rest of the eval.
-
-- **`fit_params` (`-ngpub` auto-sizing) also crashed on GB10** in the same reshape path
-  during the device-memory probe (before the fix). After the reshape fix, paged
-  auto-fit works (sized 96624 GPU blocks on the 0.6B from 85 GiB free).
-
-- **Correctness after the reshape fix:** paged decode runs and produces **coherent**
-  output on Qwen3-32B (sensible mercury / miso-soup / Starry-Night answers across 128 and
-  256 concurrent sequences), indicating the `ggml_paged_attn` op is functionally roughly
-  correct. PR's own greedy/top-K equivalence test (`test-paged-kv-e2e`, top-K argmax +
-  top-5 overlap >= 4 + first-4-token greedy match vs non-paged) on Qwen3-0.6B did
-  **not** reach a PASS/FAIL verdict on GB10: its paged auto-fit grabs ~88 GiB
-  (96531 blocks) and the run then stalls at cache init (a third GB10 fit-robustness
-  issue, distinct from the reshape bug). So the formal greedy-equivalence gate is
-  **unverified on this box**, but the qualitative evidence (coherent multi-sequence 32B
-  output with explicit small `-ngpub`) indicates the fixed op is roughly correct. This
-  does not change the verdict, which is decided by throughput below.
-
-## 2. Throughput: paged vs contiguous on GB10 (Qwen3-32B-Q4_K_M)
-
-Contiguous = `llama-batched-bench` (unified KV, continuous batching), S_TG decode tok/s.
-Paged = `llama-paged -kvp --fit off` (its scheduler-driven continuous-batching loop),
-`aggregate tps`. Both `npp~16, ntg/n_predict=128, n_batch=n_ubatch=2048, -ngl 99`.
-
-| npl  | contiguous (S_TG t/s) | paged `-kvp` (agg t/s) | outcome |
-|------|----------------------|------------------------|---------|
-| 128  | **537** (S 553)      | **477**                | both run; paged ~12% slower |
-| 256  | **541** (S 550)      | **471**                | both run; paged ~13% slower; neither gains over 128 |
-| 512  | FAIL                 | FAIL                   | **both** die: `n_seq_max must be <= 256` |
-| 1024 | FAIL                 | FAIL                   | **both** die: `n_seq_max must be <= 256` |
-
-### The decisive facts
-
-1. **PR #22569 does NOT lift the 256-sequence ceiling.** Both contiguous and paged fail
-   identically at npl 512/1024 with `n_seq_max must be <= 256` (llama.cpp's compile-time
-   `LLAMA_MAX_SEQ`). It is **not** an OOM - GB10 has 119 GiB and at npl=256 contiguous KV
-   is only 16 GiB. Paging gives **zero** concurrency headroom over contiguous here. The
-   "paged unlocks thousands of seqs" premise is false for this PR.
-
-2. **Paged is slower, not faster.** The fresh `ggml_paged_attn` op (477/471 t/s) loses to
-   the mature CUDA flash-attention contiguous path (537/541 t/s) by ~12-13% at equal
-   concurrency. The PR's A10G "2.5x" came entirely from contiguous OOMing at 26 seqs on a
-   24 GiB card; that lever does not exist on GB10's 119 GiB.
-
-3. **The 32B dense model is compute-bound and plateaus by npl=128 on GB10.** Aggregate is
-   flat from 128->256 (contiguous 537->541; paged 477->471). Doubling concurrency buys
-   nothing because the GPU is already saturated on the 32B weight matmuls. Even if we
-   recompiled with a larger `LLAMA_MAX_SEQ`, aggregate would not climb - so vLLM-class
-   ~24k aggregate is **unreachable for 32B-dense on a single GB10 regardless of KV
-   layout**. The throughput gap to vLLM at this model/hardware is a compute/bandwidth
-   problem, not a KV-fragmentation problem.
-
-## 3. Verdict and reasoning: finish our own P4
-
-**Do not adopt #22569 as the base.** Reasons:
-
-- **No win on target hardware.** Even fully completed, on GB10 + 32B it is slower than
-  what we already have and capped at the same 256 seqs. There is no throughput or
-  concurrency dividend to harvest here.
-- **Wrong integration surface.** Paged is driven only by a brand-new parallel C API
-  (`llama_paged_scheduler_init/add_request/prepare_batch/get_batch_info/update/...`) and a
-  bespoke `examples/paged` loop. `-kvp`/`--kv-paged` is gated to `LLAMA_EXAMPLE_PAGED`
-  only - it is NOT wired into `llama-server`/`batched-bench`/`parallel`, i.e. NOT the path
-  LocalAI's grpc-server derives from. Adopting it means rewriting LocalAI's serving loop
-  around the new scheduler API.
-- **Broken / restricted.** Crashes out of the box on all current Qwen3 (and any
-  decoupled-head-dim model); fit_params crashed; Phase-1 restrictions enforced at context
-  creation: single CUDA device, full offload only, `n_batch == n_ubatch`, no SWA
-  (gemma3/llama4/etc. unsupported), no CoW / prefix-caching, no
-  `seq_cp`/`seq_keep`/`seq_div`/`seq_add`, no state save/load.
-- **Contested draft.** Unmerged; the author is openly asking maintainers whether the C
-  API is even the right design; maintainers are skeptical of paged for single-node use.
-
-**What P4 should actually target (re-scoped by this data).** The aggregate-throughput
-gap to vLLM on a compute-bound dense model on one GB10 is not addressable by paged KV.
-The durable, real LocalAI wins from paging are the ones our from-scratch P0 already
-implements the machinery for and that #22569 explicitly omits:
-- **on-demand KV sizing** (fit more *diverse* concurrent tenants without per-seq
-  over-reservation), and
-- **automatic cross-tenant prefix sharing** (chained-hash block cache - shared system
-  prompts / RAG preambles), which #22569 defers to a non-existent Phase 2.
-
-Finish our own P4 (CPU gather-read + a CUDA gather-read) against these capacity/
-prefix-sharing objectives - measured as max concurrent *distinct* tenants and KV memory
-saved, not single-model aggregate tok/s. To chase raw aggregate, the levers are lifting
-`LLAMA_MAX_SEQ` and smaller/MoE models in memory-bandwidth-bound regimes - orthogonal to
-paged attention. The ~1-line reshape fix found here (and the GB10 fit_params crash) are
-worth upstreaming to #22569 regardless, but the PR is not our base.
-
-### Reproduction (DGX, `~/llama.cpp-pr22569`)
-```sh
-export PATH=/usr/local/cuda/bin:$PATH
-# contiguous
-./build/bin/llama-batched-bench -m Qwen3-32B-Q4_K_M.gguf -ngl 99 -npp 16 -ntg 128 \
-  -npl 128 -c 20480 -b 2048 -ub 2048        # 256/512/1024 -> n_seq_max must be <= 256
-# paged (needs the src/llama-graph.cpp:2556 reshape fix: hparams.n_embd -> cur->ne[0]*cur->ne[1])
-./build/bin/llama-paged -m Qwen3-32B-Q4_K_M.gguf -kvp --fit off -ngpub 2048 -ncpub 128 \
-  -np 128 -ns 128 -n 128 -b 2048 -ub 2048 -ngl 99   # 512/1024 -> n_seq_max must be <= 256
-```

backend/cpp/llama-cpp/paged/README.md

@@ -1,95 +0,0 @@
-# Paged Attention for llama.cpp (vLLM-parity), CPU-first
-
-A from-scratch port of vLLM V1's paged KV-cache model into the llama.cpp / ggml
-world, built CPU-first and verified incrementally. The host-side block manager is
-a faithful port of vLLM; the compute stays in ggml (no new op — the read path
-gathers blocks with `ggml_get_rows` and feeds the existing attention ops).
-
-Design: `docs/superpowers/specs/2026-06-19-paged-attention-llamacpp-design.md`
-Plan:   `docs/superpowers/plans/2026-06-19-paged-attention-llamacpp.md`
-
-## Status
-
-| Phase | What | State |
-|------|------|-------|
-| P0 | vLLM-parity host block manager (`FreeBlockQueue`, `BlockPool`, `PagedKVManager`, chained-hash prefix cache) | ✅ verified — `make check`, 4/4 suites |
-| P1 | ggml paged write/gather mechanism (`set_rows` by slot_mapping → `get_rows` gather) | ✅ verified — `make ggml-check`, non-contiguous blocks `[2,1,5]` round-trip + isolation |
-| P2 (core) | attention over gathered paged KV matches independent host reference | ✅ verified — max abs err **7.5e-08** |
-| P3 (partial) | capacity & prefix-sharing wins | ✅ measured — `make bench`: **9.2×** more concurrent seqs, **11.3×** less KV memory |
-| **P3 (in-model placement)** | **paged, non-contiguous block KV placement in the real model** | ✅ **Gate 0 PASSED** — Qwen3-0.6B token-identical (`patches/0001-paged-kv-block-placement.patch`) |
-| P4 (in-model compute) | gather-read (`build_attn_paged`, read only a seq's blocks) + win-2 throughput + multi-seq | ⛔ remaining |
-
-The design's central risk — *does paged (non-contiguous) KV produce correct attention?* —
-is **retired at two levels**: (1) at the ggml-op level (P2, 7.5e-08 vs reference) and
-(2) **in a real model** (P3): with KV physically scattered across permuted, non-contiguous
-blocks (cells `0-15, 144-159, 32-47, …`), Qwen3-0.6B greedy generation is **token-for-token
-identical** to the contiguous cache. Reproduce:
-
-```sh
-# from backend/cpp/llama-cpp-fallback-build/llama.cpp (patch applied, CPU build)
-B=build-cpu/bin/llama-simple; M=<Qwen3-0.6B.Q4_K_M.gguf>; P="...long prompt..."
-"$B" -m "$M" -n 40 "$P"                         > base.txt
-LLAMA_KV_PAGED=1 "$B" -m "$M" -n 40 "$P"        > paged.txt
-diff base.txt paged.txt && echo TOKEN-IDENTICAL
-# LLAMA_KV_PAGED_DEBUG=1 prints the permuted physical cells per step
-```
-
-This proves the **storage/placement** layer of paged attention in-model. What remains (P4)
-is the **compute** optimization that yields the throughput win: a gather-read that attends
-only a sequence's own blocks (instead of scanning `[0,n_kv)` with a mask), plus the
-multi-sequence driver to measure tok/s vs concurrency. The patch is single-sequence scope.
-
-## Build & test
-
-```sh
-make check                     # P0 host-manager unit suites (pure C++, no deps)
-make ggml-check GGML_SRC=<llama.cpp>/ggml GGML_BUILD=<ggml-build>   # P1/P2 ggml tests
-make bench                     # P3 capacity + prefix-sharing numbers
-```
-
-`ggml-check` needs a built ggml. To build one CPU-only from a llama.cpp checkout:
-`cmake -S <llama.cpp>/ggml -B /tmp/ggml-build -DGGML_CUDA=OFF -DCMAKE_BUILD_TYPE=Release && cmake --build /tmp/ggml-build -j`
-(if it complains about a missing `ggml.pc.in`, add a minimal pkg-config stub).
-
-## Files
-
-- `paged_kv_manager.{h,cpp}` — the vLLM-parity block manager (no ggml/llama dep).
-- `tests/test_free_block_queue.cpp` — intrusive LRU free list.
-- `tests/test_block_pool.cpp` — alloc/touch/free/evict/cache.
-- `tests/test_paged_kv_manager.cpp` — allocate/block_table/slot_mapping/free.
-- `tests/test_prefix_cache.cpp` — chained block hashing + first-miss cache hit.
-- `tests/test_ggml_paged_rw.cpp` — paged write/gather through real ggml ops.
-- `tests/test_ggml_paged_attn.cpp` — attention over paged KV vs host reference.
-- `paged-bench.cpp` — capacity (win 1) + prefix-sharing (win 3) measurements.
-
-## Remaining work — integration map (for the next session)
-
-Target: a paged read path active behind a flag, producing **token-identical** greedy
-output vs the contiguous cache on a real model (Gate 0), then `paged-bench` win 2.
-
-Exact seams in the vendored llama.cpp (`backend/cpp/llama-cpp-fallback-build/llama.cpp`,
-the pinned build fetches `LLAMA_VERSION=f3e182816421…`):
-
-1. **Memory type** — `src/llama-model.cpp:2070` `create_memory()` constructs `llama_kv_cache`.
-   Add a paged variant (or a flag on the existing cache) implementing `llama_memory_i`
-   (`src/llama-memory.h`), backed by `PagedKVManager`.
-2. **Allocation** — `src/llama-kv-cache.cpp:818` `find_slot()` produces `slot_info.idxs`.
-   Replace the ring-buffer scan with block-aligned allocation from `PagedKVManager`.
-3. **Read path** — `src/llama-kv-cache.cpp:1145/1165` `get_k`/`get_v` return a contiguous
-   `[0,n_kv)` view. For paged, gather the sequence's blocks (`ggml_get_rows`) into scratch.
-   The new branch lives alongside `build_attn` in `src/llama-graph.cpp` (`build_attn_mha`).
-4. **Mask** — `src/llama-graph.cpp` `build_attn_inp_kq_mask` sizes the mask to the gathered
-   length per sequence.
-5. **Gate 0 driver** — `build-cpu/bin/llama-simple` (greedy argmax) on
-   `Qwen3-0.6B.Q4_K_M.gguf`; assert paged output == contiguous output token-for-token.
-
-### Honest caveats (from the maintainer discussion + reading `find_slot`)
-
-- llama.cpp's **unified cache already shares one KV pool** across sequences and already
-  tolerates non-contiguous slots. So win-1 vs *unified* is smaller than vs per-seq
-  reservation (stream mode). The durable LocalAI wins are **on-demand sizing** and
-  **automatic cross-tenant prefix sharing** (P0 implements the block-hash machinery).
-- vLLM's classic `paged_attention_v1/v2` CUDA kernel is **deprecated**; the live path is
-  FlashAttention/FlashInfer over a block table. The port targets that pattern, not the
-  old kernel. Upstream draft PRs #22569 (new `ggml_paged_attn` op) and #17579 (CUDA) are
-  unmerged; maintainers are skeptical for single-user use.

backend/cpp/llama-cpp/paged/UPSTREAM_GGML_ISSUE.md

@@ -1,78 +0,0 @@
-# Upstream ggml issue draft: MXFP4 MoE prefill underutilizes Blackwell (GB10) — ~22 TFLOP/s, ~27× behind vLLM
-
-**Title:** CUDA: MXFP4 MoE prefill runs the Ampere-class warp `mma.sync`, far below Blackwell FP4 peak (GB10 / sm_121)
-
-## Summary
-
-On a GB10 (DGX Spark, sm_121), MXFP4 MoE prefill for Qwen3-Coder-30B-A3B is bottlenecked by
-`mul_mat_q<MXFP4>` (the per-expert grouped MMQ), which runs at only **~22 effective TFLOP/s** — a small
-fraction of the GPU's FP4 capability. Batched prefill plateaus at ~3.65k tok/s (B=32) vs vLLM FP8 ~99k
-on the same box (~27×). The native FP4 block-scaled `mma.sync` path (PR #17906 et al.) *is* engaged — the
-limit is that it's a warp-level MMA kernel, not a tcgen05/CUTLASS-class grouped GEMM.
-
-## Hardware / build
-
-- NVIDIA GB10, compute capability 12.1, 119 GiB unified LPDDR5X.
-- llama.cpp built `-DCMAKE_CUDA_ARCHITECTURES=121` (sm_121a/compute_121a confirmed in cubins).
-- Model: Qwen3-Coder-30B-A3B-Instruct, `MXFP4_MOE` (15.9 GiB, 4.47 BPW).
-
-## Measurements
-
-Single-stream (`llama-bench`, ub2048):
-
-| metric | Q8_0 | MXFP4 | vLLM FP8 |
-|---|---|---|---|
-| prefill pp2048 | ~2200 | 3441 | — |
-| decode tg128 | 62 | 86 | 52 |
-
-Batched (decode-phase aggregate `S_TG`; prefill aggregate `S_PP`):
-
-| B | llama MXFP4 prefill | vLLM FP8 prefill | llama MXFP4 decode | vLLM FP8 decode |
-|---|---|---|---|---|
-| 1 | 1625 | 9644 | 83 | 48 |
-| 8 | 3634 | 33373 | 267 | 312 |
-| 32 | 3651 | 99398 | 551 | 1171 |
-| 64 | 3648 | 151990 | 770 | 2064 |
-
-Decode is competitive (we win at B=1). **Prefill plateaus and is the gap.**
-
-## Profiling (nsys, MXFP4 pp2048 kernel time)
-
-| kernel | % |
-|---|---|
-| `mul_mat_q<(ggml_type)39>` (MXFP4 MoE GEMM) | **37.2** |
-| `mul_mat_q<(ggml_type)8>` (dense/attn, still Q8) | 10.1 |
-| `flash_attn_ext_f16` | 8.8 |
-| `quantize_mmq_mxfp4` (activation quant) | 8.0 |
-
-Only cutlass kernel present is `cutlass_80_tensorop` (Ampere). No tcgen05 / wgmma anywhere.
-
-## What we ruled out (so it's the kernel, not config)
-
-- **ubatch**: saturates at 2048 (pp4096: ub512 2994 → ub2048 3316 → ub8192 3180).
-- **tile width**: `mmq_x` already selects the full 128-wide tile at ub2048 (~128 tokens/expert).
-- **cuBLAS fallback**: `GGML_CUDA_FORCE_CUBLAS` is a no-op (3419 ↔ 3423 t/s) — dequant→cuBLAS-FP16 neither
-  helps nor hurts, i.e. the FP4 MMQ kernel isn't worse than FP16 cuBLAS, both hit a common ceiling.
-- prefill does **not** scale with bigger single prompts (attention O(N²) confounds): pp2048 3295, pp8192
-  1524, pp16384 2051 — so it's the many-sequence batched MoE GEMM, not batch size.
-
-## Proposal
-
-A tcgen05 / CUTLASS-3.x grouped-GEMM path for FP4 (MXFP4 + NVFP4) MoE on sm_120/121:
-- One grouped GEMM over all experts with per-group token offsets (full tiles regardless of tokens/expert),
-  vs today's per-expert MMQ scheduler.
-- Block-scaled `e2m1` operands via tcgen05 tensor-memory MMA (`mma.sync.aligned.kind::mxf4…` is the
-  warp-level form; the collective-mainloop/tcgen05 form is what extracts Blackwell throughput at prefill
-  tile sizes).
-- Fuse activation quantization (`quantize_mmq_mxfp4`, ~8%) into the permute/gather.
-- Optionally extend to dense layers (qkv/o_proj/lm_head) so full-model prefill is FP4/FP8.
-
-This mirrors what vLLM/FlashInfer/TensorRT-LLM do for Blackwell MoE. Happy to test iterations on the GB10.
-
-## Repro
-
-```sh
-llama-quantize qwen3coder-f16.gguf qwen3coder-mxfp4.gguf MXFP4_MOE
-llama-bench -m qwen3coder-mxfp4.gguf -ngl 99 -p 2048 -n 0 -ub 2048
-llama-batched-bench -m qwen3coder-mxfp4.gguf -ngl 99 -c 45056 -b 2048 -ub 2048 -npp 512 -ntg 128 -npl 1,8,32,64
-```

backend/cpp/llama-cpp/paged/VLLM_DECOMPOSITION.md

@@ -1,83 +0,0 @@
-# What makes vLLM fast on GB10 — kernel vs scheduler (code-grounded, measured)
-
-Decisive analysis (vLLM v0.23.0, torch 2.11+cu130, sm_121, model `RedHatAI/Qwen3-32B-NVFP4A16`, source at tag
-`v0.23.0`). **Answer: it's the scheduler, not the kernel.** This closes the kernel track and opens the
-scheduler track.
-
-## The decomposition (measured on the DGX, prefix-cache OFF, unique prompts)
-
-| | vLLM W4A16 Marlin | llama.cpp | verdict |
-|---|---|---|---|
-| **single-stream prefill** | ~800 t/s (~52 TFLOPS) | 718 MMQ / **1153 MXFP4** | **tied; llama.cpp MXFP4 wins** |
-| decode batch-1 | 11.8 t/s | ~similar | bandwidth-bound (≈190/273 GB/s); no kernel helps |
-| **aggregate decode** | 328 (N32) / 569 (N64) / **667 (N128)** | the gap | **~56× multiplier = scheduler** |
-
-vLLM's single-stream Marlin is **not** at the roofline — it's in the same ~4×-under regime as MMQ. The 24k
-headline is entirely the aggregate decode multiplier.
-
-## The kernel vLLM actually runs on sm_121 (W4A16, forced)
-
-Dispatch (vLLM v0.23.0): `compressed_tensors.py:704` (NVFP4 + no input-quant → `W4A4Fp4(use_a16=True)`) →
-`compressed_tensors_w4a4_nvfp4.py:28` → `kernels/linear/__init__.py:894` (`if use_a16: force_kernel =
-MarlinNvFp4LinearKernel`, **unconditional, no cc gate**) → `nvfp4/marlin.py` → `marlin_utils_fp4.py:182`
-`ops.marlin_gemm(b_q_type=float4_e2m1f)`, activations FP16/BF16. csrc: `csrc/quantization/marlin/marlin.cu`
-+ `marlin_template.h` + `marlin.cuh`.
-
-Techniques = **exactly the playbook we proved loses on GB10**: XOR shared swizzle (`marlin_template.h:722
-^ (row%8)`), 4-stage cp.async pipeline (`marlin.cu:396 stages=4`, `cp_async_wait<stages-2>`), ldmatrix+mma,
-FP16/BF16 acts. Native FP4 (`FlashInferB12xNvFp4LinearKernel`) needs `Sm120BlockScaledDenseGemm` cubins absent
-on GB10 → W4A4 hangs → forced W4A16 Marlin fallback. **Nothing to port; vLLM's kernel is occupancy-blocked too.**
-
-## The scheduler (the real multiplier) — what llama.cpp lacks
-
-- **Paged KV cache** (`vllm/v1/core/kv_cache_manager.py`, `block_pool.py`): block KV, no fragmentation → very
-  high concurrent batch. **llama.cpp: NO** (contiguous per-slot KV → fragmentation caps real concurrency).
-- **Chunked prefill** (`config/scheduler.py:84 enable_chunked_prefill=True`, default ON): interleaves prefill
-  chunks with decode so decode batches stay full. **llama.cpp: NO** (a long prefill stalls the decode batch).
-- **Continuous batching** (`v1/core/sched/scheduler.py`): per-step admit/evict. **llama.cpp: YES** (`n_parallel`,
-  rudimentary — we enabled VRAM-scaled slots in #10411).
-
-## Sizing the scheduler gap — MEASURED (llama.cpp aggregate, the surprise)
-
-`llama-batched-bench` Qwen3-32B-Q4_K_M, npp=128 ntg=128, npl scaling (DGX):
-
-| npl | S_PP (agg prefill) | **S_TG (agg decode)** | vLLM decode | llama % of vLLM |
-|---|---|---|---|---|
-| 1 | 628 | 10.2 | 11.8 | 86% |
-| 8 | 773 | 59.8 | - | - |
-| 32 | 763 | **235** | **328** | **72%** |
-| 64 | 761 | **391** | **569** | **69%** |
-| 128 | 762 | **540** | **667** | **81%** |
-
-**The "30x gap" headline is wrong for realistic concurrency.** llama.cpp's continuous batching already
-captures **~70-81% of vLLM's aggregate decode** at npl<=128, with a near-identical multiplier (10.2 -> 540 =
-**53x**, vs vLLM's 56x). And it is still climbing linearly at 128 (not plateaued). Combined with llama.cpp being
-*ahead* single-stream (MXFP4 1153 > vLLM 800), **llama.cpp is already broadly competitive with vLLM on GB10 at
-self-hosted concurrency.**
-
-Two real findings remain:
-1. **Aggregate prefill is flat ~760** regardless of npl - but that is the **GB10 compute roofline** (vLLM single-
-   stream is ~800; neither can prefill faster aggregate, it is compute-bound). So prefill is **not a throughput
-   gap**; chunked prefill is a **latency/TTFT** win (stop a long prefill stalling the decode batch), not a
-   throughput one.
-2. **vLLM's ~24k headline lives at thousands-of-sequences concurrency**, which **paged KV** unlocks (block KV,
-   no fragmentation). llama.cpp's contiguous KV caps how far npl can scale before memory/fragmentation bite. So
-   paged KV is the **high-concurrency (datacenter) lever**, not a moderate-concurrency one.
-
-## Recommendation
-
-**Pivot to the scheduler; treat the GEMM kernel as good-enough / roofline-blocked on GB10.**
-Now that the gap is measured, ROI-ordered:
-1. **Ship the MXFP4-dense win** — 1153 t/s single-stream beats vLLM's 800; a Blackwell dense-quant
-   recommendation (requantize, no kernel work). Already documented in `BLACKWELL_KERNEL_GAPS.md` §6. Cheapest.
-2. **Chunked prefill** — the tractable scheduler win: interleave prefill chunks with decode so a long prompt
-   doesn't stall the decode batch. Payoff is **latency/TTFT under mixed load** (and steadier decode batches),
-   not aggregate prefill throughput (that's GB10-compute-capped at ~760-800 for both engines). A grpc-server
-   scheduler change; no KV-layout rewrite.
-3. **Paged KV** — the **high-concurrency (thousands-of-seqs) lever** that unlocks vLLM's 24k regime. Heavy
-   (block KV manager; contested upstream PR #22569 / vendored `patches/`). Worth it only if datacenter-scale
-   concurrency is a target; at self-hosted concurrency (npl<=128) llama.cpp is already ~75-80% of vLLM.
-
-**Reframed expectation:** llama.cpp on GB10 is NOT 30x behind vLLM. It is ahead single-stream (MXFP4) and
-~70-81% of vLLM aggregate at npl<=128. The genuine differentiator vLLM still has is **scaling to very high
-concurrency via paged KV**. Kernel tracks (W4A16 178 t/s; FP4-MMA) stay **banked** - not the lever.

backend/cpp/llama-cpp/paged/VLLM_THROUGHPUT_GAP.md

@@ -1,59 +0,0 @@
-# Where vLLM beats llama.cpp on a DGX Spark (GB10), and how to close it — keeping quality
-
-The question: "vLLM is faster at the end — what do we improve, while keeping good quality?" Answer: the
-gap is **three independent things**, and the biggest *per-user, quality-preserving* one is **speculative
-decoding**, which llama.cpp already supports.
-
-## Decomposition (measured + researched)
-
-| vLLM advantage | helps single user? | llama.cpp answer | quality cost | status |
-|---|---|---|---|---|
-| **Per-user decode speed** | **yes** | **speculative decoding** (Qwen3 draft / EAGLE3) | **none** (target-verified, lossless) | mature in llama.cpp; **the main lever** |
-| Prefill / TTFT | no (it's first-token latency) | tune FP4-MMA / Marlin W4A16 kernel | none | hard; `BLACKWELL_KERNEL_GAPS.md` |
-| Aggregate throughput @ concurrency | no (per-user = 0) | continuous batching (paged engine) | none | also kernel-bound |
-
-Key measured fact: **single-user decode is already at parity** (Qwen3-32B: llama 10.2 vs vLLM 11.7 t/s) —
-both hit GB10's ~273 GB/s bandwidth wall (~15 t/s ceiling) **without** spec-dec. So vLLM's real per-user
-speed edge is spec-dec, not architecture.
-
-## Why spec-dec is THE lever here (and quality-safe)
-
-- **Lossless:** the 32B target verifies every drafted token (accept/reject) — output distribution is
-  identical to no-drafting. So you keep **Q4_K_M quality** (no lossy MXFP4 needed) *and* get speed.
-- **GB10 is best-case for it:** decode is bandwidth-bound (one ~17 GB weight-read per token) with huge idle
-  compute. Spec-dec verifies K drafted tokens in **one** weight-read → converts the loop to compute-bound,
-  where GB10 has headroom. Realized speedup ≈ mean accepted length.
-- **Measured (others, same model class):** llama.cpp Qwen2.5-32B dense + 0.5B draft = **2.9×** (13→38 t/s);
-  vLLM EAGLE3 on Qwen3-32B = ~1.8–2.5× general, up to ~3× code/structured. **Competitive.**
-- **Regime caveat:** spec-dec gives **~nothing for MoE-A3B** models (only ~3B active → not bandwidth-bound,
-  nothing to amortize). It shines for **dense** 27–32B — the opposite regime. So this lever is *dense-model*
-  specific.
-
-## Qwen3-32B specifics
-
-- **No native MTP head** (MTP is a Qwen3-*Next*/MoE feature). Options: a **same-family draft**
-  (Qwen3-0.6B or **1.7B** — same tokenizer, llama.cpp vocab check passes) or an external **EAGLE3 head**
-  (RedHatAI/AngelSlim Qwen3-32B-eagle3, accept length 2.15–2.49).
-- Draft pick: **lean Qwen3-1.7B** (0.6B had ~60% lower acceptance in AWS's test; on a bandwidth-bound box the
-  32B weight-read dwarfs the draft cost, so maximize acceptance). `--spec-draft-n-max 5–8`.
-
-## Recommended LocalAI actions (quality-preserving, ranked)
-
-1. **Make speculative decoding easy/recommended for dense ≥14B models on Blackwell** — a draft-model field in
-   the model config (`-md` / `--spec-draft-*`), with a suggested Qwen3-1.7B draft for the Qwen3 family. This
-   is the biggest per-user speed win, lossless, available **now** (no kernel). Gallery: ship target+draft pairs.
-2. Kernel work (FP4-MMA tuning / Marlin W4A16) — improves **prefill/TTFT**, separate metric.
-3. Continuous batching (paged engine) — **aggregate** concurrency only; per-user = 0.
-
-## Honesty / status
-
-The research conclusion is solid (sources below). **Our own empirical spec-dec run on the DGX is pending** —
-the box rebooted mid-session and `llama-cli` now hangs at 0% GPU (while `llama-bench` works), plus the network
-is dropping ssh mid-command. Drafts (Qwen3-0.6B/1.7B Q8) are downloaded and the spec-dec flags are confirmed;
-re-run `llama-cli -m Qwen3-32B-Q4_K_M -md Qwen3-1.7B-Q8_0 -ngl 99 -ngld 99 --spec-draft-n-max 8` when the box
-is stable to confirm the ~2× locally. The conclusion does not depend on it (it's measured-reproducible by
-others on this exact model class), but we should bank our own number.
-
-Sources: llama.cpp Discussion #10466 (Qwen2.5-32B+0.5B = 2.9×), #16578 (DGX Spark), DandinPower/llama.cpp_bench
-(32B = 10.7 t/s, bandwidth-bound); vLLM MTP docs + Red Hat EAGLE3 article (lossless, up to 2.5×); AWS spec-dec
-blog (Qwen3-32B+1.7B up to 3×, 0.6B ~60% lower accept); RedHatAI/AngelSlim Qwen3-32B-eagle3 heads.

backend/cpp/llama-cpp/paged/W4A16_MARLIN_KERNEL_PLAN.md

@@ -1,176 +0,0 @@
-# W4A16 Marlin-style GEMM for ggml-cuda on Blackwell (sm_120/121) — implementation plan
-
-> **STOPPED (2026-06-21): the kernel is NOT the lever — validated by a code-grounded vLLM analysis.**
-> Measured on the DGX: vLLM's single-stream W4A16 prefill on GB10 = **~800 t/s (~52 TFLOPS), statistically TIED
-> with llama.cpp MMQ (718/47)** — and vLLM uses the *exact* XOR-swizzle + 4-stage cp.async Marlin we proved
-> collapses GB10 occupancy (vLLM even warns at load that Marlin "may degrade performance for compute-heavy
-> workloads"). There is no kernel trick to port. Moreover llama.cpp's **MXFP4 path (1153 t/s) already BEATS
-> vLLM single-stream (800)** — vLLM has no FP4 cubins on sm_121 and falls back to slower W4A16 Marlin, so
-> llama.cpp is *ahead* on the kernel. **vLLM's entire 24k headline is the aggregate decode multiplier (~56×)
-> from paged KV + chunked prefill + continuous batching — a SCHEDULER win.** llama.cpp lacks paged KV +
-> chunked prefill. **Effort pivots to the scheduler** (see the paged-attention work). This kernel work is
-> banked + resumable (178 t/s, P0/P1/P2/P3/P3b committed) but is not the throughput lever on GB10. Detail:
-> `VLLM_DECOMPOSITION.md`.
-
-The committed multi-week kernel. Goal: get 4-bit-weight dense matmul to the GB10 **BF16 ceiling (~213
-TFLOP/s ≈ ~3,300 t/s prefill on Qwen3-32B)**, ~4.3× over today's 765. This is the *match-vLLM* path; vLLM's
-own GB10 dense throughput runs on W4A16 Marlin (its FP4 path is broken on sm_121).
-
-## Why a custom kernel (validated, not assumed)
-
-On GB10 (sm_121), measured: **both** llama-MMQ (int8, Ampere-tuned) **and** cuBLAS-FP16 sit at ~46 TFLOP/s
-(~21% of peak). cuBLAS falls back to an Ampere `cutlass_80_tensorop` kernel (CUDA-13 has no sm_121 GEMM for
-these shapes); rebuilt with `-DGGML_CUDA_FORCE_CUBLAS=ON` it's *slower* than MMQ (690 vs 750). **No library
-path reaches the ceiling on consumer Blackwell** — a hand-tuned sm_120a kernel is required. `mmapeak` measures
-the 213 BF16 peak as reachable, and vLLM's Marlin hits it, so the ceiling is real; the work is reaching it.
-
-## What Marlin does (the design we mirror)
-
-Weights stored 4-bit, **dequantized in-register/shared-mem** in-flight; GEMM math on **FP16/BF16 tensor
-cores** (`mma.sync m16n8k16`). Speed comes from: `cp.async` global→shared with a **multi-stage double-buffered
-pipeline**, **offline weight reshuffle** into the MMA-friendly layout, activations kept resident in registers,
-and **Stream-K** partitioning. Sources: IST-DASLab/marlin, arXiv 2408.11743, vLLM machete (Hopper successor).
-
-## Phases (each ends with: numerical parity vs MMQ + a prefill benchmark)
-
-### P0 — Harness + baseline — DONE
-- **Correctness gate (GREEN):** `test-backend-ops test -o MUL_MAT -b CUDA0` → **1103/1103 passed** (CUDA vs CPU
-  reference, covers Q4_0/Q4_K at the real FFN shapes m=4096,k=14336,n=1..512). This is *the* parity check the
-  W4A16 kernel must keep green at every phase — it tests the CUDA MUL_MAT path the kernel will hook. The
-  `not supported` lines are `type_b=f16` combos (irrelevant; prefill uses f32 activations).
-- **Perf baseline:** `llama-bench` dense Q4_K prefill = **~750 t/s (pp512 718 / pp2048 750) ≈ 46 TFLOP/s ≈ 21%
-  of the 213 BF16 ceiling**. The kernel must beat this toward ~3,300. (`test-backend-ops perf -o MUL_MAT` gives
-  per-shape GFLOPS too; build it once with the harness.)
-- **Op-level baseline (the canonical kernel target), `test-backend-ops perf -o MUL_MAT`, m=4096 k=14336 (FFN):**
-  | n (tokens) | q4_0 | q4_K | regime |
-  |---|---|---|---|
-  | 1 | 817 GFLOPS | 761 GFLOPS | decode / mat-vec (memory-bound) |
-  | 8 | 5.77 TFLOPS | 4.11 TFLOPS | small-batch |
-  | **512** | **49.5 TFLOPS** | **47.1 TFLOPS** | **prefill GEMM — ~22% of the 213 ceiling** |
-
-  So the prefill GEMM target: lift q4_K n=512 from **47 → toward ~213 TFLOPS** (~4.5×). This per-shape number
-  is cleaner than end-to-end for kernel iteration.
-- **Harness script:** `~/p0harness.sh` on the DGX (build test-backend-ops + correctness + perf). Reusable each
-  phase: `test-backend-ops test -o MUL_MAT -b CUDA0` must stay 1103/1103; the q4_K n=512 perf must climb from 47.
-- test-backend-ops needed `-DLLAMA_BUILD_TESTS=ON`; now built in `~/llama.cpp-pr24423/build`.
-
-### P1 — Dispatch seam (no behavior change) — DONE
-- `marlin-w4a16.{cuh,cu}` + a gated hook in `ggml_cuda_mul_mat` (dense, non-ids path), behind
-  `GGML_CUDA_W4A16` + sm_120/121 (`cc >= GGML_CUDA_CC_BLACKWELL`) + type∈{Q4_0,Q4_K} + f32 activations.
-  Returns false → falls back to MMQ. Source + apply instructions: `kernel/w4a16/` (`HOOK.md`).
-- **Verified on GB10:** clean build; `test-backend-ops MUL_MAT` = **1103/1103** (byte-identical default);
-  `llama-bench` dense Q4 pp512 unchanged (717.77 default / 718.26 with flag); `GGML_CUDA_W4A16=1` reaches the
-  seam (stderr `[w4a16] ... P1 seam - using MMQ`) and falls back. The empty frame P2/P3 fills.
-
-### P2 — Correctness-first kernel (slow OK) — DONE
-- **Kernel:** `marlin-w4a16.cu` replaces the P1 TODO with a real W4A16 GEMM. In-kernel dequant Q4→BF16 into
-  shared mem, `mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32` via ggml's `mma.cuh` tile abstractions
-  (`tile<16,8,nv_bfloat162>` A, `tile<8,8,nv_bfloat162>` B, `tile<16,8,float>` C), F32 accumulate, F32 write.
-  One warp per 16(M)x8(N) output tile, K looped in steps of 16. Both src0 (weights, row m) and src1 (acts,
-  row n) are row-major `[row][k]`, so A and B load symmetrically via `load_generic`; the mma does the dot over k.
-- **Types handled:** Q4_0 and Q4_K. Q4_0 dequant `w=d*(q-8)` inline; Q4_K via the superblock decode mirrored
-  from `convert.cu` (`get_scale_min_k4`, 8x32 sub-blocks, `d*q-m`).
-- **Shape classes handled:** contiguous 2D GEMM (the prefill path), `ne2==ne3==1`, f32 activations, K%16==0
-  (always true: Q4_0 K%32, Q4_K K%256). **Falls back to MMQ (returns false)** for batched (bs!=[1,1]),
-  broadcast (nr!=[1,1]), permuted / non-contiguous (per!=[0,1,2,3]), and any non-f32 activation (e.g. f16) -
-  keeps the gate green. M / N boundaries are zero-padded in-kernel (handles M not %16, N not %8).
-- **Parity (the gate):** `GGML_CUDA_W4A16=1 test-backend-ops test -o MUL_MAT -b CUDA0` = **1103/1103 passed**
-  (the Q4_0/Q4_K f32 contiguous shapes run the kernel and match the CPU reference; batched/permuted/f16 fall
-  back). Default (flag-unset) build still **1103/1103** (byte-identical, seam returns false).
-- **Model sanity / P2 perf:** `GGML_CUDA_W4A16=1 llama-bench -m Qwen3-32B-Q4_K_M.gguf -ngl 99 -p 512 -n 16
-  -ub 2048` runs clean: **pp512 = 31.75 t/s**, tg16 = 6.28 t/s. Slow as expected (naive 1-warp/tile, weights
-  re-dequantized per n-tile, no pipeline) - this is the correctness checkpoint; P3 brings the speedup. The real
-  Q4_K model matmul path engages the kernel without error.
-
-### P3 — The Marlin pipeline (the speedup) — STEP 1 + SKEW-PAD/TILING LANDED; PREPACK + PIPELINE + STREAM-K DEFERRED
-Goal: `cp.async` double/triple-buffered global->shared; offline weight reshuffle (a one-time repack of the Q4
-tensor into the mma+pipeline layout); register-resident activation tiles; Stream-K split for the prefill M.
-Target: >=150 TFLOP/s (>=~2,300 t/s), then ~213. **MMQ baseline to beat: 47.1 TFLOPS (q4_K n=512) / pp512 718.**
-
-**Kernel structure now (committed P3b):** block-tiled multi-warp GEMM with a CONFLICT-FREE shared feed via skew
-padding. `blockDim=(32, WM*WN)` so `threadIdx.x` is the warp lane (required by `mma.cuh` get_i/get_j) and
-`threadIdx.y` is the warp index; the original 1-warp P2 launch put 128 threads on `threadIdx.x` and exploded
-`get_j` into an out-of-bounds shared read (found via compute-sanitizer). `WM*WN` warps compute a
-`BM(=WM*FM*16) x BN(=WN*FN*8)` output tile; each warp owns an `FM x FN` grid of m16n8k16 mma fragments
-accumulated in F32. Per k-step (16-deep): all warps cooperatively dequant the `BM x 16` Q4 weight strip + load
-the `BN x 16` f32->bf16 activation strip into shared, one `__syncthreads`, then `ldmatrix.x4` (A) / `ldmatrix.x2`
-(B) fragments + `FM*FN` mmas. The shared rows hold 8 bf162 of data but are stored at a PADDED stride of 12 bf162
-(`W4A16_SPAD`): ldmatrix's per-lane address is `row*stride`, and the natural stride 8 (a divisor of the
-32-bank / 128-byte cycle) collides rows 0,4,8,12 into a 2-way bank conflict; skewing to 12 (4-byte aligned, so
-ldmatrix's 16-byte alignment holds) makes `{r*12 mod 32}` hit 8 distinct bank-quads for r in 0..7, so both
-halves of ldmatrix are conflict-free at only +50% on the small staged tile (~12 KB at the shipping tile).
-Shipping config `WM=4,WN=4,FM=2,FN=4` -> `BM=128, BN=128`, 16 warps, 8 m16n8 C-tiles per warp (keeping
-register pressure low is what lets BN grow without an occupancy cliff). M/N tails zero-padded in-kernel; still
-gated to contiguous 2D Q4_0/Q4_K f32 prefill, else falls back to MMQ.
-
-**Per-step results (q4_K n=512 via `test-backend-ops perf`; pp512/pp2048 via llama-bench Qwen3-32B-Q4_K_M):**
-
-| step | q4_K n=512 | q4_0 n=512 | pp512 | pp2048 | vs MMQ 47 / 718 | notes |
-|---|---|---|---|---|---|---|
-| P2 (1 warp/tile) | ~2 TFLOPS | - | 31.75 | - | 0.04x | correctness checkpoint |
-| Step 1: block tiling (load_generic, BM64/4w) | 6.63 (cold) | 7.53 | 119 | 123 | 0.14x | original committed kernel |
-| P3b-1: skew-pad ldmatrix + BM128/8w | 8.50 (cold) | 10.56 | 148.5 | 153.9 | 0.18x | +28% q4_K, +40% q4_0 over step 1 |
-| **P3b-2: + BN128/16w (current)** | **9.92 (cold)** | **11.68** | **177.6** | **185.0** | **0.21x** | +17% q4_K, +20% pp512 over P3b-1 (+49% pp512 over step 1) |
-
-Parity gate **1103/1103** at every step, flag set and unset (byte-identical when unset). All P3b numbers above
-are from thermally-bracketed cold A/B sessions (committed measured immediately before AND after each candidate,
-identical both times -> the deltas are real, not thermal). P3b-1 cold A/B: 6.63/7.53 vs 8.52/10.49. P3b-2 cold
-A/B: BN64/8w 10.56/8.50 then 10.51/8.45 (bracket) vs BN128/16w 11.68/9.92.
-
-**What landed / what was tried (honest):**
-- **P3b - LANDED (committed).** Two combined changes lift the prior committed kernel: (1) **skew-pad
-  conflict-free ldmatrix** (shared row stride 8->12 bf162; makes `ldmatrix.x4`/`.x2` bank-conflict-free at near
-  zero occupancy cost) and (2) **bigger tile / more warps** (`BM=128, BN=64`, 8 warps). Cold A/B: q4_K
-  6.63->8.52 (+28%), q4_0 7.53->10.49 (+40%), pp512 119->148.5 (+25%). **Still ~5.5x under MMQ (47) per-op and
-  ~4.8x under pp512 718 - does NOT beat MMQ.** This is forward progress, not the finish line.
-- **The XOR-swizzle-FIRST plan was tested and is WRONG for this GPU - documented so it is not re-tried.** A
-  wide-row (BK=64, 128-byte rows) XOR swizzle `seg ^ (row&7)` IS conflict-free, but the 16 KB shared it needs
-  collapsed occupancy and dropped q4_K n=512 to **2.84 TFLOPS** (worse than the unswizzled 6.63) - the same
-  occupancy cliff P3 hit with a 32 KB pipeline. The conflict-free feed must be bought WITHOUT widening shared:
-  skew padding (above) does exactly that (6 KB), which is why it is the committed form. Lesson: on GB10 occupancy
-  dominates bank-conflict latency; never trade occupancy for a conflict-free layout.
-- **Conflict-free feed alone did NOT beat the unswizzled kernel - the limiter moved.** At the SAME BM64/4w tile,
-  skew-pad ldmatrix (6.70) ~= load_generic (6.63): removing bank conflicts bought ~nothing. The win came only
-  when the tile grew (BM128/8w). A 5-config tile sweep then split the two quant types:
-  - **q4_0 SCALES with warps/tiles** (7.7 -> 10.5 -> **15.8 TFLOPS at BM128/16w**): feed/global-traffic bound,
-    helped by cutting redundant activation re-reads (more BM = fewer M-blocks each re-reading the act column).
-  - **q4_K is largely DEQUANT-COMPUTE bound** (the BM64/16w tile gives q4_0=15.8 but q4_K=6.8 - they diverge
-    hard). This **refines P3's "within 12%" finding**: that held only in the low-throughput memory-bound regime;
-    once the feed is unblocked, q4_K's per-element 6-bit superblock decode (`get_scale_min_k4` + superblock
-    indexing, redone every k-step AND re-done by every N-block) becomes the wall. BM256 regressed both (too few
-    blocks / register pressure).
-- **Growing BN partly relieves the q4_K dequant wall (P3b-2).** Because every N-block re-decodes the same
-  weight strip, halving the N-block count (BN 64->128) halves that redundant q4_K decode - but only when BN is
-  spread across MORE WARPS (16w, 8 C-tiles/warp), not more fragments-per-warp: the FN=8 / FM=4 variants (16
-  C-tiles/warp) regressed to ~6.6 on register pressure, while WM=4,WN=4,FM=2,FN=4 (16w, 8 tiles/warp) lifted
-  q4_K 8.5->9.9 and q4_0 10.6->11.7 cold. BN=256 was no better and costs more shared. **BN128/16w is the
-  shipping tile.**
-- **Next blocker (the remaining q4_K unlock) = offline prepack.** BN growth only divides the redundant decode by
-  the N-block count; it cannot remove the per-k-step decode itself. The full fix is the **one-time offline
-  repack** - decode the Q4 tensor ONCE into a cached device buffer keyed off the tensor data pointer, in a layout
-  with the scale/min pre-applied (store reshuffled 4-bit + per-subblock bf16 d,m, ~1.25x the q4 size, NOT a full
-  bf16 blow-up which would be ~4x), so the in-kernel path becomes a cheap `q*d - m` with coalesced loads. Then
-  `cp.async` multi-stage (sized to NOT widen shared past the occupancy cliff) and **Stream-K** over M. These
-  remain the multi-week core; **prepack is the highest-value next step for q4_K specifically** (it should let
-  q4_K join q4_0 on the feed-bound scaling curve instead of plateauing at ~10).
-- **Methodology note (unchanged):** the box thermally throttles under sustained perf+bench runs (identical code
-  ~8.8 cold vs ~6.6 hot earlier), so only same-session A/Bs are trustworthy. The P3b deltas above were taken in
-  one bracketed cold session for exactly this reason.
-
-### P4 — Tune
-- Tile (mmq_x/y analogues), warps, pipeline depth, occupancy. We have nsys (throughput) but **not ncu** on the
-  DGX — tuning is empirical (sweep configs, measure t/s). Note ncu would need sudo/driver perms we lack.
-
-### P5 — Enable
-- Default on for sm_120/121 + Q4_0/Q4_K dense when parity holds + faster; keep the flag as an escape hatch.
-  Ship as a LocalAI llama.cpp patch (the patches/ series) and/or upstream (ggml has no Marlin-equivalent —
-  issue #1519 — so it's net-new upstream value; float it with maintainers first).
-
-## Risks / notes
-- **Multi-week, expert-CUDA, DGX-only** (GB10 is the only sm_121). The session's network flakiness +
-  `llama-cli` hang make `llama-bench`/`test-backend-ops` the reliable verification tools (both work).
-- Quantization correctness: Q4_K's superblock structure (256-elem, 6-bit scales) is more complex to dequant
-  in-kernel than Q4_0; consider landing Q4_0 first, then Q4_K.
-- **Beat-path follow-on:** the FP4-MMA path (`mul_mat_q<MXFP4>`, ~5% of FP4 peak) tuned/fixed on sm_121 reaches
-  ~6,600 (2× BF16). Separate track; this W4A16 kernel is the match-path foundation.
-- Reuse ggml's `mma.cuh` tile abstractions (MMQ already uses them) rather than raw PTX where possible.

backend/cpp/llama-cpp/paged/kernel/w4a16/HOOK.md

@@ -1,31 +0,0 @@
-# W4A16 seam — how to apply to a llama.cpp / ggml-cuda checkout
-
-Two source files + two one-line edits to `ggml/src/ggml-cuda/ggml-cuda.cu`. The build picks up the
-new `.cu` via the existing `file(GLOB)` after a `cmake -S . -B build` reconfigure (no CMakeLists edit).
-
-## Files (copy into `ggml/src/ggml-cuda/`)
-- `marlin-w4a16.cuh`
-- `marlin-w4a16.cu`
-
-## Edit `ggml/src/ggml-cuda/ggml-cuda.cu`
-
-1. **Include** — after the existing `#include "ggml-cuda/fp4-grouped-moe.cuh"` (sibling-header style):
-   ```cpp
-   #include "ggml-cuda/marlin-w4a16.cuh"
-   ```
-
-2. **Dispatch hook** — immediately before the dense dispatch chain, i.e. before
-   `if (!split && use_mul_mat_vec_f) {` in `ggml_cuda_mul_mat(...)` (after `const int cc = ...`):
-   ```cpp
-   if (!split && ggml_cuda_w4a16_mul_mat(ctx, src0, src1, dst)) { return; }
-   ```
-
-## Verify (P1 acceptance — met)
-- `cmake --build build --target test-backend-ops llama-bench` → builds clean.
-- `test-backend-ops test -o MUL_MAT -b CUDA0` → **1103/1103** (byte-identical default).
-- `llama-bench` dense Q4 pp512 → unchanged (~718, MMQ).
-- `GGML_CUDA_W4A16=1 llama-bench` → unchanged + stderr `[w4a16] ... P1 seam - using MMQ` (seam reached,
-  gating passes on sm_121, falls back).
-
-The kernel body (P2 correctness → P3 Marlin pipeline) replaces the `TODO(P2/P3)` block in `marlin-w4a16.cu`
-and returns `true` once parity holds.

backend/cpp/llama-cpp/paged/kernel/w4a16/SUBAGENT_BRIEFS.md

@@ -1,66 +0,0 @@
-# W4A16 kernel - subagent dispatch briefs (P3, P4, P5)
-
-**Dispatch strategy.** Each phase = one fresh **Opus-4.8** subagent handed a complete zero-context brief.
-Phases are **sequential** (P3 needs P2's correct kernel; P4 needs P3's pipeline; P5 needs P4's tuned kernel),
-so dispatch phase N+1 only after phase N's commit lands, and before dispatching, splice phase N's *actual*
-deliverable (final kernel shape, configs, fallback set) into the next brief. P2's brief (already dispatched)
-is the template; reuse the COMMON section below verbatim in every dispatch.
-
----
-
-## COMMON (paste into every phase brief)
-
-- **Kernel dev is on the remote DGX** (GB10, sm_121): `ssh -o ConnectTimeout=25 -o ServerAliveInterval=10 -o ServerAliveCountMax=10 dgx.casa '<cmd>'`. Network is FLAKY (re-poll on drop; nohup jobs survive). `llama-cli` HANGS - never use it. Only `llama-bench` + `test-backend-ops` work.
-- Checkout `~/llama.cpp-pr24423`, build `~/llama.cpp-pr24423/build` (sm_121, `-DLLAMA_BUILD_TESTS=ON`). Kernel file `ggml/src/ggml-cuda/marlin-w4a16.cu`. Build auto-GLOBs it; no CMakeLists edits. Hook already in `ggml-cuda.cu`, gated behind env `GGML_CUDA_W4A16`.
-- Dense test model: `~/bench/q3-32b-gguf/Qwen3-32B-Q4_K_M.gguf`.
-- **Builds run detached + poll** (never blocking foreground): write a `~/pN.sh` that builds `--target test-backend-ops llama-bench`, echoes `RC=$?`, runs the gate, echoes `PN_DONE`; `nohup` it; poll `for i in $(seq 1 90); do grep -q PN_DONE ~/pN.out && break; sleep 20; done; tail ~/pN.out`.
-- **GPU hygiene:** check `docker ps | grep local-ai` + `nvidia-smi`; `docker stop` a running localai worker if present (authorized); never pkill native procs; never start model servers.
-- **Parity gate (must stay green every step):** `GGML_CUDA_W4A16=1 CUDA_VISIBLE_DEVICES=0 ./build/bin/test-backend-ops test -o MUL_MAT -b CUDA0` = **1103/1103**; and flag-unset stays 1103/1103 (byte-identical). A wrong result is worse than a fallback - return false for any shape you can't do correctly.
-- **Perf measurement:** `test-backend-ops perf -o MUL_MAT -b CUDA0` (per-shape GFLOPS; the canonical target is q4_K m=4096 k=14336 **n=512**, baseline **47.1 TFLOPS**, ceiling ~213) + `llama-bench -m <model> -ngl 99 -p 512,2048 -n 0 -ub 2048` (baseline pp512 ~718).
-- **LocalAI repo (commit here; you do NOT inherit cwd - `cd` explicitly):** `/home/mudler/_git/LocalAI/.claude/worktrees/feat+paged-attention`. Plan: `backend/cpp/llama-cpp/paged/W4A16_MARLIN_KERNEL_PLAN.md`. Source mirror: `backend/cpp/llama-cpp/paged/kernel/w4a16/`. After a phase passes: fetch the final `marlin-w4a16.cu` from the DGX (`ssh ... 'cat ...'`), overwrite the mirror, update the plan (mark the phase DONE with numbers), `git commit -s` (DCO sign-off; user is Ettore Di Giacinto <mudler@localai.io>). **No `Co-Authored-By`. No em-dashes anywhere. Trailer `Assisted-by: Claude:opus-4.8 [Claude Code]`. Do NOT push.**
-- Final message = the result (gate ?/1103, the perf delta, blockers + resolutions, commit hash). A precise partial result beats a vague success claim.
-
----
-
-## P3 brief - the Marlin pipeline (the speedup)
-
-**Goal.** Take P2's correct-but-slow kernel from ~47 toward ~150+ TFLOPS (then ~213) on the q4_K n=512 prefill GEMM, **without ever breaking parity**. This is the Marlin design: the math is the same BF16 mma; the speed comes from feeding the tensor cores without stalling.
-
-**Implement, incrementally (re-run the parity gate after each):**
-1. **`cp.async` multi-stage pipeline** - double/triple-buffer global->shared loads of both the Q4 weight tiles and the activation tiles so dequant+mma on stage k overlaps the load of stage k+1. (Study `mma.cuh` + how `mmq.cu`/`mmf.cu` stage shared memory; ggml already uses `cp.async`/`__pipeline_*`.)
-2. **Offline weight reshuffle** - repack the Q4 weights once into the mma+pipeline-friendly layout (Marlin's interleave) so loads are coalesced and the mma fragment maps directly. Do this as a load-time transform of src0 (a new prepacked buffer keyed off the tensor) - NOT per-call. Document where the repack lives + its memory cost.
-3. **Register-resident activation tiles + Stream-K** split of the M dimension across blocks for the prefill (large-M) case so all SMs stay busy.
-
-**Acceptance.** Parity gate stays **1103/1103** at every commit; `test-backend-ops perf` q4_K n=512 climbs materially above 47 TFLOPS (target >=150) and `llama-bench` pp512 climbs above ~718. Report the TFLOPS + t/s after each of the 3 steps so the contribution of each is visible. If a step regresses parity, revert it and report why.
-
-**Reference.** IST-DASLab/marlin (github), arXiv 2408.11743, vLLM machete. Mirror `mmf.cu`'s BF16 GEMM structure; Marlin = that + Q4 dequant-on-load + the pipeline/reshuffle.
-
-**Splice before dispatch:** P2's final kernel structure (tile sizes, which types/shapes it handles vs falls back, helper functions it defined).
-
----
-
-## P4 brief - tune to the ceiling
-
-**Goal.** Drive the P3 kernel as close to the ~213 TFLOPS ceiling as empirical tuning allows. **No `ncu` on this box** (no driver perms) - tune by throughput: `test-backend-ops perf` + `llama-bench` + `nsys` (throughput only).
-
-**Do.** Parametrize the kernel (template params / constants) over: tile M/N/K, warps per block, pipeline depth (stages), and occupancy (regs, shared-mem budget). Sweep systematically (a script that rebuilds + benches each config, logs q4_K n=512 TFLOPS + pp512/pp2048 t/s), pick the best, hard-set it (with a short comment on the sweep). Check both prefill shapes (n=512 and n=2048) and confirm decode (n=1) didn't regress (it should still route to mat-vec, not this kernel - verify the gating).
-
-**Acceptance.** Best config maximizes q4_K n=512 TFLOPS (stretch ~150-213) with parity **1103/1103** intact; the sweep table (config -> TFLOPS/t-s) is recorded in the plan's P4 section. Report the chosen config + the final pp512/pp2048 t/s vs the 718/750 baseline and vs vLLM's ~3300 single-stream target.
-
-**Splice before dispatch:** P3's pipeline structure + the perf it reached + which knobs are already fixed vs free.
-
----
-
-## P5 brief - enable + package + (maybe) upstream
-
-**Goal.** Make W4A16 the default dense-Q4 path on Blackwell and ship it through LocalAI.
-
-**Do.**
-1. **Flip the gate:** default-ON for sm_120/121 + Q4_0/Q4_K dense when faster, keep an opt-out env (e.g. `GGML_CUDA_W4A16=0`) as an escape hatch. The existing return-false-on-unhandled-shape path is the correctness safety net; keep it. Verify the default (no env) build now runs W4A16 for dense Q4, gate green, faster than the old MMQ baseline.
-2. **Package as a LocalAI llama.cpp patch:** produce `backend/cpp/llama-cpp/paged/patches/kernel/0002-w4a16-marlin.patch` (the new files + the `ggml-cuda.cu` hook + the gate flip) that applies cleanly to the pinned llama.cpp, mirroring the existing `patches/kernel/0001-fp4-grouped-moe-scaffold.patch`. Confirm LocalAI's `make backends/llama-cpp` build path can consume it (read `.agents/llama-cpp-backend.md` + the build memory: `make -C backend/cpp/llama-cpp clean` before rebuilds).
-3. **Docs:** update `BLACKWELL_KERNEL_GAPS.md` + the plan with the shipped result; add a short note to the LocalAI docs if there's a Blackwell/performance page.
-4. **Upstream decision (do NOT open without surfacing first):** ggml has no Marlin-equivalent (issue #1519) so this is net-new upstream value. Draft (do not submit) an upstream PR description + note the sm_121 build-flag caveats; report it for the user to decide.
-
-**Acceptance.** Default Blackwell build uses W4A16 for dense Q4, parity 1103/1103, measurably faster than MMQ; the patch applies + the LocalAI llama-cpp backend builds with it (verify or, if the full backend build is too heavy, document the exact build command + that the patch applies cleanly). Report the end-to-end LocalAI dense-Q4 prefill number vs the start-of-project 765 t/s.
-
-**Splice before dispatch:** P4's final kernel + config + the measured ceiling reached; the exact enable condition decided.

backend/cpp/llama-cpp/paged/kernel/w4a16/marlin-w4a16.cu

@@ -1,258 +0,0 @@
-#include "marlin-w4a16.cuh"
-#include "mma.cuh"
-
-#include <cstdio>
-#include <cstdlib>
-#include <cuda_bf16.h>
-
-// W4A16 Marlin-style GEMM.
-//
-// In-kernel dequantize Q4 weights -> BF16, multiply against BF16-converted F32
-// activations using mma.sync m16n8k16 BF16 tensor-core ops, accumulate in F32,
-// write F32 output. Handles only the contiguous 2D GEMM (prefill) case for
-// Q4_0 / Q4_K; everything else returns false and falls back to MMQ.
-//
-// ggml MUL_MAT convention: dst[m,n] = sum_k src0[k,m] * src1[k,n].
-//   src0 (weights): ne0=K (contiguous), ne1=M  -> row m is K contiguous quants.
-//   src1 (acts,f32): ne0=K (contiguous), ne1=N -> row n is K contiguous floats.
-//   dst  (f32):      ne0=M (contiguous), ne1=N -> element (m,n) at m + n*M.
-// Both operands are row-major [row][k]; m16n8k16 computes C[m,n] += sum_k A[m,k]*B[n,k].
-//
-// Thread layout: blockDim = (32, WM*WN). threadIdx.x is the warp lane (0..31,
-// required by mma.cuh get_i/get_j), threadIdx.y is the warp index.
-//
-// P3b step 1 - conflict-free shared layout via SKEW PADDING:
-//  - WM*WN warps compute a BM(=WM*FM*16) x BN(=WN*FN*8) output tile; each warp
-//    owns an FM x FN grid of m16n8k16 mma fragments accumulated in F32.
-//  - Per 16-deep k-step the warps cooperatively dequant the BM x 16 Q4 weight
-//    strip + load the BN x 16 f32->bf16 activation strip into shared, then feed
-//    the tensor cores with ldmatrix.x4 (A) / ldmatrix.x2 (B).
-//  - The shared rows are PADDED to SPAD(=12) bf162 instead of the natural 8.
-//    ldmatrix's per-lane address is row*stride; with the natural stride 8 (a
-//    divisor of the 32-bank / 128-byte cycle) rows 0,4,8,12 collide -> 2-way
-//    bank conflict on every fragment load (this is why P3 measured a plain
-//    ldmatrix swap as neutral). Skewing the stride to 12 (4-byte aligned, so
-//    ldmatrix's 16-byte alignment holds) makes {r*12 mod 32} hit 8 distinct
-//    bank-quads for r in 0..7, so both halves of ldmatrix.x4 and ldmatrix.x2 are
-//    conflict-free. The pad costs only +50% on the small (~4 KB) staged tile, so
-//    unlike a 128-byte-row XOR swizzle it does NOT collapse occupancy on GB10
-//    (a wide-row swizzle pushed shared to 16 KB and dropped this to ~2.8 TFLOPS).
-//
-// Dead-ends already proven (do not re-try): a double-buffered KSTAGE=64 cp.async
-// pipeline collapsed occupancy (32 KB shared -> 2.7 TFLOPS); a plain ldmatrix on
-// the UNpadded layout was neutral (bank conflicts); a wide-row (BK=64) XOR swizzle
-// was conflict-free but occupancy-starved (16 KB shared -> 2.8 TFLOPS). Skew
-// padding gets the conflict-free feed at near-zero occupancy cost.
-
-using namespace ggml_cuda_mma;
-
-typedef tile<16, 8, nv_bfloat162> tile_A; // 16(M) x 16(K)
-typedef tile< 8, 8, nv_bfloat162> tile_B; //  8(N) x 16(K)
-typedef tile<16, 8, float>        tile_C; // 16(M) x  8(N)
-
-// bf162 columns actually live per shared row (16 k-values = 8 bf162) ...
-#define W4A16_KP   8
-// ... padded to this stride to bank-skew the ldmatrix row addresses.
-#define W4A16_SPAD 12
-
-static bool w4a16_enabled() {
-    static const bool en = (std::getenv("GGML_CUDA_W4A16") != nullptr);
-    return en;
-}
-
-// 6-bit packed scale/min decode for Q4_K (mirrors convert.cu get_scale_min_k4).
-static __device__ __forceinline__ void w4a16_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
-    if (j < 4) {
-        d = q[j] & 63; m = q[j + 4] & 63;
-    } else {
-        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
-    }
-}
-
-// Dequantize a single Q4_0 weight at column k of a row.
-static __device__ __forceinline__ float w4a16_dq_q4_0(const char * row, int k) {
-    const block_q4_0 * blk = (const block_q4_0 *) row + (k / QK4_0);
-    const int j = k % QK4_0;
-    const float d = __half2float(blk->d);
-    const int q = (j < QK4_0/2) ? (blk->qs[j] & 0xF) : (blk->qs[j - QK4_0/2] >> 4);
-    return (q - 8) * d;
-}
-
-// Dequantize a single Q4_K weight at column k of a row.
-static __device__ __forceinline__ float w4a16_dq_q4_K(const char * row, int k) {
-    const block_q4_K * blk = (const block_q4_K *) row + (k / QK_K);
-    const int e = k % QK_K;
-    const int il     = e / 64;        // 0..3
-    const int within = e % 64;
-    const int half   = within / 32;   // 0..1
-    const int pos    = within % 32;
-    const int ir     = pos / 4;       // 0..7
-    const int l      = pos % 4;       // 0..3
-    const int is     = 2*il + half;
-    const float dall = __low2half (blk->dm);
-    const float dmin = __high2half(blk->dm);
-    uint8_t sc, mn;
-    w4a16_scale_min_k4(is, blk->scales, sc, mn);
-    const float d = dall * sc;
-    const float m = dmin * mn;
-    const uint8_t qb = blk->qs[32*il + 4*ir + l];
-    const int q = (half == 0) ? (qb & 0xF) : (qb >> 4);
-    return d * q - m;
-}
-
-template <bool IS_Q4_K, int WM, int WN, int FM, int FN>
-static __global__ void __launch_bounds__(WM*WN*32, 1)
-w4a16_gemm_kernel(
-        const char * __restrict__ src0,
-        const char * __restrict__ src1,
-        float      * __restrict__ dst,
-        const int M, const int N, const int K,
-        const int64_t nb01, const int64_t nb11, const int64_t dst_ne0) {
-    constexpr int KP   = W4A16_KP;      // 8 bf162 = 16 k per row
-    constexpr int SPAD = W4A16_SPAD;    // padded row stride (bank skew)
-    constexpr int BM  = WM*FM*16;
-    constexpr int BN  = WN*FN*8;
-    constexpr int NTH = WM*WN*32;
-
-    const int m0 = blockIdx.x * BM;
-    const int n0 = blockIdx.y * BN;
-
-    const int warp_id = threadIdx.y;        // 0 .. WM*WN-1
-    const int warp_n  = warp_id % WN;
-    const int warp_m  = warp_id / WN;
-    const int tid     = threadIdx.y*32 + threadIdx.x;
-
-    __shared__ nv_bfloat162 sW[BM*SPAD]; // [m][kpair], padded row stride SPAD
-    __shared__ nv_bfloat162 sB[BN*SPAD]; // [n][kpair], padded row stride SPAD
-
-    tile_C C[FM][FN]; // zero-initialized accumulators
-
-    for (int k0 = 0; k0 < K; k0 += 16) {
-        // Dequantize the BM x 16 weight strip once; reused across the block's BN span.
-        #pragma unroll
-        for (int idx = tid; idx < BM*KP; idx += NTH) {
-            const int m  = idx / KP;
-            const int kk = idx % KP;
-            const int k  = k0 + 2*kk;
-            float w0 = 0.0f, w1 = 0.0f;
-            if (m0 + m < M) {
-                const char * row = src0 + (int64_t)(m0 + m) * nb01;
-                if (IS_Q4_K) { w0 = w4a16_dq_q4_K(row, k); w1 = w4a16_dq_q4_K(row, k + 1); }
-                else         { w0 = w4a16_dq_q4_0(row, k); w1 = w4a16_dq_q4_0(row, k + 1); }
-            }
-            sW[m*SPAD + kk] = __floats2bfloat162_rn(w0, w1);
-        }
-        // Load the BN x 16 activation strip (f32 -> bf16).
-        #pragma unroll
-        for (int idx = tid; idx < BN*KP; idx += NTH) {
-            const int n  = idx / KP;
-            const int kk = idx % KP;
-            const int k  = k0 + 2*kk;
-            float a0 = 0.0f, a1 = 0.0f;
-            if (n0 + n < N) {
-                const float * arow = (const float *)(src1 + (int64_t)(n0 + n) * nb11);
-                a0 = arow[k]; a1 = arow[k + 1];
-            }
-            sB[n*SPAD + kk] = __floats2bfloat162_rn(a0, a1);
-        }
-        __syncthreads();
-
-        tile_A Af[FM];
-        tile_B Bf[FN];
-        #pragma unroll
-        for (int fm = 0; fm < FM; ++fm) {
-            const int mrow = (warp_m*FM + fm) * 16;
-            load_ldmatrix(Af[fm], sW + mrow*SPAD, SPAD);
-        }
-        #pragma unroll
-        for (int fn = 0; fn < FN; ++fn) {
-            const int ncol = (warp_n*FN + fn) * 8;
-            load_ldmatrix(Bf[fn], sB + ncol*SPAD, SPAD);
-        }
-        #pragma unroll
-        for (int fm = 0; fm < FM; ++fm) {
-            #pragma unroll
-            for (int fn = 0; fn < FN; ++fn) {
-                mma(C[fm][fn], Af[fm], Bf[fn]);
-            }
-        }
-        __syncthreads();
-    }
-
-    #pragma unroll
-    for (int fm = 0; fm < FM; ++fm) {
-        #pragma unroll
-        for (int fn = 0; fn < FN; ++fn) {
-            const int mbase = m0 + (warp_m*FM + fm) * 16;
-            const int nbase = n0 + (warp_n*FN + fn) * 8;
-            #pragma unroll
-            for (int l = 0; l < tile_C::ne; ++l) {
-                const int m = mbase + tile_C::get_i(l);
-                const int n = nbase + tile_C::get_j(l);
-                if (m < M && n < N) {
-                    dst[(int64_t)n * dst_ne0 + m] = C[fm][fn].x[l];
-                }
-            }
-        }
-    }
-}
-
-bool ggml_cuda_w4a16_mul_mat(
-        ggml_backend_cuda_context & ctx,
-        const ggml_tensor * src0,
-        const ggml_tensor * src1,
-        ggml_tensor       * dst) {
-    if (!w4a16_enabled()) {
-        return false;
-    }
-    if (src0->type != GGML_TYPE_Q4_0 && src0->type != GGML_TYPE_Q4_K) {
-        return false;
-    }
-    if (src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
-        return false;
-    }
-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    if (!GGML_CUDA_CC_IS_NVIDIA(cc) || cc < GGML_CUDA_CC_BLACKWELL) {
-        return false; // consumer Blackwell (sm_120/121) only
-    }
-
-    if (src0->ne[2] != 1 || src0->ne[3] != 1 ||
-        src1->ne[2] != 1 || src1->ne[3] != 1 ||
-        dst->ne[2]  != 1 || dst->ne[3]  != 1) {
-        return false;
-    }
-    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
-        return false;
-    }
-
-    const int64_t K = src0->ne[0];
-    const int64_t M = src0->ne[1];
-    const int64_t N = src1->ne[1];
-    if (src1->ne[0] != K || dst->ne[0] != M || dst->ne[1] != N) {
-        return false;
-    }
-    if (K % 16 != 0) {
-        return false;
-    }
-
-    cudaStream_t stream = ctx.stream();
-
-    // Block tile config: WM*WN warps compute BM(=WM*FM*16) x BN(=WN*FN*8).
-    constexpr int WM = 4, WN = 4, FM = 2, FN = 4; // BM=128, BN=128, 16 warps
-    constexpr int BM = WM*FM*16;
-    constexpr int BN = WN*FN*8;
-    const dim3 grid((unsigned)((M + BM - 1) / BM), (unsigned)((N + BN - 1) / BN), 1);
-    const dim3 block(32, WM*WN, 1);
-
-    if (src0->type == GGML_TYPE_Q4_K) {
-        w4a16_gemm_kernel<true, WM, WN, FM, FN><<<grid, block, 0, stream>>>(
-            (const char *) src0->data, (const char *) src1->data, (float *) dst->data,
-            (int) M, (int) N, (int) K, src0->nb[1], src1->nb[1], dst->ne[0]);
-    } else {
-        w4a16_gemm_kernel<false, WM, WN, FM, FN><<<grid, block, 0, stream>>>(
-            (const char *) src0->data, (const char *) src1->data, (float *) dst->data,
-            (int) M, (int) N, (int) K, src0->nb[1], src1->nb[1], dst->ne[0]);
-    }
-    return true;
-}

backend/cpp/llama-cpp/paged/kernel/w4a16/marlin-w4a16.cuh

@@ -1,14 +0,0 @@
-#pragma once
-
-#include "common.cuh"
-
-// W4A16 Marlin-style BF16 GEMM for NVIDIA Blackwell consumer GPUs (sm_120/121).
-// Dense (non-MoE) 4-bit-weight matmul run on BF16 tensor cores, the path that
-// reaches the GB10 BF16 ceiling where MMQ (int8, Ampere-tuned) and cuBLAS (sm_80
-// fallback) both plateau at ~22% of it. Returns true if it handled the op; false
-// to fall back to MMQ. Gated behind GGML_CUDA_W4A16 until correct + faster.
-bool ggml_cuda_w4a16_mul_mat(
-        ggml_backend_cuda_context & ctx,
-        const ggml_tensor * src0,   // 4-bit weights (Q4_0/Q4_K)
-        const ggml_tensor * src1,   // F32 activations
-        ggml_tensor       * dst);   // F32 output

backend/cpp/llama-cpp/paged/paged-bench.cpp

@@ -1,129 +0,0 @@
-// paged-bench: quantify the multi-tenant wins of paged KV allocation that are
-// properties of the host-side block model (vLLM-parity), independent of the
-// in-model compute path.
-//
-//   Win 1 (capacity):       on-demand block allocation vs contiguous per-seq
-//                           reservation, under a fixed KV block budget.
-//   Win 3 (prefix sharing): automatic cross-tenant prefix dedup via block
-//                           hashing.
-//
-// Win 2 (throughput) is intentionally NOT here: it requires the paged read
-// path wired into llama-graph.cpp (Gate 0). Measuring it at this layer would
-// be dishonest, so it is reported as pending.
-
-#include "paged_kv_manager.h"
-
-#include <cstdio>
-#include <vector>
-#include <numeric>
-
-using namespace paged;
-
-// A deterministic LCG so sequence lengths vary without Math.random-style nondeterminism.
-struct Lcg {
-    uint64_t s;
-    explicit Lcg(uint64_t seed) : s(seed) {}
-    uint32_t next() { s = s * 6364136223846793005ULL + 1442695040888963407ULL; return (uint32_t)(s >> 33); }
-    int range(int lo, int hi) { return lo + (int)(next() % (uint32_t)(hi - lo + 1)); }
-};
-
-static size_t cdiv(size_t a, size_t b) { return (a + b - 1) / b; }
-
-int main() {
-    const int block_size = 16;
-    const int n_ctx      = 2048;   // max context a sequence could use
-    const int num_blocks = 512;    // fixed KV budget: 512 blocks * 16 = 8192 cells
-
-    printf("paged-bench  (block_size=%d, n_ctx=%d, budget=%d blocks = %d cells)\n\n",
-           block_size, n_ctx, num_blocks, num_blocks * block_size);
-
-    // ---------------------------------------------------------------------
-    // WIN 1: concurrency capacity. Sequences have realistic, VARYING lengths
-    // (most short, a few long) - the regime where reserving n_ctx per seq
-    // wastes the most. Count how many fit under the same block budget.
-    // ---------------------------------------------------------------------
-    {
-        Lcg rng(12345);
-        const int blocks_per_ctx = (int) cdiv(n_ctx, block_size); // contiguous reserves this per seq
-
-        // Contiguous (stream-style) reservation: every seq reserves n_ctx worth.
-        int contiguous_fit = num_blocks / blocks_per_ctx;
-
-        // Paged on-demand: draw real lengths until the pool is exhausted.
-        PagedKVManager m(num_blocks, block_size, /*enable_caching=*/false);
-        int paged_fit = 0;
-        long total_tokens = 0;
-        for (int seq = 0; ; ++seq) {
-            // 80% short (8-128 tok), 20% long (up to n_ctx)
-            int len = (rng.range(0, 99) < 80) ? rng.range(8, 128) : rng.range(128, n_ctx);
-            if (!m.allocate(seq, (size_t) len)) break;
-            paged_fit++;
-            total_tokens += len;
-        }
-
-        printf("WIN 1  concurrency capacity @ %d-block budget\n", num_blocks);
-        printf("  contiguous (reserve n_ctx/seq): %d sequences\n", contiguous_fit);
-        printf("  paged (on-demand blocks):       %d sequences  (avg %ld tok/seq)\n",
-               paged_fit, paged_fit ? total_tokens / paged_fit : 0);
-        printf("  --> paged fits %.1fx more concurrent sequences\n\n",
-               contiguous_fit ? (double) paged_fit / contiguous_fit : 0.0);
-    }
-
-    // ---------------------------------------------------------------------
-    // WIN 3: cross-tenant prefix sharing. N tenants share a long system
-    // prompt / RAG context, then diverge. Compare physical blocks consumed
-    // with prefix caching on vs off.
-    // ---------------------------------------------------------------------
-    {
-        const int n_tenants    = 32;
-        const int shared_len   = 1024;  // shared system prompt (64 blocks)
-        const int distinct_len = 64;    // per-tenant suffix (4 blocks)
-
-        // Shared prefix token ids (identical across tenants -> identical block hashes).
-        std::vector<int> shared(shared_len);
-        for (int i = 0; i < shared_len; ++i) shared[i] = 1000 + i;
-
-        // --- prefix caching OFF: every tenant pays for the whole prefix ---
-        long blocks_off = 0;
-        {
-            PagedKVManager m(num_blocks * 8, block_size, /*enable_caching=*/false);
-            for (int t = 0; t < n_tenants; ++t) {
-                m.allocate(t, (size_t) (shared_len + distinct_len));
-                blocks_off += m.block_table(t).size();
-            }
-        }
-
-        // --- prefix caching ON: shared blocks are deduped to one physical copy ---
-        long blocks_on = 0;
-        {
-            PagedKVManager m(num_blocks * 8, block_size, /*enable_caching=*/true);
-            // tenant 0 fills + caches the shared prefix
-            auto h = m.compute_block_hashes(shared);
-            m.allocate(0, (size_t) (shared_len + distinct_len));
-            m.cache_blocks(0, h, (size_t) shared_len);
-            long physical = m.block_table(0).size();
-            // tenants 1..N-1 hit the cached prefix; only their distinct suffix is new
-            for (int t = 1; t < n_tenants; ++t) {
-                size_t cached_tokens = m.get_computed_blocks(h); // shared blocks reused
-                size_t new_tokens = (shared_len - cached_tokens) + distinct_len;
-                m.allocate(t, (size_t) (shared_len + distinct_len));
-                // physically new blocks = only what wasn't already resident
-                physical += (long) cdiv(new_tokens, block_size);
-            }
-            blocks_on = physical;
-        }
-
-        printf("WIN 3  cross-tenant prefix sharing (%d tenants, %d-tok shared prefix)\n",
-               n_tenants, shared_len);
-        printf("  prefix-cache OFF: %ld physical blocks\n", blocks_off);
-        printf("  prefix-cache ON:  %ld physical blocks\n", blocks_on);
-        printf("  --> %.1fx less KV memory for the shared workload\n\n",
-               blocks_on ? (double) blocks_off / blocks_on : 0.0);
-    }
-
-    printf("WIN 2  aggregate throughput under load: PENDING\n");
-    printf("  Requires the paged gather-read path wired into llama-graph.cpp\n");
-    printf("  (Gate 0) to measure tok/s vs concurrency. Not measurable at the\n");
-    printf("  allocation layer; not reported here to avoid overclaiming.\n");
-    return 0;
-}

backend/cpp/llama-cpp/paged/paged-loadgen.cpp

@@ -1,169 +0,0 @@
-// paged-loadgen: a dynamic-load benchmark for paged KV that actually exercises the
-// regime where paging wins - variable prompt lengths, variable generation lengths,
-// staggered (continuous) arrival, and a shared system prefix. The stock
-// examples/paged/paged.cpp adds all requests up front with a fixed n_predict from a
-// 20-prompt pool, so it never creates KV-memory pressure or fragmentation and
-// therefore never shows a paged advantage (see PAGED_KV_HIGH_CONCURRENCY.md).
-//
-// Build: drop into PR #22569's examples/paged/ and add to its CMakeLists.txt next to
-// llama-paged (it uses the same llama_paged_scheduler_* API). Run on the TARGET GPU
-// (e.g. 2xH200) where bandwidth lets decode scale to thousands of sequences and KV
-// memory becomes the binding constraint - that is where paged KV pays off and where
-// this harness produces a meaningful number. On a low-bandwidth box (GB10) throughput
-// plateaus long before memory binds, so the win is not observable there regardless.
-//
-// Metrics reported:
-//   - goodput (decode tokens/s aggregate) under the dynamic load
-//   - peak concurrent in-flight sequences actually sustained
-//   - paged peak KV bytes used  vs  the contiguous reservation a unified cache needs
-//     (n_seq_peak * max_ctx), i.e. the capacity ratio = the headroom paging unlocks
-//
-// The capacity ratio is the load-bearing number for the buy decision: it is how many
-// more concurrent tenants a fixed HBM budget serves with paging than without.
-
-#include "common.h"
-#include "llama.h"
-
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <random>
-#include <string>
-#include <vector>
-
-// ---- workload knobs (env-overridable so the harness is sweepable without rebuilds) ----
-static int env_int(const char * k, int dflt) { const char * v = getenv(k); return v ? atoi(v) : dflt; }
-
-struct workload_cfg {
-    int    total_requests  = env_int("LG_TOTAL",    2000); // total requests to serve
-    int    target_inflight = env_int("LG_INFLIGHT",  256); // continuous-batching concurrency target
-    int    prefix_tokens   = env_int("LG_PREFIX",    512); // shared system-prompt prefix (prefix-cache target)
-    int    suffix_min      = env_int("LG_SUFMIN",     16); // per-request unique prompt suffix range
-    int    suffix_max      = env_int("LG_SUFMAX",    768);
-    int    gen_short       = env_int("LG_GENSHORT",   32); // bimodal generation: most short...
-    int    gen_long        = env_int("LG_GENLONG",  1024); // ...some long (the over-reservation driver)
-    int    gen_long_pct    = env_int("LG_LONGPCT",    15); // % of requests that are long
-    int    block_size      = env_int("LG_BLOCK",      16); // must match -kvbls
-    unsigned seed          = (unsigned) env_int("LG_SEED", 1234);
-};
-
-// Per-request plan drawn from the workload distribution.
-struct req_plan { int prompt_len; int gen_len; };
-
-int main(int argc, char ** argv) {
-    common_params params;
-    params.n_predict = -1; // per-request, controlled by the plan below
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PAGED)) {
-        fprintf(stderr, "usage: %s -m <model> -kvp --fit off -ngpub N -ncpub M -ngl 99\n", argv[0]);
-        return 1;
-    }
-    params.kv_paged = true;
-
-    common_init_result init = common_init_from_params(params);
-    llama_model *   model = init.model.get();
-    llama_context * ctx   = init.context.get();
-    if (!model || !ctx) { fprintf(stderr, "load failed\n"); return 1; }
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    workload_cfg cfg;
-    std::mt19937 rng(cfg.seed);
-    std::uniform_int_distribution<int> suf(cfg.suffix_min, cfg.suffix_max);
-    std::uniform_int_distribution<int> pct(1, 100);
-
-    // KV bytes/token = 2(K,V) * n_layers * n_head_kv * head_dim * sizeof(f16). Confirmed
-    // against llama-kv-cache-paged.cpp (block_bytes formula). Used for the capacity ratio.
-    const int n_layers   = llama_model_n_layer(model);
-    const int n_head_kv  = llama_model_n_head_kv(model);
-    const int head_dim   = llama_model_n_embd(model) / llama_model_n_head(model);
-    const size_t kv_bytes_per_token = (size_t)2 * n_layers * n_head_kv * head_dim * sizeof(uint16_t);
-
-    // A long shared system prefix that every request reuses (the prefix-cache target).
-    std::vector<llama_token> prefix = common_tokenize(ctx, std::string(cfg.prefix_tokens, 'x'), true);
-
-    // Pre-draw all request plans so paged peak usage and the contiguous reservation are
-    // computed from the SAME workload.
-    std::vector<req_plan> plans(cfg.total_requests);
-    int max_ctx = 0;
-    for (auto & p : plans) {
-        p.prompt_len = cfg.prefix_tokens + suf(rng);
-        p.gen_len    = (pct(rng) <= cfg.gen_long_pct) ? cfg.gen_long : cfg.gen_short;
-        max_ctx      = std::max(max_ctx, p.prompt_len + p.gen_len);
-    }
-
-    llama_paged_scheduler * sched = llama_paged_scheduler_init(ctx);
-    if (!sched) { fprintf(stderr, "scheduler init failed\n"); return 1; }
-
-    // ---- continuous-arrival loop: keep ~target_inflight requests live at all times ----
-    int    next_req = 0, done = 0, inflight = 0, peak_inflight = 0;
-    long   total_decoded = 0;
-    size_t peak_kv_bytes_paged = 0;   // sum over live seqs of ceil(used/block)*block*kv_bytes
-    size_t live_used_tokens = 0;      // running sum of actual KV tokens held by live seqs
-
-    auto admit = [&](int rid) {
-        const req_plan & p = plans[rid];
-        std::vector<llama_token> toks = prefix; // shared prefix...
-        std::vector<llama_token> suff = common_tokenize(ctx, std::string(p.prompt_len - cfg.prefix_tokens, 'y'), false);
-        toks.insert(toks.end(), suff.begin(), suff.end()); // ...+ unique suffix
-        if (llama_paged_scheduler_add_request(sched, toks.data(), toks.size(), rid)) {
-            inflight++; peak_inflight = std::max(peak_inflight, inflight);
-            live_used_tokens += p.prompt_len;
-        }
-    };
-
-    const int64_t t0 = ggml_time_us();
-    for (int i = 0; i < cfg.target_inflight && next_req < cfg.total_requests; ++i) admit(next_req++);
-
-    llama_batch batch = {};
-    std::vector<llama_token> sampled; std::vector<int8_t> stop_flags;
-
-    while (done < cfg.total_requests) {
-        if (!llama_paged_scheduler_prepare_batch(sched, &batch)) break;
-        const llama_paged_batch_info * info = llama_paged_scheduler_get_batch_info(sched);
-        sampled.assign(info->n_seq, 0); stop_flags.assign(info->n_seq, 0);
-
-        // (decode is done inside the scheduler/update path in PR #22569; greedy here)
-        for (int i = 0; i < info->n_seq; ++i) {
-            const int rid = info->seq_ids[i];
-            llama_paged_seq_state st{};
-            llama_paged_scheduler_get_seq_state(sched, rid, &st);
-            // greedy argmax from the i-th row of logits
-            const float * lg = llama_get_logits_ith(ctx, i);
-            int best = 0; float bv = lg[0];
-            for (int t = 1; t < llama_vocab_n_tokens(vocab); ++t) if (lg[t] > bv) { bv = lg[t]; best = t; }
-            sampled[i] = best;
-            const bool stop = llama_vocab_is_eog(vocab, best) || st.n_decoded + 1 >= plans[rid].gen_len;
-            stop_flags[i] = stop ? 1 : 0;
-            if (!stop) { total_decoded++; live_used_tokens++; }
-            if (stop) {
-                done++; inflight--;
-                live_used_tokens -= (plans[rid].prompt_len + st.n_decoded);
-                if (next_req < cfg.total_requests) admit(next_req++); // continuous arrival
-            }
-        }
-        // paged peak KV: blocks are allocated per live seq = ceil(used/block); approximate
-        // current paged footprint from live_used_tokens rounded up per the block size.
-        const size_t paged_now = (size_t)std::ceil((double)live_used_tokens / cfg.block_size)
-                                 * cfg.block_size * kv_bytes_per_token;
-        peak_kv_bytes_paged = std::max(peak_kv_bytes_paged, paged_now);
-
-        llama_paged_scheduler_update(sched, &batch, sampled.data(), stop_flags.data());
-    }
-    const double secs = (ggml_time_us() - t0) / 1e6;
-
-    // Contiguous unified-KV reservation needed to serve the SAME peak concurrency without
-    // mid-generation eviction: every live slot must be backed for the worst-case context.
-    const size_t contig_reserve = (size_t)peak_inflight * max_ctx * kv_bytes_per_token;
-
-    printf("\n==== paged-loadgen ====\n");
-    printf("requests served      : %d  (target inflight %d, peak inflight %d)\n", done, cfg.target_inflight, peak_inflight);
-    printf("goodput (decode)     : %.1f tok/s   (%ld tokens / %.2f s)\n", total_decoded / secs, total_decoded, secs);
-    printf("kv bytes / token     : %zu (n_layer=%d n_head_kv=%d head_dim=%d f16)\n", kv_bytes_per_token, n_layers, n_head_kv, head_dim);
-    printf("paged peak KV        : %.2f GiB (allocated on demand)\n", peak_kv_bytes_paged / 1073741824.0);
-    printf("contiguous reserve   : %.2f GiB (peak_inflight * max_ctx %d)\n", contig_reserve / 1073741824.0, max_ctx);
-    printf("CAPACITY RATIO       : %.2fx  <- tenants-per-HBM paging unlocks\n",
-           peak_kv_bytes_paged ? (double)contig_reserve / peak_kv_bytes_paged : 0.0);
-    printf("  (plus cross-request prefix sharing of the %d-token shared prefix, not counted above)\n", cfg.prefix_tokens);
-
-    llama_paged_scheduler_free(sched);
-    return 0;
-}

backend/cpp/llama-cpp/paged/paged_kv_manager.cpp

@@ -1,296 +0,0 @@
-#include "paged_kv_manager.h"
-#include <cassert>
-#include <stdexcept>
-
-namespace paged {
-
-// ---------------------------------------------------------------------------
-// FreeBlockQueue  (port of kv_cache_utils.py FreeKVCacheBlockQueue)
-// ---------------------------------------------------------------------------
-
-FreeBlockQueue::FreeBlockQueue(const std::vector<KVCacheBlock*>& blocks) {
-    num_free_blocks = blocks.size();
-    for (size_t i = 0; i < blocks.size(); ++i) {
-        if (i > 0)                  blocks[i]->prev_free = blocks[i - 1];
-        if (i + 1 < blocks.size())  blocks[i]->next_free = blocks[i + 1];
-    }
-    if (!blocks.empty()) {
-        fake_head.next_free = blocks.front();
-        blocks.front()->prev_free = &fake_head;
-        fake_tail.prev_free = blocks.back();
-        blocks.back()->next_free = &fake_tail;
-    } else {
-        fake_head.next_free = &fake_tail;
-        fake_tail.prev_free = &fake_head;
-    }
-}
-
-KVCacheBlock* FreeBlockQueue::popleft() {
-    KVCacheBlock* first = fake_head.next_free;
-    if (first == &fake_tail || first == nullptr) {
-        assert(num_free_blocks == 0);
-        throw std::runtime_error("No free blocks available");
-    }
-    fake_head.next_free = first->next_free;
-    first->next_free->prev_free = &fake_head;
-    first->prev_free = first->next_free = nullptr;
-    num_free_blocks--;
-    return first;
-}
-
-std::vector<KVCacheBlock*> FreeBlockQueue::popleft_n(size_t n) {
-    std::vector<KVCacheBlock*> ret;
-    if (n == 0) return ret;
-    assert(num_free_blocks >= n);
-    num_free_blocks -= n;
-    KVCacheBlock* curr = fake_head.next_free;
-    ret.reserve(n);
-    for (size_t i = 0; i < n; ++i) {
-        assert(curr != nullptr);
-        ret.push_back(curr);
-        KVCacheBlock* last = curr;
-        curr = curr->next_free;
-        last->prev_free = last->next_free = nullptr;
-    }
-    if (curr != nullptr) {
-        fake_head.next_free = curr;
-        curr->prev_free = &fake_head;
-    }
-    return ret;
-}
-
-void FreeBlockQueue::remove(KVCacheBlock* block) {
-    if (!block->prev_free || !block->next_free)
-        throw std::runtime_error("remove() called on an invalid block");
-    block->prev_free->next_free = block->next_free;
-    block->next_free->prev_free = block->prev_free;
-    block->prev_free = block->next_free = nullptr;
-    num_free_blocks--;
-}
-
-void FreeBlockQueue::append(KVCacheBlock* block) {
-    KVCacheBlock* last = fake_tail.prev_free;
-    last->next_free = block;
-    block->prev_free = last;
-    block->next_free = &fake_tail;
-    fake_tail.prev_free = block;
-    num_free_blocks++;
-}
-
-void FreeBlockQueue::append_n(const std::vector<KVCacheBlock*>& blocks) {
-    if (blocks.empty()) return;
-    KVCacheBlock* last = fake_tail.prev_free;
-    for (KVCacheBlock* b : blocks) {
-        b->prev_free = last;
-        last->next_free = b;
-        last = b;
-    }
-    last->next_free = &fake_tail;
-    fake_tail.prev_free = last;
-    num_free_blocks += blocks.size();
-}
-
-void FreeBlockQueue::prepend_n(const std::vector<KVCacheBlock*>& blocks) {
-    if (blocks.empty()) return;
-    KVCacheBlock* first = fake_head.next_free;
-    KVCacheBlock* prev = &fake_head;
-    for (KVCacheBlock* b : blocks) {
-        b->prev_free = prev;
-        prev->next_free = b;
-        prev = b;
-    }
-    prev->next_free = first;
-    first->prev_free = prev;
-    num_free_blocks += blocks.size();
-}
-
-std::vector<KVCacheBlock*> FreeBlockQueue::get_all_free_blocks() const {
-    std::vector<KVCacheBlock*> ret;
-    const KVCacheBlock* curr = fake_head.next_free;
-    while (curr && curr->next_free != nullptr) {
-        ret.push_back(const_cast<KVCacheBlock*>(curr));
-        curr = curr->next_free;
-    }
-    return ret;
-}
-
-// ---------------------------------------------------------------------------
-// BlockPool  (port of block_pool.py)
-// ---------------------------------------------------------------------------
-
-static std::vector<KVCacheBlock*> make_ptrs(std::vector<KVCacheBlock>& v) {
-    std::vector<KVCacheBlock*> p;
-    p.reserve(v.size());
-    for (auto& b : v) p.push_back(&b);
-    return p;
-}
-
-static std::vector<KVCacheBlock> make_block_vec(int32_t num_blocks) {
-    std::vector<KVCacheBlock> v;
-    v.reserve(num_blocks);
-    for (int32_t i = 0; i < num_blocks; ++i) v.emplace_back(i);
-    return v;
-}
-
-BlockPool::BlockPool(int32_t num_blocks, bool enable_caching)
-    : enable_caching_(enable_caching),
-      blocks_(make_block_vec(num_blocks)),
-      ptrs_(make_ptrs(blocks_)),
-      free_queue_(ptrs_) {
-    // vLLM reserves block_id 0 as the null block (never cached).
-    null_block = free_queue_.popleft();
-    null_block->is_null = true;
-}
-
-bool BlockPool::maybe_evict_cached_block(KVCacheBlock* block) {
-    if (!block->has_hash) return false;
-    auto it = cached_block_hash_to_block_.find(block->block_hash);
-    if (it == cached_block_hash_to_block_.end() || it->second != block) return false;
-    cached_block_hash_to_block_.erase(it);
-    block->reset_hash();
-    return true;
-}
-
-std::vector<KVCacheBlock*> BlockPool::get_new_blocks(size_t n) {
-    if (n > get_num_free_blocks())
-        throw std::runtime_error("Cannot get free blocks from pool");
-    auto ret = free_queue_.popleft_n(n);
-    for (KVCacheBlock* b : ret) {
-        if (enable_caching_) maybe_evict_cached_block(b);
-        assert(b->ref_cnt == 0);
-        b->ref_cnt += 1;
-    }
-    return ret;
-}
-
-KVCacheBlock* BlockPool::get_cached_block(uint64_t block_hash) {
-    auto it = cached_block_hash_to_block_.find(block_hash);
-    return it == cached_block_hash_to_block_.end() ? nullptr : it->second;
-}
-
-void BlockPool::touch(const std::vector<KVCacheBlock*>& blocks) {
-    for (KVCacheBlock* b : blocks) {
-        // ref_cnt==0 means the block is a free-list eviction candidate; pull it out.
-        if (b->ref_cnt == 0 && !b->is_null) free_queue_.remove(b);
-        b->ref_cnt += 1;
-    }
-}
-
-void BlockPool::free_blocks(const std::vector<KVCacheBlock*>& ordered_blocks) {
-    std::vector<KVCacheBlock*> without_hash, with_hash;
-    for (KVCacheBlock* b : ordered_blocks) {
-        if (b->is_null) continue;
-        b->ref_cnt -= 1;
-        if (b->ref_cnt == 0) (b->has_hash ? with_hash : without_hash).push_back(b);
-    }
-    free_queue_.prepend_n(without_hash); // un-hashed: evicted first (front)
-    free_queue_.append_n(with_hash);     // hashed: kept warm (tail)
-}
-
-void BlockPool::cache_full_blocks(const std::vector<KVCacheBlock*>& req_blocks,
-                                  size_t num_cached_blocks, size_t num_full_blocks,
-                                  const std::vector<uint64_t>& block_hashes) {
-    for (size_t i = num_cached_blocks; i < num_full_blocks; ++i) {
-        KVCacheBlock* blk = req_blocks[i];
-        if (blk->has_hash) continue;
-        blk->has_hash = true;
-        blk->block_hash = block_hashes[i];
-        cached_block_hash_to_block_[blk->block_hash] = blk;
-    }
-}
-
-// ---------------------------------------------------------------------------
-// PagedKVManager  (port of SingleTypeKVCacheManager / FullAttentionManager)
-// ---------------------------------------------------------------------------
-
-static inline size_t cdiv(size_t a, size_t b) { return (a + b - 1) / b; }
-
-PagedKVManager::PagedKVManager(int32_t num_blocks, int block_size, bool enable_caching)
-    : block_size_(block_size), pool_(num_blocks, enable_caching) {}
-
-bool PagedKVManager::allocate(int seq_id, size_t total_tokens) {
-    auto& req = req_to_blocks_[seq_id];
-    size_t need = cdiv(total_tokens, block_size_);
-    if (need <= req.size()) return true;
-    size_t add = need - req.size();
-    if (add > pool_.get_num_free_blocks()) return false; // OOM
-    auto nb = pool_.get_new_blocks(add);
-    req.insert(req.end(), nb.begin(), nb.end());
-    return true;
-}
-
-std::vector<int32_t> PagedKVManager::block_table(int seq_id) const {
-    std::vector<int32_t> bt;
-    auto it = req_to_blocks_.find(seq_id);
-    if (it == req_to_blocks_.end()) return bt;
-    bt.reserve(it->second.size());
-    for (KVCacheBlock* b : it->second) bt.push_back(b->block_id);
-    return bt;
-}
-
-int64_t PagedKVManager::slot(int seq_id, int pos) const {
-    const auto& req = req_to_blocks_.at(seq_id);
-    int32_t phys = req[pos / block_size_]->block_id;
-    return (int64_t)phys * block_size_ + (pos % block_size_);
-}
-
-std::vector<int64_t> PagedKVManager::slot_mapping(int seq_id, const std::vector<int>& positions) const {
-    std::vector<int64_t> sm;
-    sm.reserve(positions.size());
-    for (int p : positions) sm.push_back(slot(seq_id, p));
-    return sm;
-}
-
-void PagedKVManager::free(int seq_id) {
-    auto it = req_to_blocks_.find(seq_id);
-    if (it == req_to_blocks_.end()) return;
-    // Free in reverse so the tail of the block chain is evicted first (vLLM order).
-    std::vector<KVCacheBlock*> ordered(it->second.rbegin(), it->second.rend());
-    pool_.free_blocks(ordered);
-    req_to_blocks_.erase(it);
-}
-
-// FNV-1a chained block hash. Deterministic and prefix-sensitive; folds the parent
-// hash into the seed so each block hash transitively encodes its whole prefix
-// (behavioral parity with vLLM hash_block_tokens chaining; vLLM uses sha256 bytes).
-uint64_t PagedKVManager::hash_block(uint64_t parent_hash, const std::vector<int>& token_ids) {
-    uint64_t h = 1469598103934665603ull ^ parent_hash;
-    for (int t : token_ids) {
-        h ^= (uint64_t)(uint32_t)t;
-        h *= 1099511628211ull;
-    }
-    if (h == 0) h = 0x9e3779b97f4a7c15ull; // never 0 (0 reads as "no hash")
-    return h;
-}
-
-std::vector<uint64_t> PagedKVManager::compute_block_hashes(const std::vector<int>& token_ids) const {
-    std::vector<uint64_t> hashes;
-    uint64_t parent = 0; // NONE_HASH analogue
-    size_t n_full = token_ids.size() / block_size_;
-    for (size_t i = 0; i < n_full; ++i) {
-        std::vector<int> blk(token_ids.begin() + i * block_size_,
-                             token_ids.begin() + (i + 1) * block_size_);
-        parent = hash_block(parent, blk);
-        hashes.push_back(parent);
-    }
-    return hashes;
-}
-
-size_t PagedKVManager::get_computed_blocks(const std::vector<uint64_t>& block_hashes) {
-    std::vector<KVCacheBlock*> hits;
-    for (uint64_t bh : block_hashes) {        // stop at first miss (prefix property)
-        KVCacheBlock* cb = pool_.get_cached_block(bh);
-        if (!cb) break;
-        hits.push_back(cb);
-    }
-    pool_.touch(hits);                        // ++ref_cnt, pull from free list
-    return hits.size() * (size_t)block_size_;
-}
-
-void PagedKVManager::cache_blocks(int seq_id, const std::vector<uint64_t>& block_hashes, size_t num_tokens) {
-    auto& req = req_to_blocks_[seq_id];
-    size_t n_full = num_tokens / block_size_;
-    pool_.cache_full_blocks(req, /*num_cached=*/0, n_full, block_hashes);
-}
-
-} // namespace paged

backend/cpp/llama-cpp/paged/paged_kv_manager.h

@@ -1,108 +0,0 @@
-#pragma once
-// Paged KV cache block manager for llama.cpp (CPU-first prototype).
-//
-// Host-side block management is a faithful port of vLLM V1:
-//   vllm/v1/core/kv_cache_utils.py            (KVCacheBlock, FreeKVCacheBlockQueue, hash_block_tokens)
-//   vllm/v1/core/block_pool.py                (BlockPool: get_new_blocks/touch/free/evict/cache_full_blocks)
-//   vllm/v1/core/single_type_kv_cache_manager.py (allocate_new_blocks, find_longest_cache_hit)
-//
-// Parity is on behavior/algorithm (block chaining, first-miss stop, ref-counting,
-// LRU eviction order), not on exact hash bytes. This unit has zero ggml/llama.cpp
-// dependency so it can be unit-tested in isolation.
-
-#include <cstdint>
-#include <vector>
-#include <unordered_map>
-#include <map>
-
-namespace paged {
-
-// vLLM KVCacheBlock (kv_cache_utils.py).
-struct KVCacheBlock {
-    int32_t  block_id   = 0;
-    int      ref_cnt    = 0;
-    bool     has_hash   = false;   // vLLM: _block_hash is set only when full+cached
-    uint64_t block_hash = 0;
-    bool     is_null    = false;
-    KVCacheBlock* prev_free = nullptr;
-    KVCacheBlock* next_free = nullptr;
-
-    explicit KVCacheBlock(int32_t id = 0) : block_id(id) {}
-    void reset_hash() { has_hash = false; block_hash = 0; }
-};
-
-// Intrusive doubly-linked free list with fake head/tail (vLLM FreeKVCacheBlockQueue).
-// O(1) middle removal is required so touch() can pull a warm cached block out of the
-// free list when a later request hits its prefix.
-class FreeBlockQueue {
-public:
-    size_t num_free_blocks = 0;
-
-    explicit FreeBlockQueue(const std::vector<KVCacheBlock*>& blocks);
-    KVCacheBlock* popleft();
-    std::vector<KVCacheBlock*> popleft_n(size_t n);
-    void remove(KVCacheBlock* block);
-    void append(KVCacheBlock* block);
-    void append_n(const std::vector<KVCacheBlock*>& blocks);
-    void prepend_n(const std::vector<KVCacheBlock*>& blocks);
-    std::vector<KVCacheBlock*> get_all_free_blocks() const;
-
-private:
-    KVCacheBlock fake_head{-1};
-    KVCacheBlock fake_tail{-1};
-};
-
-// vLLM BlockPool (block_pool.py).
-class BlockPool {
-public:
-    KVCacheBlock* null_block = nullptr;
-
-    BlockPool(int32_t num_blocks, bool enable_caching);
-    std::vector<KVCacheBlock*> get_new_blocks(size_t n);
-    KVCacheBlock* get_cached_block(uint64_t block_hash);
-    void touch(const std::vector<KVCacheBlock*>& blocks);
-    void free_blocks(const std::vector<KVCacheBlock*>& ordered_blocks);
-    void cache_full_blocks(const std::vector<KVCacheBlock*>& req_blocks,
-                           size_t num_cached_blocks, size_t num_full_blocks,
-                           const std::vector<uint64_t>& block_hashes);
-    size_t get_num_free_blocks() const { return free_queue_.num_free_blocks; }
-
-private:
-    bool maybe_evict_cached_block(KVCacheBlock* block);
-
-    bool enable_caching_;
-    std::vector<KVCacheBlock> blocks_;     // owns all block descriptors
-    std::vector<KVCacheBlock*> ptrs_;
-    FreeBlockQueue free_queue_;
-    // vLLM stores hash -> {block_id: block} to allow duplicate-content blocks; the
-    // prototype keeps the last writer (single KV-cache group is sufficient for the wins).
-    std::unordered_map<uint64_t, KVCacheBlock*> cached_block_hash_to_block_;
-};
-
-// Allocation + prefix-caching surface, ported from SingleTypeKVCacheManager /
-// FullAttentionManager. Single KV-cache group; no extra_keys / eagle / spec-decode.
-class PagedKVManager {
-public:
-    PagedKVManager(int32_t num_blocks, int block_size, bool enable_caching);
-
-    // Grow seq_id to cover total_tokens slots. Returns false on OOM (free queue empty).
-    bool allocate(int seq_id, size_t total_tokens);
-    std::vector<int32_t> block_table(int seq_id) const;
-    int64_t slot(int seq_id, int pos) const;
-    std::vector<int64_t> slot_mapping(int seq_id, const std::vector<int>& positions) const;
-    void free(int seq_id);
-    int block_size() const { return block_size_; }
-
-    // Prefix caching (win 3).
-    static uint64_t hash_block(uint64_t parent_hash, const std::vector<int>& token_ids);
-    std::vector<uint64_t> compute_block_hashes(const std::vector<int>& token_ids) const;
-    size_t get_computed_blocks(const std::vector<uint64_t>& block_hashes); // returns num cached tokens
-    void cache_blocks(int seq_id, const std::vector<uint64_t>& block_hashes, size_t num_tokens);
-
-protected:
-    int block_size_;
-    BlockPool pool_;
-    std::map<int, std::vector<KVCacheBlock*>> req_to_blocks_;
-};
-
-} // namespace paged

backend/cpp/llama-cpp/paged/patches/0001-paged-kv-block-placement.patch

@@ -1,59 +0,0 @@
-diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
-index a49a055a6..d95102bbd 100644
---- a/src/llama-kv-cache.cpp
-+++ b/src/llama-kv-cache.cpp
-@@ -11,6 +11,8 @@
- #include <cstring>
- #include <limits>
- #include <map>
-+#include <numeric>
-+#include <cstdlib>
- #include <stdexcept>
- 
- static bool ggml_is_power_of_2(int n) {
-@@ -931,6 +933,45 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch,
-             return { };
-         }
- 
-+        // [paged, experimental] Place this sequence's tokens at permuted,
-+        // non-contiguous fixed-size BLOCK positions instead of a contiguous run.
-+        // This validates that attention is invariant to physical KV placement -
-+        // the correctness premise of paged attention. Enabled via LLAMA_KV_PAGED.
-+        // Single-sequence scope (uses get_used() as the logical base); falls back
-+        // to the normal allocator if the permuted cells aren't available.
-+        static const bool paged_mode = (std::getenv("LLAMA_KV_PAGED") != nullptr);
-+        if (paged_mode) {
-+            const uint32_t bs   = 16;                 // block size (tokens/block)
-+            const uint32_t nblk = cells.size() / bs;  // blocks in this stream's pool
-+            if (nblk >= 2) {
-+                // stride coprime to nblk => block-index permutation is a bijection
-+                uint32_t k = 1;
-+                for (uint32_t cand = (nblk / 2) | 1u; cand < nblk; cand += 2) {
-+                    if (std::gcd(cand, nblk) == 1u) { k = cand; break; }
-+                }
-+                const uint32_t base = cells.get_used();
-+                bool ok = true;
-+                for (uint32_t i = 0; i < n_tokens; ++i) {
-+                    const uint32_t L    = base + i;
-+                    const uint32_t b    = L / bs;
-+                    const uint32_t off  = L % bs;
-+                    if (b >= nblk) { ok = false; break; }
-+                    const uint32_t phys = ((b * k) % nblk) * bs + off; // permuted block
-+                    if (phys >= cells.size() || !cells.is_empty(phys)) { ok = false; break; }
-+                    res.idxs[s].push_back(phys);
-+                }
-+                if (ok && res.idxs[s].size() == n_tokens) {
-+                    if (std::getenv("LLAMA_KV_PAGED_DEBUG")) {
-+                        fprintf(stderr, "[paged] seq placed %u tok at cells:", n_tokens);
-+                        for (uint32_t z = 0; z < res.idxs[s].size() && z < 24; ++z) fprintf(stderr, " %u", res.idxs[s][z]);
-+                        fprintf(stderr, " (k=%u nblk=%u base=%u)\n", k, nblk, base);
-+                    }
-+                    continue; // paged placement succeeded for this sequence
-+                }
-+                res.idxs[s].clear(); // fall back to the normal allocator
-+            }
-+        }
-+
-         uint32_t n_tested = 0;
- 
-         // for continuous slots, we test that all tokens in the ubatch fit, starting from the current head

backend/cpp/llama-cpp/paged/patches/0002-paged-e2e-disable-broken-autofit.patch

@@ -1,12 +0,0 @@
-diff --git a/tests/test-paged-kv-e2e.cpp b/tests/test-paged-kv-e2e.cpp
-index 5a352e3..06ead50 100644
---- a/tests/test-paged-kv-e2e.cpp
-+++ b/tests/test-paged-kv-e2e.cpp
-@@ -115,6 +115,7 @@ static path_result run_paged(const std::string & model_path) {
-     params.sampling.temp = 0.0f;  // greedy
-     params.warmup        = false;
-     params.kv_paged      = true;
-+    params.fit_params    = false;  // honor explicit n_gpu_blocks; GB10 dev_memory over-reports free VRAM
-     params.n_gpu_blocks  = 64;
-     params.n_cpu_blocks  = 16;
-     params.n_sequences   = 1;

backend/cpp/llama-cpp/paged/tests/test_block_pool.cpp

@@ -1,42 +0,0 @@
-#include "../paged_kv_manager.h"
-#include <cassert>
-#include <cstdio>
-using namespace paged;
-
-int main() {
-    BlockPool pool(/*num_blocks=*/8, /*enable_caching=*/true);
-    // block 0 is reserved as null_block (vLLM pops one at init)
-    assert(pool.null_block != nullptr && pool.null_block->block_id == 0);
-    assert(pool.get_num_free_blocks() == 7);
-
-    // get_new_blocks sets ref_cnt=1 and removes from free list
-    auto b = pool.get_new_blocks(2);
-    assert(b.size() == 2 && b[0]->ref_cnt == 1 && b[1]->ref_cnt == 1);
-    assert(pool.get_num_free_blocks() == 5);
-
-    // cache two full blocks with chained hashes, then look them up
-    std::vector<uint64_t> hashes = {1111, 2222};
-    pool.cache_full_blocks(b, /*num_cached=*/0, /*num_full=*/2, hashes);
-    assert(b[0]->has_hash && b[0]->block_hash == 1111);
-    assert(pool.get_cached_block(1111) == b[0]);
-    assert(pool.get_cached_block(2222) == b[1]);
-    assert(pool.get_cached_block(9999) == nullptr);
-
-    // free: hashed blocks go to tail (kept warm), so they remain queryable.
-    pool.free_blocks(b);
-    assert(b[0]->ref_cnt == 0);
-    assert(pool.get_num_free_blocks() == 7);
-    assert(pool.get_cached_block(1111) == b[0]); // still cached/warm
-
-    // touch a warm cached block: pulls it out of free list, ++ref_cnt
-    pool.touch({b[0]});
-    assert(b[0]->ref_cnt == 1);
-    assert(pool.get_num_free_blocks() == 6);
-
-    // exhausting the pool then allocating evicts a warm cached hash
-    auto rest = pool.get_new_blocks(pool.get_num_free_blocks());
-    (void) rest;
-    assert(pool.get_cached_block(2222) == nullptr); // evicted on reuse
-    printf("test_block_pool: OK\n");
-    return 0;
-}

backend/cpp/llama-cpp/paged/tests/test_free_block_queue.cpp

@@ -1,44 +0,0 @@
-#include "../paged_kv_manager.h"
-#include <cassert>
-#include <cstdio>
-#include <vector>
-
-using namespace paged;
-
-static std::vector<KVCacheBlock> make_blocks(int n) {
-    std::vector<KVCacheBlock> v;
-    v.reserve(n);
-    for (int i = 0; i < n; ++i) v.push_back(KVCacheBlock{i});
-    return v;
-}
-
-int main() {
-    // ordered 0..9 at init; popleft yields ascending block_ids
-    auto blocks = make_blocks(10);
-    std::vector<KVCacheBlock*> ptrs;
-    for (auto& b : blocks) ptrs.push_back(&b);
-    FreeBlockQueue q(ptrs);
-    assert(q.num_free_blocks == 10);
-
-    KVCacheBlock* b0 = q.popleft();
-    assert(b0->block_id == 0);
-    assert(q.num_free_blocks == 9);
-
-    auto two = q.popleft_n(2);            // {1,2}
-    assert(two.size() == 2 && two[0]->block_id == 1 && two[1]->block_id == 2);
-    assert(q.num_free_blocks == 7);
-
-    // O(1) middle removal: remove block 5 (currently free), count drops
-    q.remove(ptrs[5]);
-    assert(q.num_free_blocks == 6);       // free: 3,4,6,7,8,9
-
-    // append puts a block at the tail; it comes back out only after the rest
-    q.append(b0);                          // free order now: 3,4,6,7,8,9,0
-    assert(q.num_free_blocks == 7);
-    auto all = q.get_all_free_blocks();
-    assert(all.front()->block_id == 3);
-    assert(all.back()->block_id == 0);
-
-    printf("test_free_block_queue: OK\n");
-    return 0;
-}

backend/cpp/llama-cpp/paged/tests/test_ggml_paged_attn.cpp

@@ -1,133 +0,0 @@
-// Phase 2 (core numeric de-risk): attention over GATHERED paged KV must equal
-// an independent host-computed reference.
-//
-// This answers the central risk in the design: feeding gather-to-scratch KV
-// (a sequence whose blocks are non-contiguous in the shared pool) into ggml's
-// standard attention ops (mul_mat -> soft_max_ext -> mul_mat) produces correct
-// attention. If this holds, the paged read path is numerically sound; the
-// remaining work is wiring it into llama-graph.cpp (Gate 0 in a real model).
-
-#include "../paged_kv_manager.h"
-
-#include "ggml.h"
-#include "ggml-cpu.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-
-#include <cassert>
-#include <cstdio>
-#include <cmath>
-#include <vector>
-
-using namespace paged;
-
-int main() {
-    const int d          = 8;     // head dim
-    const int n_kv       = 48;    // 3 blocks worth of KV tokens
-    const int n_q        = 4;     // query tokens
-    const int block_size = 16;
-    const int num_blocks = 8;
-    const int total_slots = block_size * num_blocks;
-    const float scale = 1.0f / std::sqrt((float) d);
-
-    // Non-contiguous physical layout for the KV sequence (blocks [2,1,5]).
-    PagedKVManager m(num_blocks, block_size, /*enable_caching=*/false);
-    assert(m.allocate(0, 2 * block_size));
-    assert(m.allocate(1, 2 * block_size));
-    m.free(0);
-    assert(m.allocate(2, n_kv));
-    std::vector<int> positions(n_kv);
-    for (int i = 0; i < n_kv; ++i) positions[i] = i;
-    auto slots64 = m.slot_mapping(2, positions);
-    std::vector<int32_t> slots32(slots64.begin(), slots64.end());
-
-    // Deterministic K, V, Q in logical [d, n] layout (column-major: col = token).
-    std::vector<float> K(d * n_kv), V(d * n_kv), Q(d * n_q);
-    for (int t = 0; t < n_kv; ++t)
-        for (int e = 0; e < d; ++e) {
-            K[t * d + e] = std::sin(0.1f * t + 0.3f * e);
-            V[t * d + e] = std::cos(0.2f * t - 0.1f * e);
-        }
-    for (int q = 0; q < n_q; ++q)
-        for (int e = 0; e < d; ++e) Q[q * d + e] = std::sin(0.05f * q + 0.7f * e);
-
-    // ---- Independent host reference attention -------------------------------
-    std::vector<float> ref(d * n_q, 0.0f);
-    for (int q = 0; q < n_q; ++q) {
-        std::vector<float> score(n_kv);
-        float mx = -1e30f;
-        for (int t = 0; t < n_kv; ++t) {
-            float dot = 0.0f;
-            for (int e = 0; e < d; ++e) dot += K[t * d + e] * Q[q * d + e];
-            score[t] = dot * scale;
-            mx = std::fmax(mx, score[t]);
-        }
-        float sum = 0.0f;
-        for (int t = 0; t < n_kv; ++t) { score[t] = std::exp(score[t] - mx); sum += score[t]; }
-        for (int t = 0; t < n_kv; ++t) {
-            float p = score[t] / sum;
-            for (int e = 0; e < d; ++e) ref[q * d + e] += p * V[t * d + e];
-        }
-    }
-
-    // ---- ggml paged path ----------------------------------------------------
-    ggml_backend_t backend = ggml_backend_cpu_init();
-    struct ggml_init_params dp = { ggml_tensor_overhead() * 16, NULL, true };
-    struct ggml_context * ctx_data = ggml_init(dp);
-
-    struct ggml_tensor * poolK = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, d, total_slots);
-    struct ggml_tensor * poolV = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, d, total_slots);
-    struct ggml_tensor * kSrc  = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, d, n_kv);
-    struct ggml_tensor * vSrc  = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, d, n_kv);
-    struct ggml_tensor * qT    = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, d, n_q);
-    struct ggml_tensor * wIdx  = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I64, n_kv);
-    struct ggml_tensor * gIdx  = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I32, n_kv);
-
-    ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_data, backend);
-    std::vector<float> zeros(d * total_slots, 0.0f);
-    ggml_backend_tensor_set(poolK, zeros.data(), 0, ggml_nbytes(poolK));
-    ggml_backend_tensor_set(poolV, zeros.data(), 0, ggml_nbytes(poolV));
-    ggml_backend_tensor_set(kSrc, K.data(), 0, ggml_nbytes(kSrc));
-    ggml_backend_tensor_set(vSrc, V.data(), 0, ggml_nbytes(vSrc));
-    ggml_backend_tensor_set(qT,   Q.data(), 0, ggml_nbytes(qT));
-    ggml_backend_tensor_set(wIdx, slots64.data(), 0, ggml_nbytes(wIdx));
-    ggml_backend_tensor_set(gIdx, slots32.data(), 0, ggml_nbytes(gIdx));
-
-    struct ggml_init_params cp = { ggml_tensor_overhead() * 64 + ggml_graph_overhead(), NULL, true };
-    struct ggml_context * ctx = ggml_init(cp);
-
-    struct ggml_tensor * wroteK = ggml_set_rows(ctx, poolK, kSrc, wIdx);
-    struct ggml_tensor * wroteV = ggml_set_rows(ctx, poolV, vSrc, wIdx);
-    struct ggml_tensor * gK = ggml_get_rows(ctx, wroteK, gIdx);          // [d, n_kv]
-    struct ggml_tensor * gV = ggml_get_rows(ctx, wroteV, gIdx);          // [d, n_kv]
-
-    struct ggml_tensor * kq    = ggml_mul_mat(ctx, gK, qT);              // [n_kv, n_q]
-    struct ggml_tensor * probs = ggml_soft_max_ext(ctx, kq, NULL, scale, 0.0f);
-    struct ggml_tensor * vT    = ggml_cont(ctx, ggml_transpose(ctx, gV)); // [n_kv, d]
-    struct ggml_tensor * out   = ggml_mul_mat(ctx, vT, probs);           // [d, n_q]
-    ggml_set_output(out);
-
-    struct ggml_cgraph * gf = ggml_new_graph(ctx);
-    ggml_build_forward_expand(gf, out);
-    ggml_gallocr_t galloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    assert(ggml_gallocr_alloc_graph(galloc, gf));
-    assert(ggml_backend_graph_compute(backend, gf) == GGML_STATUS_SUCCESS);
-
-    std::vector<float> got(d * n_q);
-    ggml_backend_tensor_get(out, got.data(), 0, ggml_nbytes(out));
-
-    // ---- compare ------------------------------------------------------------
-    double max_err = 0.0;
-    for (int i = 0; i < d * n_q; ++i) max_err = std::fmax(max_err, std::fabs(got[i] - ref[i]));
-    printf("paged attention max abs err vs host reference: %.3e\n", max_err);
-    assert(max_err < 1e-4 && "paged-gathered attention must match host reference");
-
-    ggml_gallocr_free(galloc);
-    ggml_free(ctx);
-    ggml_free(ctx_data);
-    ggml_backend_buffer_free(buf);
-    ggml_backend_free(backend);
-
-    printf("test_ggml_paged_attn: OK (attention over non-contiguous paged KV matches reference)\n");
-    return 0;
-}

backend/cpp/llama-cpp/paged/tests/test_ggml_paged_rw.cpp

@@ -1,142 +0,0 @@
-// Phase 1 integration test: prove the paged KV write+read MECHANISM at the
-// ggml-op level, driven by PagedKVManager.
-//
-//   write:  ggml_set_rows(pool, k_src, slot_mapping)   // scatter by slot
-//   read:   ggml_get_rows(pool, gather_idx)            // gather seq's slots
-//
-// The decisive property: a sequence's physical blocks are NON-CONTIGUOUS and
-// OUT-OF-ORDER (forced via allocate/free/reallocate), yet gather(write(x)) == x,
-// and a second sequence written into disjoint blocks does not contaminate it.
-// This is exactly how a paged read path feeds contiguous scratch to attention.
-
-#include "../paged_kv_manager.h"
-
-#include "ggml.h"
-#include "ggml-cpu.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-
-#include <cassert>
-#include <cstdio>
-#include <cmath>
-#include <vector>
-
-using namespace paged;
-
-int main() {
-    const int n_embd      = 8;
-    const int block_size  = 16;
-    const int num_blocks  = 8;                       // block 0 reserved as null
-    const int total_slots = block_size * num_blocks; // 128
-
-    // --- Force a non-contiguous, out-of-order block layout for seqC ----------
-    PagedKVManager m(num_blocks, block_size, /*enable_caching=*/false);
-    assert(m.allocate(/*seqA=*/0, 2 * block_size)); // blocks {1,2}
-    assert(m.allocate(/*seqB=*/1, 2 * block_size)); // blocks {3,4}
-    m.free(0);                                       // returns {1,2} to free list
-    assert(m.allocate(/*seqC=*/2, 3 * block_size));  // reuses freed blocks, reordered
-
-    auto btC = m.block_table(2);
-    auto btB = m.block_table(1);
-    printf("seqC block_table = [");
-    for (size_t i = 0; i < btC.size(); ++i) printf("%s%d", i ? "," : "", btC[i]);
-    printf("]\n");
-    assert(btC.size() == 3);
-    // sanity: seqC and seqB occupy disjoint physical blocks
-    for (int cb : btC) for (int bb : btB) assert(cb != bb);
-
-    const int n_tokens = 3 * block_size; // 48 tokens for seqC
-
-    // slot_mapping for seqC positions 0..n_tokens-1
-    std::vector<int> positions(n_tokens);
-    for (int i = 0; i < n_tokens; ++i) positions[i] = i;
-    std::vector<int64_t> slots64 = m.slot_mapping(2, positions); // I64 for set_rows
-    std::vector<int32_t> slots32(slots64.begin(), slots64.end()); // I32 for get_rows
-
-    // seqB occupies different blocks; write a sentinel there to prove isolation.
-    std::vector<int> posB(2 * block_size);
-    for (size_t i = 0; i < posB.size(); ++i) posB[i] = (int) i;
-    std::vector<int64_t> slotsB64 = m.slot_mapping(1, posB);
-
-    // --- ggml backend + persistent (statically allocated) tensors ------------
-    ggml_backend_t backend = ggml_backend_cpu_init();
-    assert(backend);
-
-    struct ggml_init_params dp = { /*mem_size=*/ ggml_tensor_overhead() * 16,
-                                   /*mem_buffer=*/ NULL, /*no_alloc=*/ true };
-    struct ggml_context * ctx_data = ggml_init(dp);
-
-    // The shared paged KV pool: one flat block pool, exactly like a paged layer.
-    struct ggml_tensor * pool    = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, n_embd, total_slots);
-    struct ggml_tensor * k_src   = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, n_embd, n_tokens);
-    struct ggml_tensor * w_idx   = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I64, n_tokens);
-    struct ggml_tensor * g_idx   = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I32, n_tokens);
-    struct ggml_tensor * kB_src  = ggml_new_tensor_2d(ctx_data, GGML_TYPE_F32, n_embd, (int) posB.size());
-    struct ggml_tensor * wB_idx  = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I64, (int) posB.size());
-
-    ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_data, backend);
-    assert(buf);
-
-    // pool starts zeroed
-    std::vector<float> zeros(n_embd * total_slots, 0.0f);
-    ggml_backend_tensor_set(pool, zeros.data(), 0, ggml_nbytes(pool));
-
-    // token t carries the value (float) t in every embedding lane -> easy to verify
-    std::vector<float> ksrc(n_embd * n_tokens);
-    for (int t = 0; t < n_tokens; ++t)
-        for (int e = 0; e < n_embd; ++e) ksrc[t * n_embd + e] = (float) t;
-    ggml_backend_tensor_set(k_src, ksrc.data(), 0, ggml_nbytes(k_src));
-    ggml_backend_tensor_set(w_idx, slots64.data(), 0, ggml_nbytes(w_idx));
-    ggml_backend_tensor_set(g_idx, slots32.data(), 0, ggml_nbytes(g_idx));
-
-    // seqB sentinel = 999 everywhere
-    std::vector<float> kBsrc(n_embd * posB.size(), 999.0f);
-    ggml_backend_tensor_set(kB_src, kBsrc.data(), 0, ggml_nbytes(kB_src));
-    ggml_backend_tensor_set(wB_idx, slotsB64.data(), 0, ggml_nbytes(wB_idx));
-
-    // --- compute graph: write seqB, write seqC, then gather seqC -------------
-    struct ggml_init_params cp = { /*mem_size=*/ ggml_tensor_overhead() * 32 + ggml_graph_overhead(),
-                                   /*mem_buffer=*/ NULL, /*no_alloc=*/ true };
-    struct ggml_context * ctx = ggml_init(cp);
-
-    struct ggml_tensor * wroteB = ggml_set_rows(ctx, pool,   kB_src, wB_idx); // view(pool)
-    struct ggml_tensor * wroteC = ggml_set_rows(ctx, wroteB, k_src,  w_idx);  // chain so order is fixed
-    struct ggml_tensor * gathered = ggml_get_rows(ctx, wroteC, g_idx);
-    ggml_set_output(gathered);
-
-    struct ggml_cgraph * gf = ggml_new_graph(ctx);
-    ggml_build_forward_expand(gf, gathered);
-
-    ggml_gallocr_t galloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    assert(ggml_gallocr_alloc_graph(galloc, gf));
-
-    assert(ggml_backend_graph_compute(backend, gf) == GGML_STATUS_SUCCESS);
-
-    // --- verify gather(write(x)) == x for the non-contiguous sequence --------
-    std::vector<float> out(n_embd * n_tokens);
-    ggml_backend_tensor_get(gathered, out.data(), 0, ggml_nbytes(gathered));
-
-    int mism = 0;
-    for (int t = 0; t < n_tokens; ++t)
-        for (int e = 0; e < n_embd; ++e)
-            if (std::fabs(out[t * n_embd + e] - (float) t) > 1e-6f) mism++;
-    assert(mism == 0 && "gathered paged KV must equal source (round-trip)");
-
-    // --- verify isolation: read seqC slots directly from pool, unaffected by seqB
-    std::vector<float> pool_host(n_embd * total_slots);
-    ggml_backend_tensor_get(pool, pool_host.data(), 0, ggml_nbytes(pool));
-    for (int t = 0; t < n_tokens; ++t) {
-        int slot = (int) slots64[t];
-        for (int e = 0; e < n_embd; ++e)
-            assert(std::fabs(pool_host[slot * n_embd + e] - (float) t) < 1e-6f);
-    }
-
-    ggml_gallocr_free(galloc);
-    ggml_free(ctx);
-    ggml_free(ctx_data);
-    ggml_backend_buffer_free(buf);
-    ggml_backend_free(backend);
-
-    printf("test_ggml_paged_rw: OK (non-contiguous paged write/gather round-trip)\n");
-    return 0;
-}

backend/cpp/llama-cpp/paged/tests/test_paged_kv_manager.cpp

@@ -1,32 +0,0 @@
-#include "../paged_kv_manager.h"
-#include <cassert>
-#include <cstdio>
-using namespace paged;
-
-int main() {
-    PagedKVManager m(/*num_blocks=*/8, /*block_size=*/16, /*enable_caching=*/false);
-    // 20 tokens -> ceil(20/16)=2 blocks
-    assert(m.allocate(/*seq=*/0, 20));
-    auto bt = m.block_table(0);
-    assert(bt.size() == 2);
-
-    // slot arithmetic: pos 0 -> block bt[0]*16 + 0 ; pos 17 -> bt[1]*16 + 1
-    assert(m.slot(0, 0)  == (int64_t)bt[0] * 16 + 0);
-    assert(m.slot(0, 17) == (int64_t)bt[1] * 16 + 1);
-
-    auto sm = m.slot_mapping(0, {0, 16, 17});
-    assert(sm.size() == 3 && sm[1] == (int64_t)bt[1] * 16 + 0);
-
-    // growing the same seq reuses existing blocks, adds only new ones
-    assert(m.allocate(0, 40)); // ceil(40/16)=3 -> +1 block
-    assert(m.block_table(0).size() == 3);
-
-    // OOM: blocks left = 8 - 1(null) - 3 = 4 blocks; ask for 5 blocks
-    assert(m.allocate(1, 5 * 16) == false);
-
-    // free returns blocks to the pool for reuse
-    m.free(0);
-    assert(m.allocate(1, 5 * 16)); // now fits
-    printf("test_paged_kv_manager: OK\n");
-    return 0;
-}

backend/cpp/llama-cpp/paged/tests/test_prefix_cache.cpp

@@ -1,35 +0,0 @@
-#include "../paged_kv_manager.h"
-#include <cassert>
-#include <cstdio>
-#include <vector>
-using namespace paged;
-
-int main() {
-    PagedKVManager m(/*num_blocks=*/64, /*block_size=*/16, /*enable_caching=*/true);
-
-    // shared prefix of 32 tokens (2 full blocks) + distinct suffix
-    std::vector<int> shared(32);
-    for (int i = 0; i < 32; ++i) shared[i] = 100 + i;
-
-    // chained hashing is deterministic and prefix-sensitive
-    auto h = m.compute_block_hashes(shared);
-    assert(h.size() == 2);
-    auto h2 = m.compute_block_hashes(shared);
-    assert(h == h2);                          // deterministic
-    std::vector<int> other = shared; other[0] = 999;
-    assert(m.compute_block_hashes(other)[0] != h[0]); // sensitive to content
-
-    // seq 0: cold, no cache hit yet
-    assert(m.get_computed_blocks(h) == 0);
-    assert(m.allocate(0, 32));
-    m.cache_blocks(0, h, 32);
-
-    // seq 1: warm — the 2 shared blocks are a cache hit (32 tokens)
-    assert(m.get_computed_blocks(h) == 32);
-
-    // first-miss stop: a chain that diverges after block 1 hits only 1 block
-    auto hmix = h; hmix[1] = 0xDEADBEEF;
-    assert(m.get_computed_blocks(hmix) == 16);
-    printf("test_prefix_cache: OK\n");
-    return 0;
-}

backend/cpp/llama-cpp/patches/BENCHMARKS.md

@@ -1,106 +0,0 @@
-# Paged-attention / parity benchmarks (GB10 / DGX Spark)
-
-Goal of the series: vLLM parity. This records the measured gap so the parity claim is data-backed, not asserted.
-
-**Setup:** GB10 (sm_121, 119 GiB unified). Model Qwen3-Coder-30B-A3B. llama.cpp = pinned base + this series
-(MXFP4_MOE, `-fa 1 -b 2048 -ub 2048`, `llama-batched-bench`, PP=512 TG=128). vLLM = 0.23.0 FP8 (recorded
-prior run, same box/model). S_PP / S_TG are aggregate prefill / decode tok/s across B streams.
-
-## Fresh llama.cpp (this series, MXFP4) vs vLLM (FP8)
-
-| B | llama S_PP | vLLM S_PP | PP gap | llama S_TG | vLLM S_TG | TG gap |
-|---|-----------|-----------|--------|-----------|-----------|--------|
-| 1 | 1565 | 9644 | 6.2× | **83** | 48 | **llama wins** |
-| 8 | 3648 | 33373 | 9.1× | 126 | 312 | 2.5× |
-| 32 | 2074 | 99398 | 48× | 319 | 1171 | 3.7× |
-| 64 | 3643 | 151990 | 42× | 771 | 2064 | 2.7× |
-
-## Verdict — two distinct gaps, only one is the engine's
-
-1. **Prefill (S_PP): 6–48× behind, and it does NOT scale with B** (plateaus ~3.6k). This is the **FP4 MoE
-   GEMM kernel** (`mul_mat_q<MXFP4>` ~22 TFLOP/s), confirmed earlier. **Paged attention cannot close this** —
-   it's per-token compute. Needs the tcgen05/CUTLASS grouped-GEMM (Lever 3, multi-week, no upstream base).
-2. **Decode at concurrency (S_TG): 2.5–3.7× behind for B≥8** (we *win* at B=1). This gap IS partly the
-   engine's domain — vLLM's block-paged KV + continuous batching pack more concurrent decode work per step.
-   **This is what patches 0003–0006 target.** The win here is realistic; the prefill win is not (kernel).
-
-## CORRECTION — decode-phase profile (B=64, decode-dominated nsys)
-
-The "decode gap is engine-addressable" read above was **wrong**. Profiling a decode-dominated B=64 run:
-
-| kernel | % GPU time |
-|---|---|
-| `mul_mat_q<MXFP4>` (MoE GEMM) | **54.6** |
-| `flash_attn_ext` (attention) | 19.8 |
-| `mul_mat_q<Q8>` (dense) | 10.9 |
-| KV writes / quant / norms / rest | ~15 |
-
-**Decode at concurrency is ALSO dominated by the FP4 MoE GEMM (54.6%)** — the same Lever-3 kernel as prefill.
-Attention (the only thing paging optimizes) is ~20%, and the gather-read reclaims only the *masked-cell*
-fraction of that. So **the paged series (0003–0006) cannot close the vLLM gap in either phase** — both are
-MoE-kernel-bound. vLLM's concurrency advantage is its MoE/attention *kernels*, not (mainly) its KV management.
-
-### What the paged series IS still good for (just not throughput parity)
-
-- **Capacity**: block-granular + on-demand allocation → fit more/longer concurrent sequences in fixed VRAM.
-- **Prefix sharing**: cross-request block dedup → lower TTFT + memory on shared system prompts / RAG.
-
-These are real wins on *memory-pressured* and *shared-prefix* workloads — but they are not tok/s parity, and
-batched-bench (fresh, non-fragmented, no shared prefix) won't show them.
-
-## DENSE model parity (Qwen3-32B) — does the kernel gap exist for dense too? YES.
-
-The MoE work above is about the grouped MoE GEMM. Dense models use a different (non-grouped) matmul path,
-so we benchmarked a dense 32B head-to-head.
-
-**Headline comparison — vLLM NVFP4 W4A16 vs llama.cpp Q4_K_M.** This is the *correct apples-to-apples on
-DGX Spark*: both are **4-bit weights / 16-bit activations** (same quant class). vLLM = `Qwen3-32B-NVFP4A16`
-(FlashInfer Marlin W4A16 kernel); llama.cpp = `Qwen3-32B-Q4_K_M` (int8-MMQ compute). The only difference is
-the compute kernel — which is exactly what we're measuring. (Full **W4A4** NVFP4 does not run on GB10 today;
-root cause below — and it would *not* be a fair comparison even if it did, since Q4_K_M is also weight-only-4-bit.)
-
-| B | llama Q4_K_M PP | vLLM W4A16 PP | PP gap | llama decode | vLLM decode | TG gap |
-|---|---|---|---|---|---|---|
-| 1 | 708 | 5367 | 7.6× | 10.2 | 11.7 | ~parity |
-| 8 | 761 | 14941 | 20× | 58 | 92 | 1.6× |
-| 32 | 763 | 21952 | 29× | 205 | 330 | 1.6× |
-| 64 | 765 | 24444 | 32× | 253 | 569 | 2.2× |
-
-**Findings:**
-1. **Dense prefill has the SAME (larger) kernel gap.** llama dense prefill plateaus at ~765 t/s regardless of
-   B; vLLM scales to 24.4k (32×). Both read 4-bit weights — the gap is the compute kernel: vLLM's FP4 Marlin
-   tensor-core GEMM vs llama's int8-MMQ. (Note: on consumer Blackwell, W4A16 Marlin is also reported *faster*
-   than the experimental W4A4 path, so W4A16 isn't a handicapped stand-in — it's the fast path.)
-2. **Decode is ~parity at B=1** (10.2 vs 11.7 — both weight-bandwidth-bound reading 4-bit weights), and the
-   gap grows with batch (compute starts to matter → the kernel gap reappears: 2.2× at B=64).
-3. **Scope decision (the reason for this benchmark): the Lever-3 kernel track must also deliver a NON-grouped
-   block-scaled FP4 GEMM for dense**, not only the MoE grouped GEMM. The dense GEMM is the simpler of the two
-   (a plain CUTLASS dense GEMM), so it's a good first kernel to land — and it benefits every dense model.
-   - **No cheap lever:** `GGML_CUDA_FORCE_CUBLAS` is a **no-op for dense too** (Q4_K pp512: 720.8 vs 721.8) —
-     dequant→cuBLAS-BF16 doesn't engage / isn't faster than int8-MMQ on GB10. With ubatch (saturates) and
-     nwarps (static_assert) already ruled out for MoE, **every config/flag lever is now exhausted** for both
-     model classes. Parity is strictly the FP4 tensor-core kernel.
-4. **Why full W4A4 NVFP4 hangs on GB10 (root cause, researched).** This is a *known consumer-Blackwell
-   limitation, not a misconfiguration*. **FlashInfer ships no FP4 cubins for sm_120/sm_121** — its precompiled
-   kernels are all datacenter `Sm100a/Sm103a` (B200/B300). So on GB10 the dense `mm_fp4` W4A4 GEMM has no
-   working kernel: the optimized path is gated off for sm_121 (heuristic checks `minor==0`; 12.1 fails), the
-   CUTLASS dense FP4 fallback is documented to silently return **all-zeros**, and TRT-LLM errors at capability
-   120. Our exact symptom — loads weights, then stalls at the first profiling forward pass with
-   `enable_flashinfer_autotune=True` at 0–3% GPU — is the **FlashInfer FP4 autotuner/JIT spinning on an arch
-   with no FP4 cubins** (matches vllm #30163/#26381, flashinfer #2577/#3294). The "NVFP4 on DGX Spark" story
-   everyone cites is about *quantization + memory footprint + W4A16/MoE*, **not dense W4A4 inference**, which
-   isn't validated on sm_121 yet (where people patched it working, it was slower than W4A16 anyway).
-   **Therefore W4A16 vs Q4_K_M above is the right, reproducible apples-to-apples** for DGX Spark today.
-   Optional W4A4 retry (verify output isn't zeros first): `VLLM_SKIP_FLASHINFER_AUTOTUNE=1` +
-   `VLLM_NVFP4_GEMM_BACKEND=cutlass` + `--enforce-eager`, or NVIDIA's `vllm/vllm-openai:cu130-nightly` container.
-
-## So, honestly, where parity stands
-
-- **Decode single-stream: already at/above parity** (B=1: 83 vs 48).
-- **Decode concurrency: a real, engine-addressable gap** the paged series can narrow (0004 on-demand pool +
-  0005 continuous batching). Target: close the 2.5–3.7× at B≥8.
-- **Prefill: kernel-bound, not engine-bound.** No amount of paging reaches vLLM here; that's a separate track.
-
-**Series status when measured:** 0001 (vendor) + 0002 (placement, token-identical) done; 0003 (gather-read)
-turn-key-planned, not yet implemented. These numbers are the *baseline* the engine patches must improve on at
-B≥8 decode — re-run this table after 0004/0005 to show the concurrency gap closing.

backend/cpp/llama-cpp/patches/README.md

@@ -1,82 +0,0 @@
-# llama.cpp patch series — paged attention (vLLM-parity engine)
-
-A **stacking** series: each patch is a small, self-contained, independently-buildable step toward an
-in-model paged-attention engine. They apply in numeric order on top of the pinned `LLAMA_VERSION`
-(`backend/cpp/llama-cpp/Makefile`). The build applies them automatically after checkout (see the
-`llama.cpp:` target). Keeping the work as ordered patches — rather than one big diff — is what lets us
-**rebase cleanly across llama.cpp bumps and avoid drift**: when a patch stops applying, only that small
-patch needs fixing, and the failure points at exactly which step the upstream change touched.
-
-## Base
-
-- `LLAMA_VERSION` pin in `../Makefile`. **All patches are generated against that exact commit.** Bumping
-  the pin = re-run the regen workflow below and fix only the patches that no longer apply.
-
-## The series (phases → patches)
-
-| # | Patch | What | Verifies |
-|---|-------|------|----------|
-| 0001 | `0001-vendor-paged-kv-manager.patch` | Add `src/paged-kv-manager.{h,cpp}` (vLLM-parity block manager, CPU foundation) + CMake; no behavior change | builds; unit-tested separately under `../paged/` |
-| 0002 | `0002-paged-kv-storage.patch` | Shared block-pool KV tensor + `set_rows`-by-slot writes, behind `LLAMA_KV_PAGED` | builds; write/gather round-trip |
-| 0003 | `0003-paged-gather-read.patch` | `build_attn_paged` gather-read in `llama-graph.cpp` | **Gate 0**: token-identical greedy gen, single + multi-seq |
-| 0004 | `0004-paged-ondemand-alloc.patch` | On-demand block allocation via PagedKVManager | max concurrent seqs before OOM |
-| 0005 | `0005-paged-continuous-batching.patch` | Block-granular admit/evict in the server slot path | tok/s vs concurrency, mixed-length |
-| 0006 | `0006-paged-prefix-caching.patch` | Block-hash cross-request prefix dedup | TTFT + memory on shared prefixes |
-
-Each row is a separate `git commit` on the dev branch (below), exported 1:1 as a patch. Default off
-(`LLAMA_KV_PAGED`) until Gate 0 (0003) is green, so partial series never changes stock behavior.
-
-## Regen workflow (the anti-drift recipe)
-
-```sh
-# 1. check out the exact pin into a dev tree
-git -C /tmp clone https://github.com/ggml-org/llama.cpp llama-dev && cd /tmp/llama-dev
-git checkout <LLAMA_VERSION from ../Makefile>
-git checkout -b paged
-
-# 2. apply the current series (each becomes a commit), or develop the next patch
-git am /path/to/backend/cpp/llama-cpp/patches/00*.patch     # or `git apply` + commit per patch
-
-# 3. iterate a phase as ONE commit, then export the whole series 1:1
-git format-patch <LLAMA_VERSION>..paged -o /path/to/backend/cpp/llama-cpp/patches/ --zero-commit -N
-
-# 4. on a pin bump: rebase `paged` onto the new pin; only conflicting patches need edits; re-export.
-```
-
-## Build integration
-
-`../Makefile`'s `llama.cpp:` target runs, after `git checkout -b build $(LLAMA_VERSION)`:
-```
-for p in $(CURRENT_MAKEFILE_DIR)/patches/0*.patch; do git apply --verbose "$p"; done
-```
-All variants (avx/avx2/avx512/cuda/…) copy the patched `llama.cpp/` tree, so the series ships everywhere.
-
-## Status
-
-- **0001 vendor manager — DONE.** Applies clean to the pin; builds into `libllama`.
-- **0002 block placement — DONE + VERIFIED.** Built `llama-simple` at the pin; greedy generation is
-  **token-identical** stock vs `LLAMA_KV_PAGED=1` (Qwen3-0.6B), paged branch confirmed firing.
-- **0003 gather-read — DONE + VERIFIED (Gate 0 green).** Implemented in the **additive** form
-  (`ADDITIVE_DESIGN.md`): all logic in new `src/paged-attn.{h,cpp}` (a `llm_graph_input_i` gather-index
-  subclass + the K/V/mask gather), hooked by **one** line in `build_attn` + **two** thin accessors on
-  `llama_kv_cache_context` + 1 CMake line (216 insertions; no edit to `llm_graph_input_attn_kv` or
-  `llama-graph.h`). Greedy generation is **token-identical** stock vs `LLAMA_KV_PAGED=1` (Qwen3-0.6B,
-  **9/9** across 3 prompts × {32,96,128} tokens), with `n_gather=71 < n_kv=256` confirming real
-  compaction. Patch: `0003-paged-gather-read-env-LLAMA_KV_PAGED.patch`.
-  - **Key correctness finding:** `get_gather_idxs` must emit cells **sorted by token position**. The CPU
-    flash-attn online softmax reduces cells in physical-array order and is FP-order-sensitive, so 0002's
-    scattered placement *alone* (full-window read, no gather) diverges from stock once a sequence crosses
-    the first 16-cell block. The position-sorted gather reproduces stock's exact reduction order -> bit-
-    identical, not merely mathematically equivalent. So 0002 is the placement substrate; **0003 is what
-    makes paged placement token-identical under flash-attn.**
-- 0004–0006 follow.
-
-### Honest parity note (important)
-
-This series delivers the paged-attention **engine** (capacity + scheduling + prefix sharing). It does **not**
-by itself reach vLLM throughput parity, because the measured prefill bottleneck is the **FP4 MoE GEMM kernel**
-(Lever 3: `mul_mat_q<MXFP4>` ~22 TFLOP/s, ~27× behind vLLM) — a *per-token compute* gap that paging does not
-touch. Paged attention closes the **concurrency/memory** gap (more sequences, prefix reuse); the prefill/throughput
-gap additionally needs the tcgen05/CUTLASS grouped-GEMM (deferred, upstream-grade, no shortcut — see
-`../paged/UPSTREAM_GGML_ISSUE.md` and `DGX_BLACKWELL_PLAN.md`). So full vLLM parity = this series **AND** the
-kernel; neither alone suffices.

backend/cpp/llama-cpp/patches/kernel/0001-fp4-grouped-moe-scaffold.patch

@@ -1,91 +0,0 @@
-diff --git a/ggml/src/ggml-cuda/fp4-grouped-moe.cu b/ggml/src/ggml-cuda/fp4-grouped-moe.cu
-new file mode 100644
-index 0000000..5f5a782
---- /dev/null
-+++ b/ggml/src/ggml-cuda/fp4-grouped-moe.cu
-@@ -0,0 +1,46 @@
-+#include "fp4-grouped-moe.cuh"
-+
-+#include <cstdlib>
-+#include <cstdio>
-+
-+// SCAFFOLD for the FP4 grouped-GEMM MoE kernel (Lever 3).
-+//
-+// Why: on GB10 (sm_121) the MoE matmul runs mul_mat_q<MXFP4> - a warp-level mma.sync grouped MMQ -
-+// at ~22 effective TFLOP/s, ~27x behind vLLM prefill, and it also dominates decode at concurrency
-+// (54.6% of GPU time at B=64). It is the single bottleneck to vLLM parity in BOTH phases; paged
-+// attention cannot touch it (proven by profiling). The fix is a CUTLASS-3.x collective-mainloop
-+// grouped GEMM over all experts, block-scaled e2m1 operands via tcgen05 tensor-memory MMA.
-+//
-+// This file is the integration seam. It is currently a no-op that always falls back to MMQ, so the
-+// default build is byte-identical. The kernel is filled in over the phases in the design doc.
-+
-+static bool fp4_grouped_enabled() {
-+    static const bool en = (std::getenv("GGML_CUDA_FP4_GROUPED") != nullptr);
-+    return en;
-+}
-+
-+bool ggml_cuda_fp4_grouped_moe(
-+        ggml_backend_cuda_context & ctx,
-+        const ggml_tensor * src0,
-+        const ggml_tensor * src1,
-+        const ggml_tensor * ids,
-+        ggml_tensor       * dst) {
-+    GGML_UNUSED(ctx); GGML_UNUSED(src1); GGML_UNUSED(ids); GGML_UNUSED(dst);
-+
-+    if (!fp4_grouped_enabled()) {
-+        return false; // default: existing MMQ path
-+    }
-+    if (src0->type != GGML_TYPE_MXFP4 && src0->type != GGML_TYPE_NVFP4) {
-+        return false;
-+    }
-+
-+    // TODO(kernel - see kernel design doc): CUTLASS 3.x GemmGrouped, sm_120a, block-scaled e2m1,
-+    // tcgen05 MMA; per-expert problem offsets from `ids`; fused activation quant; numerical parity
-+    // vs mul_mat_q<MXFP4> before enabling by default.
-+    static bool warned = false;
-+    if (!warned) {
-+        warned = true;
-+        fprintf(stderr, "[fp4-grouped] GGML_CUDA_FP4_GROUPED set, kernel not yet implemented - using MMQ\n");
-+    }
-+    return false; // scaffold: fall back until the kernel lands
-+}
-diff --git a/ggml/src/ggml-cuda/fp4-grouped-moe.cuh b/ggml/src/ggml-cuda/fp4-grouped-moe.cuh
-new file mode 100644
-index 0000000..29e1b5a
---- /dev/null
-+++ b/ggml/src/ggml-cuda/fp4-grouped-moe.cuh
-@@ -0,0 +1,13 @@
-+#pragma once
-+
-+#include "common.cuh"
-+
-+// Entry point for the tcgen05/CUTLASS block-scaled FP4 (MXFP4/NVFP4) grouped-GEMM MoE kernel for
-+// Blackwell consumer GPUs (sm_120/121). Returns true if it handled the op; false to fall back to
-+// the existing warp-mma MMQ path. Gated behind GGML_CUDA_FP4_GROUPED until correct + faster.
-+bool ggml_cuda_fp4_grouped_moe(
-+        ggml_backend_cuda_context & ctx,
-+        const ggml_tensor * src0,   // expert weights, MXFP4/NVFP4 [n_embd, n_ff, n_expert]
-+        const ggml_tensor * src1,   // activations, F32 [n_embd, n_tokens, ...]
-+        const ggml_tensor * ids,    // expert routing, I32
-+        ggml_tensor       * dst);   // F32 output
-diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
-index 8ea462a..104d131 100644
---- a/ggml/src/ggml-cuda/ggml-cuda.cu
-+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
-@@ -30,6 +30,7 @@
- #include "ggml-cuda/im2col.cuh"
- #include "ggml-cuda/mmf.cuh"
- #include "ggml-cuda/mmq.cuh"
-+#include "ggml-cuda/fp4-grouped-moe.cuh"
- #include "ggml-cuda/mmvf.cuh"
- #include "ggml-cuda/mmvq.cuh"
- #include "ggml-cuda/norm.cuh"
-@@ -2701,6 +2702,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
-         }
- 
-         if (ggml_cuda_should_use_mmq(src0->type, cc, ne12, /*n_experts=*/ne02)) {
-+            if (ggml_cuda_fp4_grouped_moe(ctx, src0, src1, ids, dst)) { return; }
-             ggml_cuda_mul_mat_q(ctx, src0, src1, ids, dst);
-             return;
-         }

backend/cpp/llama-cpp/patches/paged/0001-vendor-paged-kv-manager.patch

@@ -1,447 +0,0 @@
-From bef64835d444a44ed8391bc395cdab38164229d5 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Fri, 19 Jun 2026 22:54:49 +0000
-Subject: [PATCH] vendor paged kv manager
-
-vLLM-parity host-side KV block manager (FreeBlockQueue, BlockPool,
-PagedKVManager, chained-hash prefix cache). Pure C++17, no behavior change -
-nothing uses it yet; wired in by later patches in the series.
----
- src/CMakeLists.txt       |   1 +
- src/paged-kv-manager.cpp | 296 +++++++++++++++++++++++++++++++++++++++
- src/paged-kv-manager.h   | 108 ++++++++++++++
- 3 files changed, 405 insertions(+)
- create mode 100644 src/paged-kv-manager.cpp
- create mode 100644 src/paged-kv-manager.h
-
-diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
-index d15ccfd99..a030940b8 100644
---- a/src/CMakeLists.txt
-+++ b/src/CMakeLists.txt
-@@ -24,6 +24,7 @@ add_library(llama
-             llama-io.cpp
-             llama-kv-cache.cpp
-             llama-kv-cache-iswa.cpp
-+            paged-kv-manager.cpp
-             llama-kv-cache-dsa.cpp
-             llama-memory.cpp
-             llama-memory-hybrid.cpp
-diff --git a/src/paged-kv-manager.cpp b/src/paged-kv-manager.cpp
-new file mode 100644
-index 000000000..ca0dcd83a
---- /dev/null
-+++ b/src/paged-kv-manager.cpp
-@@ -0,0 +1,296 @@
-+#include "paged-kv-manager.h"
-+#include <cassert>
-+#include <stdexcept>
-+
-+namespace paged {
-+
-+// ---------------------------------------------------------------------------
-+// FreeBlockQueue  (port of kv_cache_utils.py FreeKVCacheBlockQueue)
-+// ---------------------------------------------------------------------------
-+
-+FreeBlockQueue::FreeBlockQueue(const std::vector<KVCacheBlock*>& blocks) {
-+    num_free_blocks = blocks.size();
-+    for (size_t i = 0; i < blocks.size(); ++i) {
-+        if (i > 0)                  blocks[i]->prev_free = blocks[i - 1];
-+        if (i + 1 < blocks.size())  blocks[i]->next_free = blocks[i + 1];
-+    }
-+    if (!blocks.empty()) {
-+        fake_head.next_free = blocks.front();
-+        blocks.front()->prev_free = &fake_head;
-+        fake_tail.prev_free = blocks.back();
-+        blocks.back()->next_free = &fake_tail;
-+    } else {
-+        fake_head.next_free = &fake_tail;
-+        fake_tail.prev_free = &fake_head;
-+    }
-+}
-+
-+KVCacheBlock* FreeBlockQueue::popleft() {
-+    KVCacheBlock* first = fake_head.next_free;
-+    if (first == &fake_tail || first == nullptr) {
-+        assert(num_free_blocks == 0);
-+        throw std::runtime_error("No free blocks available");
-+    }
-+    fake_head.next_free = first->next_free;
-+    first->next_free->prev_free = &fake_head;
-+    first->prev_free = first->next_free = nullptr;
-+    num_free_blocks--;
-+    return first;
-+}
-+
-+std::vector<KVCacheBlock*> FreeBlockQueue::popleft_n(size_t n) {
-+    std::vector<KVCacheBlock*> ret;
-+    if (n == 0) return ret;
-+    assert(num_free_blocks >= n);
-+    num_free_blocks -= n;
-+    KVCacheBlock* curr = fake_head.next_free;
-+    ret.reserve(n);
-+    for (size_t i = 0; i < n; ++i) {
-+        assert(curr != nullptr);
-+        ret.push_back(curr);
-+        KVCacheBlock* last = curr;
-+        curr = curr->next_free;
-+        last->prev_free = last->next_free = nullptr;
-+    }
-+    if (curr != nullptr) {
-+        fake_head.next_free = curr;
-+        curr->prev_free = &fake_head;
-+    }
-+    return ret;
-+}
-+
-+void FreeBlockQueue::remove(KVCacheBlock* block) {
-+    if (!block->prev_free || !block->next_free)
-+        throw std::runtime_error("remove() called on an invalid block");
-+    block->prev_free->next_free = block->next_free;
-+    block->next_free->prev_free = block->prev_free;
-+    block->prev_free = block->next_free = nullptr;
-+    num_free_blocks--;
-+}
-+
-+void FreeBlockQueue::append(KVCacheBlock* block) {
-+    KVCacheBlock* last = fake_tail.prev_free;
-+    last->next_free = block;
-+    block->prev_free = last;
-+    block->next_free = &fake_tail;
-+    fake_tail.prev_free = block;
-+    num_free_blocks++;
-+}
-+
-+void FreeBlockQueue::append_n(const std::vector<KVCacheBlock*>& blocks) {
-+    if (blocks.empty()) return;
-+    KVCacheBlock* last = fake_tail.prev_free;
-+    for (KVCacheBlock* b : blocks) {
-+        b->prev_free = last;
-+        last->next_free = b;
-+        last = b;
-+    }
-+    last->next_free = &fake_tail;
-+    fake_tail.prev_free = last;
-+    num_free_blocks += blocks.size();
-+}
-+
-+void FreeBlockQueue::prepend_n(const std::vector<KVCacheBlock*>& blocks) {
-+    if (blocks.empty()) return;
-+    KVCacheBlock* first = fake_head.next_free;
-+    KVCacheBlock* prev = &fake_head;
-+    for (KVCacheBlock* b : blocks) {
-+        b->prev_free = prev;
-+        prev->next_free = b;
-+        prev = b;
-+    }
-+    prev->next_free = first;
-+    first->prev_free = prev;
-+    num_free_blocks += blocks.size();
-+}
-+
-+std::vector<KVCacheBlock*> FreeBlockQueue::get_all_free_blocks() const {
-+    std::vector<KVCacheBlock*> ret;
-+    const KVCacheBlock* curr = fake_head.next_free;
-+    while (curr && curr->next_free != nullptr) {
-+        ret.push_back(const_cast<KVCacheBlock*>(curr));
-+        curr = curr->next_free;
-+    }
-+    return ret;
-+}
-+
-+// ---------------------------------------------------------------------------
-+// BlockPool  (port of block_pool.py)
-+// ---------------------------------------------------------------------------
-+
-+static std::vector<KVCacheBlock*> make_ptrs(std::vector<KVCacheBlock>& v) {
-+    std::vector<KVCacheBlock*> p;
-+    p.reserve(v.size());
-+    for (auto& b : v) p.push_back(&b);
-+    return p;
-+}
-+
-+static std::vector<KVCacheBlock> make_block_vec(int32_t num_blocks) {
-+    std::vector<KVCacheBlock> v;
-+    v.reserve(num_blocks);
-+    for (int32_t i = 0; i < num_blocks; ++i) v.emplace_back(i);
-+    return v;
-+}
-+
-+BlockPool::BlockPool(int32_t num_blocks, bool enable_caching)
-+    : enable_caching_(enable_caching),
-+      blocks_(make_block_vec(num_blocks)),
-+      ptrs_(make_ptrs(blocks_)),
-+      free_queue_(ptrs_) {
-+    // vLLM reserves block_id 0 as the null block (never cached).
-+    null_block = free_queue_.popleft();
-+    null_block->is_null = true;
-+}
-+
-+bool BlockPool::maybe_evict_cached_block(KVCacheBlock* block) {
-+    if (!block->has_hash) return false;
-+    auto it = cached_block_hash_to_block_.find(block->block_hash);
-+    if (it == cached_block_hash_to_block_.end() || it->second != block) return false;
-+    cached_block_hash_to_block_.erase(it);
-+    block->reset_hash();
-+    return true;
-+}
-+
-+std::vector<KVCacheBlock*> BlockPool::get_new_blocks(size_t n) {
-+    if (n > get_num_free_blocks())
-+        throw std::runtime_error("Cannot get free blocks from pool");
-+    auto ret = free_queue_.popleft_n(n);
-+    for (KVCacheBlock* b : ret) {
-+        if (enable_caching_) maybe_evict_cached_block(b);
-+        assert(b->ref_cnt == 0);
-+        b->ref_cnt += 1;
-+    }
-+    return ret;
-+}
-+
-+KVCacheBlock* BlockPool::get_cached_block(uint64_t block_hash) {
-+    auto it = cached_block_hash_to_block_.find(block_hash);
-+    return it == cached_block_hash_to_block_.end() ? nullptr : it->second;
-+}
-+
-+void BlockPool::touch(const std::vector<KVCacheBlock*>& blocks) {
-+    for (KVCacheBlock* b : blocks) {
-+        // ref_cnt==0 means the block is a free-list eviction candidate; pull it out.
-+        if (b->ref_cnt == 0 && !b->is_null) free_queue_.remove(b);
-+        b->ref_cnt += 1;
-+    }
-+}
-+
-+void BlockPool::free_blocks(const std::vector<KVCacheBlock*>& ordered_blocks) {
-+    std::vector<KVCacheBlock*> without_hash, with_hash;
-+    for (KVCacheBlock* b : ordered_blocks) {
-+        if (b->is_null) continue;
-+        b->ref_cnt -= 1;
-+        if (b->ref_cnt == 0) (b->has_hash ? with_hash : without_hash).push_back(b);
-+    }
-+    free_queue_.prepend_n(without_hash); // un-hashed: evicted first (front)
-+    free_queue_.append_n(with_hash);     // hashed: kept warm (tail)
-+}
-+
-+void BlockPool::cache_full_blocks(const std::vector<KVCacheBlock*>& req_blocks,
-+                                  size_t num_cached_blocks, size_t num_full_blocks,
-+                                  const std::vector<uint64_t>& block_hashes) {
-+    for (size_t i = num_cached_blocks; i < num_full_blocks; ++i) {
-+        KVCacheBlock* blk = req_blocks[i];
-+        if (blk->has_hash) continue;
-+        blk->has_hash = true;
-+        blk->block_hash = block_hashes[i];
-+        cached_block_hash_to_block_[blk->block_hash] = blk;
-+    }
-+}
-+
-+// ---------------------------------------------------------------------------
-+// PagedKVManager  (port of SingleTypeKVCacheManager / FullAttentionManager)
-+// ---------------------------------------------------------------------------
-+
-+static inline size_t cdiv(size_t a, size_t b) { return (a + b - 1) / b; }
-+
-+PagedKVManager::PagedKVManager(int32_t num_blocks, int block_size, bool enable_caching)
-+    : block_size_(block_size), pool_(num_blocks, enable_caching) {}
-+
-+bool PagedKVManager::allocate(int seq_id, size_t total_tokens) {
-+    auto& req = req_to_blocks_[seq_id];
-+    size_t need = cdiv(total_tokens, block_size_);
-+    if (need <= req.size()) return true;
-+    size_t add = need - req.size();
-+    if (add > pool_.get_num_free_blocks()) return false; // OOM
-+    auto nb = pool_.get_new_blocks(add);
-+    req.insert(req.end(), nb.begin(), nb.end());
-+    return true;
-+}
-+
-+std::vector<int32_t> PagedKVManager::block_table(int seq_id) const {
-+    std::vector<int32_t> bt;
-+    auto it = req_to_blocks_.find(seq_id);
-+    if (it == req_to_blocks_.end()) return bt;
-+    bt.reserve(it->second.size());
-+    for (KVCacheBlock* b : it->second) bt.push_back(b->block_id);
-+    return bt;
-+}
-+
-+int64_t PagedKVManager::slot(int seq_id, int pos) const {
-+    const auto& req = req_to_blocks_.at(seq_id);
-+    int32_t phys = req[pos / block_size_]->block_id;
-+    return (int64_t)phys * block_size_ + (pos % block_size_);
-+}
-+
-+std::vector<int64_t> PagedKVManager::slot_mapping(int seq_id, const std::vector<int>& positions) const {
-+    std::vector<int64_t> sm;
-+    sm.reserve(positions.size());
-+    for (int p : positions) sm.push_back(slot(seq_id, p));
-+    return sm;
-+}
-+
-+void PagedKVManager::free(int seq_id) {
-+    auto it = req_to_blocks_.find(seq_id);
-+    if (it == req_to_blocks_.end()) return;
-+    // Free in reverse so the tail of the block chain is evicted first (vLLM order).
-+    std::vector<KVCacheBlock*> ordered(it->second.rbegin(), it->second.rend());
-+    pool_.free_blocks(ordered);
-+    req_to_blocks_.erase(it);
-+}
-+
-+// FNV-1a chained block hash. Deterministic and prefix-sensitive; folds the parent
-+// hash into the seed so each block hash transitively encodes its whole prefix
-+// (behavioral parity with vLLM hash_block_tokens chaining; vLLM uses sha256 bytes).
-+uint64_t PagedKVManager::hash_block(uint64_t parent_hash, const std::vector<int>& token_ids) {
-+    uint64_t h = 1469598103934665603ull ^ parent_hash;
-+    for (int t : token_ids) {
-+        h ^= (uint64_t)(uint32_t)t;
-+        h *= 1099511628211ull;
-+    }
-+    if (h == 0) h = 0x9e3779b97f4a7c15ull; // never 0 (0 reads as "no hash")
-+    return h;
-+}
-+
-+std::vector<uint64_t> PagedKVManager::compute_block_hashes(const std::vector<int>& token_ids) const {
-+    std::vector<uint64_t> hashes;
-+    uint64_t parent = 0; // NONE_HASH analogue
-+    size_t n_full = token_ids.size() / block_size_;
-+    for (size_t i = 0; i < n_full; ++i) {
-+        std::vector<int> blk(token_ids.begin() + i * block_size_,
-+                             token_ids.begin() + (i + 1) * block_size_);
-+        parent = hash_block(parent, blk);
-+        hashes.push_back(parent);
-+    }
-+    return hashes;
-+}
-+
-+size_t PagedKVManager::get_computed_blocks(const std::vector<uint64_t>& block_hashes) {
-+    std::vector<KVCacheBlock*> hits;
-+    for (uint64_t bh : block_hashes) {        // stop at first miss (prefix property)
-+        KVCacheBlock* cb = pool_.get_cached_block(bh);
-+        if (!cb) break;
-+        hits.push_back(cb);
-+    }
-+    pool_.touch(hits);                        // ++ref_cnt, pull from free list
-+    return hits.size() * (size_t)block_size_;
-+}
-+
-+void PagedKVManager::cache_blocks(int seq_id, const std::vector<uint64_t>& block_hashes, size_t num_tokens) {
-+    auto& req = req_to_blocks_[seq_id];
-+    size_t n_full = num_tokens / block_size_;
-+    pool_.cache_full_blocks(req, /*num_cached=*/0, n_full, block_hashes);
-+}
-+
-+} // namespace paged
-diff --git a/src/paged-kv-manager.h b/src/paged-kv-manager.h
-new file mode 100644
-index 000000000..740280a7f
---- /dev/null
-+++ b/src/paged-kv-manager.h
-@@ -0,0 +1,108 @@
-+#pragma once
-+// Paged KV cache block manager for llama.cpp (CPU-first prototype).
-+//
-+// Host-side block management is a faithful port of vLLM V1:
-+//   vllm/v1/core/kv_cache_utils.py            (KVCacheBlock, FreeKVCacheBlockQueue, hash_block_tokens)
-+//   vllm/v1/core/block_pool.py                (BlockPool: get_new_blocks/touch/free/evict/cache_full_blocks)
-+//   vllm/v1/core/single_type_kv_cache_manager.py (allocate_new_blocks, find_longest_cache_hit)
-+//
-+// Parity is on behavior/algorithm (block chaining, first-miss stop, ref-counting,
-+// LRU eviction order), not on exact hash bytes. This unit has zero ggml/llama.cpp
-+// dependency so it can be unit-tested in isolation.
-+
-+#include <cstdint>
-+#include <vector>
-+#include <unordered_map>
-+#include <map>
-+
-+namespace paged {
-+
-+// vLLM KVCacheBlock (kv_cache_utils.py).
-+struct KVCacheBlock {
-+    int32_t  block_id   = 0;
-+    int      ref_cnt    = 0;
-+    bool     has_hash   = false;   // vLLM: _block_hash is set only when full+cached
-+    uint64_t block_hash = 0;
-+    bool     is_null    = false;
-+    KVCacheBlock* prev_free = nullptr;
-+    KVCacheBlock* next_free = nullptr;
-+
-+    explicit KVCacheBlock(int32_t id = 0) : block_id(id) {}
-+    void reset_hash() { has_hash = false; block_hash = 0; }
-+};
-+
-+// Intrusive doubly-linked free list with fake head/tail (vLLM FreeKVCacheBlockQueue).
-+// O(1) middle removal is required so touch() can pull a warm cached block out of the
-+// free list when a later request hits its prefix.
-+class FreeBlockQueue {
-+public:
-+    size_t num_free_blocks = 0;
-+
-+    explicit FreeBlockQueue(const std::vector<KVCacheBlock*>& blocks);
-+    KVCacheBlock* popleft();
-+    std::vector<KVCacheBlock*> popleft_n(size_t n);
-+    void remove(KVCacheBlock* block);
-+    void append(KVCacheBlock* block);
-+    void append_n(const std::vector<KVCacheBlock*>& blocks);
-+    void prepend_n(const std::vector<KVCacheBlock*>& blocks);
-+    std::vector<KVCacheBlock*> get_all_free_blocks() const;
-+
-+private:
-+    KVCacheBlock fake_head{-1};
-+    KVCacheBlock fake_tail{-1};
-+};
-+
-+// vLLM BlockPool (block_pool.py).
-+class BlockPool {
-+public:
-+    KVCacheBlock* null_block = nullptr;
-+
-+    BlockPool(int32_t num_blocks, bool enable_caching);
-+    std::vector<KVCacheBlock*> get_new_blocks(size_t n);
-+    KVCacheBlock* get_cached_block(uint64_t block_hash);
-+    void touch(const std::vector<KVCacheBlock*>& blocks);
-+    void free_blocks(const std::vector<KVCacheBlock*>& ordered_blocks);
-+    void cache_full_blocks(const std::vector<KVCacheBlock*>& req_blocks,
-+                           size_t num_cached_blocks, size_t num_full_blocks,
-+                           const std::vector<uint64_t>& block_hashes);
-+    size_t get_num_free_blocks() const { return free_queue_.num_free_blocks; }
-+
-+private:
-+    bool maybe_evict_cached_block(KVCacheBlock* block);
-+
-+    bool enable_caching_;
-+    std::vector<KVCacheBlock> blocks_;     // owns all block descriptors
-+    std::vector<KVCacheBlock*> ptrs_;
-+    FreeBlockQueue free_queue_;
-+    // vLLM stores hash -> {block_id: block} to allow duplicate-content blocks; the
-+    // prototype keeps the last writer (single KV-cache group is sufficient for the wins).
-+    std::unordered_map<uint64_t, KVCacheBlock*> cached_block_hash_to_block_;
-+};
-+
-+// Allocation + prefix-caching surface, ported from SingleTypeKVCacheManager /
-+// FullAttentionManager. Single KV-cache group; no extra_keys / eagle / spec-decode.
-+class PagedKVManager {
-+public:
-+    PagedKVManager(int32_t num_blocks, int block_size, bool enable_caching);
-+
-+    // Grow seq_id to cover total_tokens slots. Returns false on OOM (free queue empty).
-+    bool allocate(int seq_id, size_t total_tokens);
-+    std::vector<int32_t> block_table(int seq_id) const;
-+    int64_t slot(int seq_id, int pos) const;
-+    std::vector<int64_t> slot_mapping(int seq_id, const std::vector<int>& positions) const;
-+    void free(int seq_id);
-+    int block_size() const { return block_size_; }
-+
-+    // Prefix caching (win 3).
-+    static uint64_t hash_block(uint64_t parent_hash, const std::vector<int>& token_ids);
-+    std::vector<uint64_t> compute_block_hashes(const std::vector<int>& token_ids) const;
-+    size_t get_computed_blocks(const std::vector<uint64_t>& block_hashes); // returns num cached tokens
-+    void cache_blocks(int seq_id, const std::vector<uint64_t>& block_hashes, size_t num_tokens);
-+
-+protected:
-+    int block_size_;
-+    BlockPool pool_;
-+    std::map<int, std::vector<KVCacheBlock*>> req_to_blocks_;
-+};
-+
-+} // namespace paged
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0002-paged-kv-block-placement-env-LLAMA_KV_PAGED.patch

@@ -1,75 +0,0 @@
-From 5c9c709e6c6b07e0399b75fd4e46e752d418a9a8 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Fri, 19 Jun 2026 23:04:17 +0000
-Subject: [PATCH] paged kv block placement (env LLAMA_KV_PAGED)
-
-Place each sequence's tokens at permuted, non-contiguous fixed-size block
-positions in find_slot, proving attention is invariant to physical KV placement
-(token-identical greedy generation). Default off; single-sequence scope; falls
-back to the normal allocator. The paged-placement substrate for the gather-read.
----
- src/llama-kv-cache.cpp | 41 +++++++++++++++++++++++++++++++++++++++++
- 1 file changed, 41 insertions(+)
-
-diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
-index 2802103bd..999e2ae61 100644
---- a/src/llama-kv-cache.cpp
-+++ b/src/llama-kv-cache.cpp
-@@ -11,6 +11,8 @@
- #include <cstring>
- #include <limits>
- #include <map>
-+#include <numeric>
-+#include <cstdlib>
- #include <stdexcept>
- 
- static bool ggml_is_power_of_2(int n) {
-@@ -1020,6 +1022,45 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch,
-             return { };
-         }
- 
-+        // [paged, experimental] Place this sequence's tokens at permuted,
-+        // non-contiguous fixed-size BLOCK positions instead of a contiguous run.
-+        // This validates that attention is invariant to physical KV placement -
-+        // the correctness premise of paged attention. Enabled via LLAMA_KV_PAGED.
-+        // Single-sequence scope (uses get_used() as the logical base); falls back
-+        // to the normal allocator if the permuted cells aren't available.
-+        static const bool paged_mode = (std::getenv("LLAMA_KV_PAGED") != nullptr);
-+        if (paged_mode) {
-+            const uint32_t bs   = 16;                 // block size (tokens/block)
-+            const uint32_t nblk = cells.size() / bs;  // blocks in this stream's pool
-+            if (nblk >= 2) {
-+                // stride coprime to nblk => block-index permutation is a bijection
-+                uint32_t k = 1;
-+                for (uint32_t cand = (nblk / 2) | 1u; cand < nblk; cand += 2) {
-+                    if (std::gcd(cand, nblk) == 1u) { k = cand; break; }
-+                }
-+                const uint32_t base = cells.get_used();
-+                bool ok = true;
-+                for (uint32_t i = 0; i < n_tokens; ++i) {
-+                    const uint32_t L    = base + i;
-+                    const uint32_t b    = L / bs;
-+                    const uint32_t off  = L % bs;
-+                    if (b >= nblk) { ok = false; break; }
-+                    const uint32_t phys = ((b * k) % nblk) * bs + off; // permuted block
-+                    if (phys >= cells.size() || !cells.is_empty(phys)) { ok = false; break; }
-+                    res.idxs[s].push_back(phys);
-+                }
-+                if (ok && res.idxs[s].size() == n_tokens) {
-+                    if (std::getenv("LLAMA_KV_PAGED_DEBUG")) {
-+                        fprintf(stderr, "[paged] seq placed %u tok at cells:", n_tokens);
-+                        for (uint32_t z = 0; z < res.idxs[s].size() && z < 24; ++z) fprintf(stderr, " %u", res.idxs[s][z]);
-+                        fprintf(stderr, " (k=%u nblk=%u base=%u)\n", k, nblk, base);
-+                    }
-+                    continue; // paged placement succeeded for this sequence
-+                }
-+                res.idxs[s].clear(); // fall back to the normal allocator
-+            }
-+        }
-+
-         uint32_t n_tested = 0;
- 
-         // for continuous slots, we test that all tokens in the ubatch fit, starting from the current head
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0003-gather-read-plan.md

@@ -1,102 +0,0 @@
-# Patch 0003 — paged gather-read: exact implementation plan
-
-**Goal:** a sequence attends only its own (compacted) cells via `ggml_get_rows`, instead of the scattered
-`[0,n_kv)` window. Token-identical (attention is permutation-invariant over the KV set). **Gated**: stock
-path stays byte-identical (no new ops unless `LLAMA_KV_PAGED`).
-
-**Base:** applies on top of 0001+0002 at the pin. Dev tree: `backend/cpp/llama-cpp-paged-dev` (branch `paged`).
-
-## Design
-
-The gather is keyed off one runtime index list (the sequence's used cells, in a fixed order), exposed as a
-graph input (mirroring `k_idxs`). In `build_attn`, gather K, V **and the kq_mask** by that same index, so all
-three stay aligned. `n_gathered` replaces `n_kv` for the attention. Only active when the cache is in paged
-mode (a new `is_paged()` flag set when `LLAMA_KV_PAGED`/find_slot used permuted placement).
-
-ggml note: `ggml_get_rows(a,b)` gathers `a`'s **ne1** by `b` (I32). Raw K is `[n_embd_k_gqa, kv_size, n_stream]`
-→ ne1 = cells → direct. The mask is `[n_kv, n_tokens, 1, n_stream]` → n_kv is **ne0**, so gather as
-`transpose → get_rows → transpose`.
-
-### KEY CORRECTIONS (found while implementing — these change the edits)
-
-1. **Gather index = ALL used (non-empty) cells in `[0,n_kv)`, NOT `sinfo.idxs`.** `sinfo.idxs` is only the
-   *current ubatch's write slots*; attention reads the *full history*. The query set per token is masked by
-   `kq_mask`, so gathering the union of all used cells + gathering the mask the same way is token-identical
-   and drops exactly the empty (already-masked) cells. So: `gather = { i in [0,n_kv) : !cells.is_empty(i) }`.
-
-2. **Static-graph size is fine because llama.cpp rebuilds the graph every ubatch.** `n_gather` (used-cell
-   count) is therefore a build-time constant for that ubatch — `build_input_gather_idxs` sizes the I32
-   tensor to `get_n_gather()` computed at build, `set_input_gather_idxs` fills the identical cell list. They
-   MUST use the same loop (`for i in [0,n_kv): if !is_empty(i) push i`) so build-order == fill-order.
-
-3. **K/V gather can live entirely in `build_attn`, no cache get_k change.** The `get_k` 4d view is contiguous
-   in `[ne0,ne1,ne2]` from cell 0 (nb2 == n_embd_head*n_head_kv*elemsz), so for **single stream (ns==1)**:
-   `reshape_3d(k, n_embd_head*n_head_kv, n_kv, 1) → get_rows(., gi) → reshape_4d(., n_embd_head, n_head_kv, n_gather, 1)`.
-   Multi-stream (ns>1) breaks contiguity (nb3 uses kv_size) → gate to ns==1 first, multi-stream follow-up.
-
-4. So the ONLY cache additions are `is_paged()`, `get_n_gather(n_kv)`, `build/set_input_gather_idxs(n_kv)`;
-   everything else (K/V/mask gather) is in `build_attn`. `set_input_kq_mask` is **unchanged** (built over
-   n_kv, then gathered). Smaller than the 7-edit estimate above.
-
-## Edits
-
-### 1. `src/llama-kv-cache.h` — declare gather infra (in `llama_kv_cache`)
-```cpp
-    bool        is_paged() const { return paged_active; }            // near get_size()
-    ggml_tensor * build_input_gather_idxs(ggml_context * ctx, const slot_info & sinfo) const;
-    void          set_input_gather_idxs (ggml_tensor * dst, const slot_info & sinfo) const;
-    uint32_t      get_n_gather(const slot_info & sinfo) const;       // == sum of used cells gathered
-```
-Add member `mutable bool paged_active = false;` and in `llama_kv_cache_context` forward the three (like
-`build_input_k_idxs`/`get_n_kv`).
-
-### 2. `src/llama-kv-cache.cpp`
-- In `find_slot`, in the paged branch (0002), set `paged_active = true;` on success.
-- `get_n_gather(sinfo)` = `sinfo.idxs[0].size()` summed over streams (the count actually placed).
-- `build_input_gather_idxs`: `ggml_new_tensor_1d(ctx, GGML_TYPE_I32, get_n_gather(sinfo)); ggml_set_input(...)`.
-- `set_input_gather_idxs`: fill `data[k++] = strm_off + sinfo.idxs[s][i]` for every placed cell (same order
-  the mask/k/v will see). This is the canonical gather order.
-
-### 3. `src/llama-graph.h` — `llm_graph_input_attn_kv`
-Add `ggml_tensor * gather_idxs = nullptr;` + `ggml_tensor * get_gather_idxs() const { return gather_idxs; }`.
-
-### 4. `src/llama-graph.cpp`
-- `llm_graph_input_attn_kv::set_input`: if `mctx->is_paged()` → `mctx->set_input_gather_idxs(gather_idxs, ...)`.
-- `build_attn_inp_kv` (creates the input): if `mctx_cur->is_paged()` → `inp->gather_idxs =
-  mctx_cur->build_input_gather_idxs(ctx0, ...)`.
-- `build_attn` (the kv overload, ~2356): after `k`,`v`,`kq_mask`:
-```cpp
-if (ggml_tensor * gi = inp->get_gather_idxs()) {
-    k = ggml_get_rows(ctx0, k, gi);                                   // [d, n_gather, ...] (reshape view ok)
-    v = v_trans ? /* gather columns */ : ggml_get_rows(ctx0, v, gi);
-    ggml_tensor * m = ggml_cont(ctx0, ggml_transpose(ctx0, kq_mask)); // [n_tokens, n_kv]
-    m = ggml_get_rows(ctx0, m, gi);                                   // [n_tokens, n_gather]
-    kq_mask = ggml_cont(ctx0, ggml_transpose(ctx0, m));              // [n_gather, n_tokens]
-}
-ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
-```
-Note: `get_k` returns the reshaped 4d view; gather must run on a cell-major shape. Simplest: add a paged
-variant `get_k(ctx,il)` that returns `ggml_get_rows` of the **raw** `layers[ikv].k` then reshapes to
-`[n_embd_head, n_head_kv, n_gather, ns]`. Do the gather in the cache, not the graph, for K/V; keep only the
-mask gather in the graph. (Cleaner — revisit during impl.)
-
-### 5. V-transposed path
-When `!flash_attn`, V is stored transposed `[kv_size, n_embd_v_gqa]`; gather its **rows** (ne1 = n_embd) won't
-work — gather columns via the same idx on the non-transposed store, OR force `is_paged()` to require
-flash-attn for the first cut (`GGML_ASSERT`) and handle v_trans in a follow-up.
-
-## Verification (the gate)
-```sh
-cmake --build build-cpu --target llama-simple -j
-M=Qwen3-0.6B.Q4_K_M.gguf ; P="<the 0002 prompt>"
-build-cpu/bin/llama-simple -m $M -n 64 "$P" > a.txt                    # stock
-LLAMA_KV_PAGED=1 build-cpu/bin/llama-simple -m $M -n 64 "$P" > b.txt   # paged gather-read
-diff a.txt b.txt        # MUST be identical
-```
-Also assert (debug) that `n_gather < n_kv` on a multi-chunk sequence (proves compaction, not identity).
-Export only when identical: `git format-patch HEAD~1 -o patches/ --start-number 3 -N`.
-
-## Risks
-- Mask transpose/layout: if `b.txt` diverges, dump the gathered mask vs expected for token 0; off-by-order
-  means the `set_input_gather_idxs` order ≠ the get_k gather order — they MUST use the identical loop.
-- flash-attn vs not: do flash-attn first (simpler mask), then v_trans.

backend/cpp/llama-cpp/patches/paged/0003-paged-gather-read-env-LLAMA_KV_PAGED.patch

@@ -1,369 +0,0 @@
-From c1de00f4cc1eb0dd25993880bb4c8562be1937d4 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Mon, 22 Jun 2026 10:24:22 +0200
-Subject: [PATCH] paged gather-read (env LLAMA_KV_PAGED) - patch 0003
-
-Gather K, V and the kq_mask down to each sequence stream's non-empty cells
-before build_attn_mha. Position-sorted per stream so the flash-attn online
-softmax reduction order matches stock byte-for-byte. Multi-stream: one index
-column per stream over k->ne[3], padded to the max non-empty count with a
-masked (empty) cell. Gated behind LLAMA_KV_PAGED; no-op when unset.
----
- src/CMakeLists.txt     |   1 +
- src/llama-graph.cpp    |   9 ++-
- src/llama-kv-cache.cpp |  74 ++++++++++++++++++++++++
- src/llama-kv-cache.h   |  11 ++++
- src/paged-attn.cpp     | 128 +++++++++++++++++++++++++++++++++++++++++
- src/paged-attn.h       |  40 +++++++++++++
- 6 files changed, 262 insertions(+), 1 deletion(-)
- create mode 100644 src/paged-attn.cpp
- create mode 100644 src/paged-attn.h
-
-diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
-index a030940..58083b3 100644
---- a/src/CMakeLists.txt
-+++ b/src/CMakeLists.txt
-@@ -25,6 +25,7 @@ add_library(llama
-             llama-kv-cache.cpp
-             llama-kv-cache-iswa.cpp
-             paged-kv-manager.cpp
-+            paged-attn.cpp
-             llama-kv-cache-dsa.cpp
-             llama-memory.cpp
-             llama-memory-hybrid.cpp
-diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
-index 68c9e60..b59d2a5 100644
---- a/src/llama-graph.cpp
-+++ b/src/llama-graph.cpp
-@@ -6,6 +6,8 @@
- #include "llama-cparams.h"
- 
- #include "llama-kv-cache.h"
-+
-+#include "paged-attn.h"
- #include "llama-kv-cache-iswa.h"
- #include "llama-kv-cache-dsa.h"
- #include "llama-memory-hybrid.h"
-@@ -2356,7 +2358,12 @@ ggml_tensor * llm_graph_context::build_attn(
-     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
-     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
- 
--    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
-+    // [paged 0003] gather K, V and the mask to the sequence's used cells only
-+    //   (no-op unless env LLAMA_KV_PAGED is set).
-+    ggml_tensor * kq_mask_g = kq_mask;
-+    paged_attn::gather(ctx0, res, mctx_cur, &k, &v, &kq_mask_g);
-+
-+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask_g, sinks, v_mla, kq_scale, il);
-     cb(cur, "kqv_out", il);
- 
-     if (inp->self_v_rot) {
-diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
-index 999e2ae..30d02d7 100644
---- a/src/llama-kv-cache.cpp
-+++ b/src/llama-kv-cache.cpp
-@@ -1,4 +1,6 @@
- #include "llama-kv-cache.h"
-+#include <vector>
-+#include <utility>
- 
- #include "llama-impl.h"
- #include "llama-io.h"
-@@ -1329,6 +1331,70 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k
-             ggml_row_size(v->type, kv_size*n_embd_v_gqa)*sinfo.s0);
- }
- 
-+// [paged 0003] gather-read: enumerate the non-empty cells in [0, n_kv) for the
-+// single stream addressed by sinfo. With paged placement (patch 0002) these are
-+// the sequence's scattered block cells; gathering K/V/mask by this index list
-+// compacts the attention read while preserving every unmasked (token,cell) pair.
-+uint32_t llama_kv_cache::get_n_gather(uint32_t n_kv, const slot_info & sinfo) const {
-+    // Multi-stream: the gathered K/V/mask tensors are rectangular [.., n_gather,
-+    // n_stream], so n_gather is the MAX non-empty count across the batch streams.
-+    // Streams with fewer cells are padded (see get_gather_idxs) with a masked
-+    // (empty) cell index, which contributes exp(-inf)=0 and is thus a no-op.
-+    // K is laid out over physical streams [s0, s1]; index v_cells the same way.
-+    const uint32_t ns = sinfo.s1 - sinfo.s0 + 1;
-+    uint32_t mx = 0;
-+    for (uint32_t j = 0; j < ns; ++j) {
-+        const auto & cells = v_cells[sinfo.s0 + j];
-+        const uint32_t n = std::min<uint32_t>(n_kv, cells.size());
-+        uint32_t cnt = 0;
-+        for (uint32_t i = 0; i < n; ++i) {
-+            if (!cells.is_empty(i)) {
-+                ++cnt;
-+            }
-+        }
-+        mx = std::max(mx, cnt);
-+    }
-+    return mx;
-+}
-+
-+void llama_kv_cache::get_gather_idxs(int32_t * dst, uint32_t n_kv, const slot_info & sinfo) const {
-+    const uint32_t ns       = sinfo.s1 - sinfo.s0 + 1;
-+    const uint32_t n_gather = get_n_gather(n_kv, sinfo);
-+    // dst is [n_gather, n_stream] (ne0 = n_gather): column s at dst[s*n_gather..].
-+    for (uint32_t j = 0; j < ns; ++j) {
-+        const auto & cells = v_cells[sinfo.s0 + j];
-+        const uint32_t n = std::min<uint32_t>(n_kv, cells.size());
-+        // Collect the non-empty cells, then order them by token POSITION (not by
-+        // physical cell index). The attention reduction (flash-attn online
-+        // softmax, and the non-flash soft_max) runs over cells in array order and
-+        // is order-sensitive in floating point. Stock (contiguous) placement
-+        // happens to store cells in position order, so emitting the gathered
-+        // indices in position order reproduces stock's exact reduction order -
-+        // making the paged read bit-identical, not merely math-equivalent.
-+        std::vector<std::pair<llama_pos, int32_t>> pc;
-+        pc.reserve(n);
-+        int32_t pad = -1;
-+        for (uint32_t i = 0; i < n; ++i) {
-+            if (!cells.is_empty(i)) {
-+                pc.emplace_back(cells.pos_get(i), (int32_t) i);
-+            } else if (pad < 0) {
-+                pad = (int32_t) i; // first empty cell: its mask is -inf -> safe pad
-+            }
-+        }
-+        std::sort(pc.begin(), pc.end());
-+        int32_t * col = dst + (size_t) j * n_gather;
-+        for (size_t k = 0; k < pc.size(); ++k) {
-+            col[k] = pc[k].second;
-+        }
-+        // Pad the tail to n_gather with a masked (empty) cell so the rectangular
-+        // gather drops to zero contribution for streams shorter than the max.
-+        const int32_t padv = (pad >= 0) ? pad : (pc.empty() ? 0 : pc.back().second);
-+        for (uint32_t k = (uint32_t) pc.size(); k < n_gather; ++k) {
-+            col[k] = padv;
-+        }
-+    }
-+}
-+
- ggml_tensor * llama_kv_cache::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
-     GGML_UNUSED(sinfo);
- 
-@@ -2620,6 +2686,14 @@ ggml_tensor * llama_kv_cache_context::get_v(ggml_context * ctx, int32_t il) cons
-     return kv->get_v(ctx, il, n_kv, sinfos[i_cur]);
- }
- 
-+uint32_t llama_kv_cache_context::get_n_gather() const {
-+    return kv->get_n_gather(n_kv, sinfos[i_cur]);
-+}
-+
-+void llama_kv_cache_context::get_gather_idxs(int32_t * dst) const {
-+    kv->get_gather_idxs(dst, n_kv, sinfos[i_cur]);
-+}
-+
- ggml_tensor * llama_kv_cache_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
-     return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]);
- }
-diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
-index 3d68f98..494c0fb 100644
---- a/src/llama-kv-cache.h
-+++ b/src/llama-kv-cache.h
-@@ -171,6 +171,12 @@ public:
-     ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
-     ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
- 
-+    // [paged 0003] count / list the non-empty cells in [0, n_kv) per stream of
-+    //   sinfo (position-sorted, padded across streams). Used by paged-attn
-+    //   gather-read. get_n_gather returns the max count across streams.
-+    uint32_t get_n_gather(uint32_t n_kv, const slot_info & sinfo) const;
-+    void     get_gather_idxs(int32_t * dst, uint32_t n_kv, const slot_info & sinfo) const;
-+
-     // store k_cur and v_cur in the cache based on the provided head location
-     ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const;
-     ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const;
-@@ -368,6 +374,11 @@ public:
-     ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
-     ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
- 
-+    // [paged 0003] gather-read helpers (delegate to the kv cache for the
-+    //   current ubatch's stream).
-+    uint32_t get_n_gather() const;
-+    void     get_gather_idxs(int32_t * dst) const;
-+
-     // store k_cur and v_cur in the cache based on the provided head location
-     // note: the heads in k_cur and v_cur should be laid out contiguously in memory
-     //   - k_cur  [n_embd_head_k, n_head_k, n_tokens]
-diff --git a/src/paged-attn.cpp b/src/paged-attn.cpp
-new file mode 100644
-index 0000000..ade75e8
---- /dev/null
-+++ b/src/paged-attn.cpp
-@@ -0,0 +1,128 @@
-+#include "paged-attn.h"
-+
-+#include "llama-graph.h"
-+#include "llama-kv-cache.h"
-+
-+#include "ggml.h"
-+#include "ggml-backend.h"
-+
-+#include <cstdlib>
-+#include <cstdio>
-+
-+namespace paged_attn {
-+
-+bool active() {
-+    static const bool a = (std::getenv("LLAMA_KV_PAGED") != nullptr);
-+    return a;
-+}
-+
-+static bool debug() {
-+    static const bool d = (std::getenv("LLAMA_KV_PAGED_DEBUG") != nullptr);
-+    return d;
-+}
-+
-+namespace {
-+
-+// Graph input that, at set_input time, fills an I32 [n_gather, n_stream] tensor
-+// with each stream's non-empty cell indices (position-sorted, padded with a
-+// masked/empty cell) by delegating to the kv-cache context. Private to this
-+// unit; default can_reuse()==false keeps the graph from being reused across
-+// decodes (n_gather grows every step).
-+class input_gather_idxs : public llm_graph_input_i {
-+public:
-+    input_gather_idxs(const llama_kv_cache_context * mctx, ggml_tensor * idxs)
-+        : mctx(mctx), idxs(idxs) {}
-+
-+    void set_input(const llama_ubatch * ubatch) override {
-+        GGML_UNUSED(ubatch);
-+        GGML_ASSERT(idxs && ggml_backend_buffer_is_host(idxs->buffer));
-+        mctx->get_gather_idxs((int32_t *) idxs->data);
-+    }
-+
-+    const llama_kv_cache_context * mctx;
-+    ggml_tensor * idxs;
-+};
-+
-+} // namespace
-+
-+void gather(ggml_context * ctx0,
-+            llm_graph_result * res,
-+            const llama_kv_cache_context * mctx,
-+            ggml_tensor ** k,
-+            ggml_tensor ** v,
-+            ggml_tensor ** kq_mask) {
-+    if (!active()) {
-+        return;
-+    }
-+
-+    ggml_tensor * K = *k;
-+    ggml_tensor * V = *v;
-+    ggml_tensor * M = *kq_mask;
-+
-+    // Number of streams (sequences) in the unified batch. K is laid out
-+    // [d, h, n_kv, n_stream] and the mask is [n_kv, n_tps, 1, n_stream]; the
-+    // gather is per-stream (one index column per stream), so a single
-+    // ggml_get_rows over the stream axis handles 1..N streams uniformly.
-+    const int64_t n_stream = K->ne[3];
-+    GGML_ASSERT(M->ne[3] == n_stream);
-+
-+    const int64_t n_gather = (int64_t) mctx->get_n_gather();
-+    if (n_gather <= 0) {
-+        // Worst-case graph reserve (empty cache) or nothing placed yet: leave
-+        // the full [0, n_kv) read untouched so buffer sizing stays worst-case.
-+        return;
-+    }
-+
-+    if (debug()) {
-+        static int64_t once = 0;
-+        if (once++ < 2) {
-+            fprintf(stderr, "[paged-attn] gather n_stream=%lld n_kv=%lld n_gather=%lld\n",
-+                    (long long) n_stream, (long long) K->ne[2], (long long) n_gather);
-+        }
-+    }
-+
-+    // Per-stream index tensor [n_gather, n_stream], filled at set_input from
-+    // each stream's non-empty cells. ggml_get_rows broadcasts along ne[1]==
-+    // n_stream, so column s gathers from stream s of the source.
-+    ggml_tensor * idx = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_gather, n_stream);
-+    ggml_set_input(idx);
-+    res->add_input(llm_graph_input_ptr(new input_gather_idxs(mctx, idx)));
-+
-+    // --- gather K: collapse (head_dim, n_head) so cells become the row axis ---
-+    {
-+        ggml_tensor * t = ggml_cont(ctx0, K);                                          // [d, h, n_kv, ns]
-+        t = ggml_reshape_3d(ctx0, t, K->ne[0]*K->ne[1], K->ne[2], n_stream);           // [d*h, n_kv, ns]
-+        t = ggml_get_rows(ctx0, t, idx);                                               // [d*h, n_gather, ns]
-+        *k = ggml_reshape_4d(ctx0, t, K->ne[0], K->ne[1], n_gather, n_stream);         // [d, h, n_gather, ns]
-+    }
-+
-+    // --- gather V ---
-+    // Normalize to a non-transposed [d, h, n_kv, ns] view first, so the gathered
-+    // result is contiguous and build_attn_mha sees a consistent v_trans==false.
-+    {
-+        const bool v_trans = V->nb[1] > V->nb[2];
-+        ggml_tensor * vsrc = v_trans
-+            ? ggml_permute(ctx0, V, 2, 1, 0, 3)   // [n_kv, h, d, ns] -> [d, h, n_kv, ns]
-+            : V;                                  // already [d, h, n_kv, ns]
-+        ggml_tensor * t = ggml_cont(ctx0, vsrc);                                       // [d, h, n_kv, ns]
-+        t = ggml_reshape_3d(ctx0, t, vsrc->ne[0]*vsrc->ne[1], vsrc->ne[2], n_stream);  // [d*h, n_kv, ns]
-+        t = ggml_get_rows(ctx0, t, idx);                                               // [d*h, n_gather, ns]
-+        *v = ggml_reshape_4d(ctx0, t, vsrc->ne[0], vsrc->ne[1], n_gather, n_stream);   // [d, h, n_gather, ns]
-+    }
-+
-+    // --- gather mask (cells are ne0): transpose so cells become the row axis,
-+    //     gather per stream, transpose back ---
-+    {
-+        ggml_tensor * m = ggml_reshape_3d(ctx0, M, M->ne[0], M->ne[1], n_stream);      // [n_kv, n_tps, ns]
-+        m = ggml_cont(ctx0, ggml_transpose(ctx0, m));                                  // [n_tps, n_kv, ns]
-+        m = ggml_get_rows(ctx0, m, idx);                                               // [n_tps, n_gather, ns] (F32)
-+        m = ggml_cont(ctx0, ggml_transpose(ctx0, m));                                  // [n_gather, n_tps, ns]
-+        m = ggml_reshape_4d(ctx0, m, n_gather, M->ne[1], 1, n_stream);
-+        if (M->type != m->type) {
-+            m = ggml_cast(ctx0, m, M->type);   // flash-attn requires an F16 mask
-+        }
-+        *kq_mask = m;
-+    }
-+}
-+
-+} // namespace paged_attn
-diff --git a/src/paged-attn.h b/src/paged-attn.h
-new file mode 100644
-index 0000000..c5b7bd7
---- /dev/null
-+++ b/src/paged-attn.h
-@@ -0,0 +1,40 @@
-+#pragma once
-+// Paged attention gather-read (patch 0003, experimental).
-+//
-+// Companion to the paged block placement in llama_kv_cache::find_slot (patch
-+// 0002). Patch 0002 places a sequence's tokens at permuted, non-contiguous
-+// fixed-size block cells, but attention still reads the whole [0, n_kv) window
-+// (empty cells masked to -inf). This unit compacts that read: it gathers K, V
-+// and the kq_mask down to ONLY the sequence's used (non-empty) cells before
-+// build_attn_mha.
-+//
-+// Correctness: attention is permutation-invariant over the KV set, and dropping
-+// already-masked empty cells removes only exp(-inf)=0 terms - so greedy output
-+// is identical to stock. Gated behind env LLAMA_KV_PAGED; a no-op when unset.
-+//
-+// All logic lives here to keep the core files additive: build_attn gets one
-+// call, llama_kv_cache_context gets two thin accessors, CMake gets one line.
-+
-+#include <cstdint>
-+
-+struct ggml_context;
-+struct ggml_tensor;
-+class  llm_graph_result;
-+class  llama_kv_cache_context;
-+
-+namespace paged_attn {
-+
-+// true iff env LLAMA_KV_PAGED is set (evaluated once).
-+bool active();
-+
-+// Gather K, V and the kq_mask down to the current sequence's non-empty cells.
-+// No-op (returns immediately) unless active(). On return *k, *v and *kq_mask
-+// point at the compacted tensors; pass them straight to build_attn_mha.
-+void gather(ggml_context * ctx0,
-+            llm_graph_result * res,
-+            const llama_kv_cache_context * mctx,
-+            ggml_tensor ** k,
-+            ggml_tensor ** v,
-+            ggml_tensor ** kq_mask);
-+
-+} // namespace paged_attn
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0004-paged-on-demand-block-allocation-env-LLAMA_KV_PAGED.patch

@@ -1,298 +0,0 @@
-From 7c294973de28d1ac991505638d726acfb371d541 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Mon, 22 Jun 2026 10:50:35 +0200
-Subject: [PATCH] paged on-demand block allocation (env LLAMA_KV_PAGED) - patch
- 0004
-
-Drive the paged placement in find_slot through the vendored PagedKVManager
-(patch 0001) instead of a fixed full-pool permutation. Blocks are popped from a
-free pool on demand as the sequence crosses block boundaries (peak << full
-reservation) and returned on sequence end (seq_rm full removal / clear). One
-manager per (kv-cache, stream); all state lives in the new src/paged-alloc unit,
-so the core kv-cache struct is untouched - find_slot/clear/seq_rm gain only a
-gated call. Default off; stock path byte-identical.
----
- src/CMakeLists.txt     |   1 +
- src/llama-kv-cache.cpp |  69 +++++++++++++++++----------
- src/paged-alloc.cpp    | 106 +++++++++++++++++++++++++++++++++++++++++
- src/paged-alloc.h      |  39 +++++++++++++++
- 4 files changed, 190 insertions(+), 25 deletions(-)
- create mode 100644 src/paged-alloc.cpp
- create mode 100644 src/paged-alloc.h
-
-diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
-index 58083b3..4d9d7d1 100644
---- a/src/CMakeLists.txt
-+++ b/src/CMakeLists.txt
-@@ -26,6 +26,7 @@ add_library(llama
-             llama-kv-cache-iswa.cpp
-             paged-kv-manager.cpp
-             paged-attn.cpp
-+            paged-alloc.cpp
-             llama-kv-cache-dsa.cpp
-             llama-memory.cpp
-             llama-memory-hybrid.cpp
-diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
-index 30d02d7..1125d9a 100644
---- a/src/llama-kv-cache.cpp
-+++ b/src/llama-kv-cache.cpp
-@@ -1,4 +1,5 @@
- #include "llama-kv-cache.h"
-+#include "paged-alloc.h"
- #include <vector>
- #include <utility>
- 
-@@ -381,6 +382,11 @@ llama_kv_cache::llama_kv_cache(
- }
- 
- void llama_kv_cache::clear(bool data) {
-+    // [paged 0004] return all on-demand blocks to the pool on cache clear.
-+    if (paged_alloc::active()) {
-+        paged_alloc::release_all(this);
-+    }
-+
-     for (uint32_t s = 0; s < n_stream; ++s) {
-         v_cells[s].reset();
-         v_heads[s] = 0;
-@@ -409,6 +415,16 @@ bool llama_kv_cache::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
-         p1 = std::numeric_limits<llama_pos>::max();
-     }
- 
-+    // [paged 0004] free a stream's on-demand blocks when its whole sequence is
-+    // removed (sequence end), so they return to the pool for reuse.
-+    if (paged_alloc::active() && p0 == 0 && p1 == std::numeric_limits<llama_pos>::max()) {
-+        if (seq_id >= 0) {
-+            paged_alloc::release(this, (int) seq_to_stream[seq_id]);
-+        } else {
-+            paged_alloc::release_all(this);
-+        }
-+    }
-+
-     if (seq_id >= 0) {
-         auto & cells = v_cells[seq_to_stream[seq_id]];
-         auto & head  = v_heads[seq_to_stream[seq_id]];
-@@ -1030,36 +1046,39 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch,
-         // the correctness premise of paged attention. Enabled via LLAMA_KV_PAGED.
-         // Single-sequence scope (uses get_used() as the logical base); falls back
-         // to the normal allocator if the permuted cells aren't available.
--        static const bool paged_mode = (std::getenv("LLAMA_KV_PAGED") != nullptr);
--        if (paged_mode) {
-+        // [paged 0004] On-demand block allocation. Patch 0002 proved attention is
-+        // invariant to physical KV placement; here that placement is driven by
-+        // the vendored PagedKVManager (patch 0001): blocks are popped from a free
-+        // pool only as the sequence crosses block boundaries (peak << full
-+        // reservation) and returned on sequence end. Enabled via LLAMA_KV_PAGED;
-+        // falls back to the normal allocator on pool exhaustion or any conflict.
-+        if (paged_alloc::active()) {
-             const uint32_t bs   = 16;                 // block size (tokens/block)
--            const uint32_t nblk = cells.size() / bs;  // blocks in this stream's pool
-+            const uint32_t nblk = cells.size() / bs;  // this stream's block budget
-             if (nblk >= 2) {
--                // stride coprime to nblk => block-index permutation is a bijection
--                uint32_t k = 1;
--                for (uint32_t cand = (nblk / 2) | 1u; cand < nblk; cand += 2) {
--                    if (std::gcd(cand, nblk) == 1u) { k = cand; break; }
--                }
-                 const uint32_t base = cells.get_used();
--                bool ok = true;
--                for (uint32_t i = 0; i < n_tokens; ++i) {
--                    const uint32_t L    = base + i;
--                    const uint32_t b    = L / bs;
--                    const uint32_t off  = L % bs;
--                    if (b >= nblk) { ok = false; break; }
--                    const uint32_t phys = ((b * k) % nblk) * bs + off; // permuted block
--                    if (phys >= cells.size() || !cells.is_empty(phys)) { ok = false; break; }
--                    res.idxs[s].push_back(phys);
--                }
--                if (ok && res.idxs[s].size() == n_tokens) {
--                    if (std::getenv("LLAMA_KV_PAGED_DEBUG")) {
--                        fprintf(stderr, "[paged] seq placed %u tok at cells:", n_tokens);
--                        for (uint32_t z = 0; z < res.idxs[s].size() && z < 24; ++z) fprintf(stderr, " %u", res.idxs[s][z]);
--                        fprintf(stderr, " (k=%u nblk=%u base=%u)\n", k, nblk, base);
-+                const int      strm = (int) seq_to_stream[seq_id];
-+                std::vector<uint32_t> placed;
-+                if (paged_alloc::place(this, strm, base, n_tokens, bs, nblk, placed)) {
-+                    bool ok = (placed.size() == n_tokens);
-+                    for (uint32_t i = 0; ok && i < n_tokens; ++i) {
-+                        if (placed[i] >= cells.size() || !cells.is_empty(placed[i])) {
-+                            ok = false;
-+                        }
-+                    }
-+                    if (ok) {
-+                        for (uint32_t phys : placed) {
-+                            res.idxs[s].push_back(phys);
-+                        }
-+                        if (std::getenv("LLAMA_KV_PAGED_DEBUG")) {
-+                            fprintf(stderr, "[paged] stream %d placed %u tok at cells:", strm, n_tokens);
-+                            for (uint32_t z = 0; z < res.idxs[s].size() && z < 24; ++z) fprintf(stderr, " %u", res.idxs[s][z]);
-+                            fprintf(stderr, " (nblk=%u base=%u)\n", nblk, base);
-+                        }
-+                        continue; // on-demand paged placement succeeded
-                     }
--                    continue; // paged placement succeeded for this sequence
-+                    res.idxs[s].clear(); // fall back to the normal allocator
-                 }
--                res.idxs[s].clear(); // fall back to the normal allocator
-             }
-         }
- 
-diff --git a/src/paged-alloc.cpp b/src/paged-alloc.cpp
-new file mode 100644
-index 0000000..1d13f9c
---- /dev/null
-+++ b/src/paged-alloc.cpp
-@@ -0,0 +1,106 @@
-+#include "paged-alloc.h"
-+#include "paged-kv-manager.h"
-+
-+#include <cstdlib>
-+#include <cstdio>
-+#include <map>
-+#include <memory>
-+#include <utility>
-+
-+namespace paged_alloc {
-+
-+bool active() {
-+    static const bool a = (std::getenv("LLAMA_KV_PAGED") != nullptr);
-+    return a;
-+}
-+
-+static bool debug() {
-+    static const bool d = (std::getenv("LLAMA_KV_PAGED_DEBUG") != nullptr);
-+    return d;
-+}
-+
-+namespace {
-+
-+using key_t = std::pair<const void *, int>;
-+
-+// One PagedKVManager per (kv-cache, stream): each stream owns a separate
-+// physical pool of cells.size() cells, so a manager's block ids map directly to
-+// cell ranges within that stream's pool. The internal request id is always 0.
-+std::map<key_t, std::unique_ptr<paged::PagedKVManager>> g_managers;
-+
-+paged::PagedKVManager * get_mgr(const void * cache, int stream,
-+                                uint32_t pool_blocks, uint32_t block_size) {
-+    const key_t k{cache, stream};
-+    auto it = g_managers.find(k);
-+    if (it == g_managers.end()) {
-+        // enable_caching=false: prefix caching is a later patch; 0004 exercises
-+        // only on-demand allocate / free.
-+        auto mgr = std::make_unique<paged::PagedKVManager>(
-+            (int32_t) pool_blocks, (int) block_size, /*enable_caching=*/false);
-+        it = g_managers.emplace(k, std::move(mgr)).first;
-+    }
-+    return it->second.get();
-+}
-+
-+} // namespace
-+
-+bool place(const void * cache, int stream, uint32_t base, uint32_t n_tokens,
-+           uint32_t block_size, uint32_t pool_blocks,
-+           std::vector<uint32_t> & out) {
-+    if (n_tokens == 0) {
-+        return true;
-+    }
-+
-+    paged::PagedKVManager * mgr = get_mgr(cache, stream, pool_blocks, block_size);
-+
-+    const size_t before = mgr->block_table(0).size();
-+
-+    // Grow the request to cover the highest logical position. The manager pops
-+    // free blocks only for the boundaries actually crossed - that is the on-
-+    // demand behavior; an already-covered range adds nothing.
-+    if (!mgr->allocate(0, (size_t) base + n_tokens)) {
-+        return false; // pool exhausted -> caller falls back to the stock path
-+    }
-+
-+    out.reserve(out.size() + n_tokens);
-+    for (uint32_t i = 0; i < n_tokens; ++i) {
-+        const int64_t s = mgr->slot(0, (int) (base + i));
-+        out.push_back((uint32_t) s);
-+    }
-+
-+    if (debug()) {
-+        const size_t after = mgr->block_table(0).size();
-+        if (after != before) {
-+            fprintf(stderr,
-+                    "[paged-alloc] cache=%p stream=%d grew %zu->%zu blocks "
-+                    "(budget=%u; base=%u +%u tok)\n",
-+                    cache, stream, before, after, pool_blocks, base, n_tokens);
-+        }
-+    }
-+
-+    return true;
-+}
-+
-+void release(const void * cache, int stream) {
-+    auto it = g_managers.find({cache, stream});
-+    if (it == g_managers.end()) {
-+        return;
-+    }
-+    it->second->free(0);
-+    g_managers.erase(it);
-+    if (debug()) {
-+        fprintf(stderr, "[paged-alloc] released cache=%p stream=%d\n", cache, stream);
-+    }
-+}
-+
-+void release_all(const void * cache) {
-+    for (auto it = g_managers.begin(); it != g_managers.end(); ) {
-+        if (it->first.first == cache) {
-+            it = g_managers.erase(it);
-+        } else {
-+            ++it;
-+        }
-+    }
-+}
-+
-+} // namespace paged_alloc
-diff --git a/src/paged-alloc.h b/src/paged-alloc.h
-new file mode 100644
-index 0000000..bf66665
---- /dev/null
-+++ b/src/paged-alloc.h
-@@ -0,0 +1,39 @@
-+#pragma once
-+// On-demand paged KV block allocation (patch 0004, experimental).
-+//
-+// Backs the paged placement in llama_kv_cache::find_slot (patch 0002) with the
-+// vendored host-side PagedKVManager (patch 0001). Instead of mapping a
-+// sequence's logical positions onto a fixed full-pool permutation, blocks are
-+// popped from a free pool ON DEMAND as the sequence crosses block boundaries,
-+// and returned to the pool on sequence end. This is where the paged memory-
-+// capacity benefit begins: a short sequence holds only a few blocks, not the
-+// whole reserved window.
-+//
-+// Gated behind env LLAMA_KV_PAGED; a no-op when unset. All state lives in this
-+// unit (a static registry keyed by kv-cache + stream), so the core kv-cache
-+// struct stays untouched - find_slot only gains a gated call.
-+
-+#include <cstdint>
-+#include <vector>
-+
-+namespace paged_alloc {
-+
-+// true iff env LLAMA_KV_PAGED is set (evaluated once).
-+bool active();
-+
-+// Place n_tokens logical positions [base, base+n_tokens) of one stream on
-+// demand, appending their physical cell indices to `out`. pool_blocks =
-+// cells.size()/block_size is this stream's block budget. Returns false (leaving
-+// `out` unchanged) on pool exhaustion, so the caller falls back to the stock
-+// allocator. The caller still validates each returned cell is empty.
-+bool place(const void * cache, int stream, uint32_t base, uint32_t n_tokens,
-+           uint32_t block_size, uint32_t pool_blocks,
-+           std::vector<uint32_t> & out);
-+
-+// Return a stream's blocks to the pool (sequence end).
-+void release(const void * cache, int stream);
-+
-+// Return every stream's blocks for a kv-cache (clear() / teardown).
-+void release_all(const void * cache);
-+
-+} // namespace paged_alloc
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0006-paged-cross-request-prefix-caching-env-LLAMA_KV_PAGED.patch

@@ -1,143 +0,0 @@
-From 141029beec609e87f24f6f6bba3ec842d7037862 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Mon, 22 Jun 2026 12:13:44 +0200
-Subject: [PATCH] paged cross-request prefix caching (env LLAMA_KV_PAGED) -
- patch 0006
-
-Add host-side cross-request prefix sharing to the vendored PagedKVManager
-(patches 0001-0004): on placement, hash a new sequence prefix blocks, reuse the
-matching cached physical blocks (ref_cnt++) for the shared prefix and allocate
-fresh blocks only for the divergent suffix. A shared block is freed only at
-ref 0; copy-on-write privatises a still-shared (ref>1) block before a divergent
-write so co-owners stay byte-correct. All logic lives in the vendored
-src/paged-kv-manager unit (place_with_prefix / cow_block / ref-counting); the
-core kv-cache files are untouched. Default off; gated behind LLAMA_KV_PAGED.
-
-Wiring the physical-cell reuse into find_slot so the engine itself skips
-recompute needs core seq-membership changes and is left to a later patch.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- src/paged-kv-manager.cpp | 65 ++++++++++++++++++++++++++++++++++++++++
- src/paged-kv-manager.h   | 23 ++++++++++++++
- 2 files changed, 88 insertions(+)
-
-diff --git a/src/paged-kv-manager.cpp b/src/paged-kv-manager.cpp
-index ca0dcd8..4c6ee4c 100644
---- a/src/paged-kv-manager.cpp
-+++ b/src/paged-kv-manager.cpp
-@@ -293,4 +293,69 @@ void PagedKVManager::cache_blocks(int seq_id, const std::vector<uint64_t>& block
-     pool_.cache_full_blocks(req, /*num_cached=*/0, n_full, block_hashes);
- }
- 
-+// ---------------------------------------------------------------------------
-+// Cross-request prefix caching + copy-on-write  (patch 0006)
-+// ---------------------------------------------------------------------------
-+
-+size_t PagedKVManager::place_with_prefix(int seq_id, const std::vector<int>& token_ids) {
-+    auto& req = req_to_blocks_[seq_id];
-+
-+    // Longest cached prefix: hash the full blocks and stop at the first miss.
-+    // A block hash transitively encodes its whole prefix (FNV chaining), so the
-+    // first miss bounds the reusable prefix (vLLM find_longest_cache_hit).
-+    const std::vector<uint64_t> hashes = compute_block_hashes(token_ids);
-+    std::vector<KVCacheBlock*> hits;
-+    for (uint64_t bh : hashes) {
-+        KVCacheBlock* cb = pool_.get_cached_block(bh);
-+        if (!cb) break;
-+        hits.push_back(cb);
-+    }
-+
-+    // Reuse: ++ref_cnt (pulling warm blocks back out of the free list) then
-+    // splice the shared physical blocks into this sequence's block table.
-+    pool_.touch(hits);
-+    req.insert(req.end(), hits.begin(), hits.end());
-+
-+    // Allocate fresh blocks only for the divergent suffix.
-+    const size_t need = cdiv(token_ids.size(), block_size_);
-+    if (need > req.size()) {
-+        const size_t add = need - req.size();
-+        if (add > pool_.get_num_free_blocks()) {
-+            // OOM: roll the sequence back (un-touch the shared prefix so no ref
-+            // leaks) and report no placement; the caller falls back to stock.
-+            std::vector<KVCacheBlock*> ordered(req.rbegin(), req.rend());
-+            pool_.free_blocks(ordered);
-+            req.clear();
-+            return 0;
-+        }
-+        auto nb = pool_.get_new_blocks(add);
-+        req.insert(req.end(), nb.begin(), nb.end());
-+    }
-+    return hits.size();
-+}
-+
-+std::pair<int32_t, int32_t> PagedKVManager::cow_block(int seq_id, size_t bi) {
-+    auto& req = req_to_blocks_.at(seq_id);
-+    KVCacheBlock* old = req.at(bi);
-+    if (old->ref_cnt <= 1) {
-+        return { old->block_id, old->block_id }; // already private - no copy
-+    }
-+    // Private copy for this sequence. get_new_blocks sets the fresh block's
-+    // ref_cnt to 1; free_blocks decrements the shared block, which stays >0 so
-+    // it is NOT returned to the pool and the other owners are left untouched.
-+    KVCacheBlock* fresh = pool_.get_new_blocks(1).front();
-+    pool_.free_blocks({ old });
-+    req[bi] = fresh;
-+    return { old->block_id, fresh->block_id };
-+}
-+
-+int PagedKVManager::block_ref_cnt_at(int seq_id, size_t bi) const {
-+    return req_to_blocks_.at(seq_id).at(bi)->ref_cnt;
-+}
-+
-+size_t PagedKVManager::num_blocks(int seq_id) const {
-+    auto it = req_to_blocks_.find(seq_id);
-+    return it == req_to_blocks_.end() ? 0 : it->second.size();
-+}
-+
- } // namespace paged
-diff --git a/src/paged-kv-manager.h b/src/paged-kv-manager.h
-index 740280a..34decbc 100644
---- a/src/paged-kv-manager.h
-+++ b/src/paged-kv-manager.h
-@@ -14,6 +14,7 @@
- #include <vector>
- #include <unordered_map>
- #include <map>
-+#include <utility>
- 
- namespace paged {
- 
-@@ -99,6 +100,28 @@ public:
-     size_t get_computed_blocks(const std::vector<uint64_t>& block_hashes); // returns num cached tokens
-     void cache_blocks(int seq_id, const std::vector<uint64_t>& block_hashes, size_t num_tokens);
- 
-+    // Cross-request prefix caching + copy-on-write (patch 0006).
-+    //
-+    // Splice the longest cached prefix of token_ids into seq_id (reuse the
-+    // shared physical blocks, ref_cnt++ so a block frees only at ref 0) and
-+    // allocate fresh blocks only for the divergent suffix. Returns the number of
-+    // shared (reused) blocks; the caller skips recomputing those tokens. On pool
-+    // exhaustion the sequence is rolled back (no ref leak) and 0 is returned.
-+    size_t place_with_prefix(int seq_id, const std::vector<int>& token_ids);
-+
-+    // Copy-on-write the block at logical index bi of seq_id. If that block is
-+    // shared (ref_cnt>1), allocate a fresh private block, drop this seq's ref on
-+    // the shared one (other owners keep it, content untouched) and install the
-+    // fresh block at bi. Returns {old_block_id, new_block_id}; new==old when the
-+    // block was already private (ref_cnt<=1) and no copy is needed. The caller
-+    // copies the physical cell contents old_block_id -> new_block_id.
-+    std::pair<int32_t, int32_t> cow_block(int seq_id, size_t bi);
-+
-+    // Introspection for the prefix-share gate (debug/tests).
-+    int    block_ref_cnt_at(int seq_id, size_t bi) const;
-+    size_t num_blocks(int seq_id) const;
-+    size_t num_free_blocks() const { return pool_.get_num_free_blocks(); }
-+
- protected:
-     int block_size_;
-     BlockPool pool_;
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0007-paged-engine-prefix-recompute-skip-env-LLAMA_KV_PAGED.patch

@@ -1,531 +0,0 @@
-From da20c1c0571e84bc76202d915d4bb82892a3392b Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Mon, 22 Jun 2026 12:46:28 +0200
-Subject: [PATCH] paged engine prefix recompute-skip (env LLAMA_KV_PAGED) -
- patch 0007
-
-Wire the host-side cross-request prefix cache (patch 0006) into the engine so a
-new sequence physically SHARES the cached prefix blocks and skips recomputing the
-shared prefix - the actual compute win that 0006 (which only proved the host-side
-machinery + realised reuse via the stock seq_cp) did not yet deliver from the
-paged path itself.
-
-Mechanism (all gated behind LLAMA_KV_PAGED; default off, stock byte-identical):
-
-  * paged-alloc reworked from a per-stream, request-0, destroyed-on-free manager
-    into ONE persistent caching PagedKVManager per (kv-cache, stream) whose
-    requests are keyed by the real llama_seq_id. free(seq) now releases exactly
-    one sequence, so ref-counted shared blocks survive while another sharer holds
-    them. New seams: share_prefix (place_with_prefix -> shared prefix tokens),
-    slot, commit (publish a sequence into the content cache), ref-counted release,
-    plus ref/num-free introspection.
-
-  * Two gated llama_kv_cache methods (the core seq-membership handling 0007 needs):
-    paged_prefix_share() reuses the longest cached content prefix for a sequence
-    and marks the shared physical cells as belonging to it (cells.seq_add) so the
-    engine's attention mask includes the already-computed prefix KV; the caller
-    then decodes ONLY the divergent suffix. paged_prefix_commit() publishes a
-    sequence's full blocks for later reuse.
-
-  * find_slot's paged branch anchors placement on each sequence's own logical base
-    (ubatch.pos) and keys the manager request by seq_id, so an independently-freed
-    sequence and a shared prefix coexist in one unified pool. seq_rm/clear free
-    per-sequence (ref-counted) instead of nuking the whole stream.
-
-  * paged-prefix-api: a thin gated shim so a caller holding only the public
-    llama.h can reach the seam and the introspection without the internal headers.
-
-Core existing-file touch: src/llama-kv-cache.{cpp,h}, +71 -3. Everything else is
-additive vendored units. Verified on Qwen3-0.6B-Q8_0 (CPU, unified cache): a
-sequence B sharing A's prefix decodes greedy tokens byte-identical to B from
-scratch with the prefill computing ONLY the suffix (32 prefix tokens skipped) at
-a block boundary AND mid-block; the shared block carries ref_cnt 2 while both
-hold it, drops to 1 when one sharer is removed (survivor intact, re-shareable, no
-use-after-free) and returns to the pool only when all sharers are freed. The
-0004 serving gate (unified and non-unified) stays byte-identical stock vs paged.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- src/CMakeLists.txt       |   1 +
- src/llama-kv-cache.cpp   |  66 +++++++++++++++++++++++--
- src/llama-kv-cache.h     |   8 +++
- src/paged-alloc.cpp      | 104 ++++++++++++++++++++++++++++++---------
- src/paged-alloc.h        |  69 +++++++++++++++++++-------
- src/paged-prefix-api.cpp |  48 ++++++++++++++++++
- src/paged-prefix-api.h   |  27 ++++++++++
- 7 files changed, 280 insertions(+), 43 deletions(-)
- create mode 100644 src/paged-prefix-api.cpp
- create mode 100644 src/paged-prefix-api.h
-
-diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
-index 4d9d7d1..432f42d 100644
---- a/src/CMakeLists.txt
-+++ b/src/CMakeLists.txt
-@@ -27,6 +27,7 @@ add_library(llama
-             paged-kv-manager.cpp
-             paged-attn.cpp
-             paged-alloc.cpp
-+            paged-prefix-api.cpp
-             llama-kv-cache-dsa.cpp
-             llama-memory.cpp
-             llama-memory-hybrid.cpp
-diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
-index 1125d9a..7510ff9 100644
---- a/src/llama-kv-cache.cpp
-+++ b/src/llama-kv-cache.cpp
-@@ -419,7 +419,7 @@ bool llama_kv_cache::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
-     // removed (sequence end), so they return to the pool for reuse.
-     if (paged_alloc::active() && p0 == 0 && p1 == std::numeric_limits<llama_pos>::max()) {
-         if (seq_id >= 0) {
--            paged_alloc::release(this, (int) seq_to_stream[seq_id]);
-+            paged_alloc::release(this, (int) seq_to_stream[seq_id], (int) seq_id);
-         } else {
-             paged_alloc::release_all(this);
-         }
-@@ -1056,10 +1056,15 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch,
-             const uint32_t bs   = 16;                 // block size (tokens/block)
-             const uint32_t nblk = cells.size() / bs;  // this stream's block budget
-             if (nblk >= 2) {
--                const uint32_t base = cells.get_used();
-+                // [paged 0007] Anchor placement on this sequence's own logical
-+                // base position (ubatch.pos), not the shared used-count, and key
-+                // the manager request by the real seq_id. slot(seq,pos) is then
-+                // stable per sequence, so an independently-freed (ref-counted)
-+                // sequence and a shared prefix can coexist in one unified pool.
-+                const uint32_t base = (uint32_t) ubatch.pos[s*n_tokens];
-                 const int      strm = (int) seq_to_stream[seq_id];
-                 std::vector<uint32_t> placed;
--                if (paged_alloc::place(this, strm, base, n_tokens, bs, nblk, placed)) {
-+                if (paged_alloc::place(this, strm, (int) seq_id, base, n_tokens, bs, nblk, placed)) {
-                     bool ok = (placed.size() == n_tokens);
-                     for (uint32_t i = 0; ok && i < n_tokens; ++i) {
-                         if (placed[i] >= cells.size() || !cells.is_empty(placed[i])) {
-@@ -1165,6 +1170,61 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch,
-     return res;
- }
- 
-+// [paged 0007] Cross-request prefix recompute-skip.
-+//
-+// Reuse a cached content prefix for seq_id: share_prefix() splices the longest
-+// matching cached physical blocks into seq_id (ref_cnt++) and reserves fresh
-+// blocks for the divergent suffix. We then mark the shared physical cells as
-+// belonging to seq_id - those cells already hold the owner's computed KV at the
-+// matching logical positions, so the caller decodes ONLY the suffix and the
-+// prefix is never recomputed. Returns the number of shared prefix tokens.
-+// Gated behind LLAMA_KV_PAGED; a no-op (returns 0) otherwise.
-+int32_t llama_kv_cache::paged_prefix_share(llama_seq_id seq_id, const std::vector<llama_token> & tokens) {
-+    if (!paged_alloc::active() || tokens.empty()) {
-+        return 0;
-+    }
-+    const uint32_t bs   = 16;
-+    const uint32_t strm = (uint32_t) seq_to_stream[seq_id];
-+    auto & cells = v_cells[strm];
-+    const uint32_t nblk = cells.size() / bs;
-+    if (nblk < 2) {
-+        return 0;
-+    }
-+
-+    std::vector<int> toks(tokens.begin(), tokens.end());
-+    const size_t kshare = paged_alloc::share_prefix(this, (int) strm, (int) seq_id, toks, bs, nblk);
-+
-+    for (size_t p = 0; p < kshare; ++p) {
-+        const int64_t cell = paged_alloc::slot(this, (int) strm, (int) seq_id, (int) p);
-+        if (cell < 0 || (uint32_t) cell >= cells.size() ||
-+            cells.is_empty((uint32_t) cell) ||
-+            cells.pos_get((uint32_t) cell) != (llama_pos) p) {
-+            // Owner cell missing / repurposed: cannot safely share. Roll the
-+            // sequence back so the caller recomputes the whole prompt.
-+            paged_alloc::release(this, (int) strm, (int) seq_id);
-+            return 0;
-+        }
-+        if (!cells.seq_has((uint32_t) cell, seq_id)) {
-+            cells.seq_add((uint32_t) cell, seq_id);
-+        }
-+    }
-+    return (int32_t) kshare;
-+}
-+
-+// [paged 0007] Publish a sequence's full blocks into the content cache so a
-+// later paged_prefix_share() can reuse them. Call after the sequence KV is
-+// computed (its prefill decode has run).
-+void llama_kv_cache::paged_prefix_commit(llama_seq_id seq_id, const std::vector<llama_token> & tokens) {
-+    if (!paged_alloc::active() || tokens.empty()) {
-+        return;
-+    }
-+    const uint32_t bs   = 16;
-+    const uint32_t strm = (uint32_t) seq_to_stream[seq_id];
-+    const uint32_t nblk = v_cells[strm].size() / bs;
-+    std::vector<int> toks(tokens.begin(), tokens.end());
-+    paged_alloc::commit(this, (int) strm, (int) seq_id, toks, bs, nblk);
-+}
-+
- void llama_kv_cache::apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch) {
-     // TODO: refactor [TAG_KV_CACHE_SHARE_CELLS]
-     if (other) {
-diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
-index 494c0fb..f374ac6 100644
---- a/src/llama-kv-cache.h
-+++ b/src/llama-kv-cache.h
-@@ -199,6 +199,14 @@ public:
-     // emplace the ubatch context into slot: [sinfo.idxs[0...ubatch.n_tokens - 1]]
-     void apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch);
- 
-+    // [paged 0007] Cross-request prefix recompute-skip (experimental, gated by
-+    // env LLAMA_KV_PAGED). paged_prefix_share() reuses a cached content prefix
-+    // for seq_id and returns the number of shared prefix tokens (the caller
-+    // decodes only the suffix); paged_prefix_commit() publishes a sequence into
-+    // the content cache for later reuse. No-ops when LLAMA_KV_PAGED is unset.
-+    int32_t paged_prefix_share (llama_seq_id seq_id, const std::vector<llama_token> & tokens);
-+    void    paged_prefix_commit(llama_seq_id seq_id, const std::vector<llama_token> & tokens);
-+
-     //
-     // input API
-     //
-diff --git a/src/paged-alloc.cpp b/src/paged-alloc.cpp
-index 1d13f9c..c1027fb 100644
---- a/src/paged-alloc.cpp
-+++ b/src/paged-alloc.cpp
-@@ -23,9 +23,13 @@ namespace {
- 
- using key_t = std::pair<const void *, int>;
- 
--// One PagedKVManager per (kv-cache, stream): each stream owns a separate
--// physical pool of cells.size() cells, so a manager's block ids map directly to
--// cell ranges within that stream's pool. The internal request id is always 0.
-+// One persistent PagedKVManager per (kv-cache, stream): each stream owns a
-+// separate physical pool of cells.size() cells, so a manager's block ids map
-+// directly to cell ranges within that stream's pool. Requests inside a manager
-+// are keyed by the real llama_seq_id (NOT a fixed 0), so free(seq) releases one
-+// sequence and shared blocks survive at ref>0 - this is what makes ref-counted
-+// cross-request prefix sharing (0007) possible. Caching is enabled so commit()
-+// can publish blocks and share_prefix() can hit them.
- std::map<key_t, std::unique_ptr<paged::PagedKVManager>> g_managers;
- 
- paged::PagedKVManager * get_mgr(const void * cache, int stream,
-@@ -33,18 +37,21 @@ paged::PagedKVManager * get_mgr(const void * cache, int stream,
-     const key_t k{cache, stream};
-     auto it = g_managers.find(k);
-     if (it == g_managers.end()) {
--        // enable_caching=false: prefix caching is a later patch; 0004 exercises
--        // only on-demand allocate / free.
-         auto mgr = std::make_unique<paged::PagedKVManager>(
--            (int32_t) pool_blocks, (int) block_size, /*enable_caching=*/false);
-+            (int32_t) pool_blocks, (int) block_size, /*enable_caching=*/true);
-         it = g_managers.emplace(k, std::move(mgr)).first;
-     }
-     return it->second.get();
- }
- 
-+paged::PagedKVManager * find_mgr(const void * cache, int stream) {
-+    auto it = g_managers.find({cache, stream});
-+    return it == g_managers.end() ? nullptr : it->second.get();
-+}
-+
- } // namespace
- 
--bool place(const void * cache, int stream, uint32_t base, uint32_t n_tokens,
-+bool place(const void * cache, int stream, int seq, uint32_t base, uint32_t n_tokens,
-            uint32_t block_size, uint32_t pool_blocks,
-            std::vector<uint32_t> & out) {
-     if (n_tokens == 0) {
-@@ -53,43 +60,79 @@ bool place(const void * cache, int stream, uint32_t base, uint32_t n_tokens,
- 
-     paged::PagedKVManager * mgr = get_mgr(cache, stream, pool_blocks, block_size);
- 
--    const size_t before = mgr->block_table(0).size();
-+    const size_t before = mgr->block_table(seq).size();
- 
--    // Grow the request to cover the highest logical position. The manager pops
--    // free blocks only for the boundaries actually crossed - that is the on-
--    // demand behavior; an already-covered range adds nothing.
--    if (!mgr->allocate(0, (size_t) base + n_tokens)) {
-+    // Grow this sequence's request to cover its highest logical position. The
-+    // manager pops free blocks only for boundaries actually crossed; if
-+    // share_prefix() already reserved these blocks, this is a no-op.
-+    if (!mgr->allocate(seq, (size_t) base + n_tokens)) {
-         return false; // pool exhausted -> caller falls back to the stock path
-     }
- 
-     out.reserve(out.size() + n_tokens);
-     for (uint32_t i = 0; i < n_tokens; ++i) {
--        const int64_t s = mgr->slot(0, (int) (base + i));
-+        const int64_t s = mgr->slot(seq, (int) (base + i));
-         out.push_back((uint32_t) s);
-     }
- 
-     if (debug()) {
--        const size_t after = mgr->block_table(0).size();
-+        const size_t after = mgr->block_table(seq).size();
-         if (after != before) {
-             fprintf(stderr,
--                    "[paged-alloc] cache=%p stream=%d grew %zu->%zu blocks "
-+                    "[paged-alloc] cache=%p stream=%d seq=%d grew %zu->%zu blocks "
-                     "(budget=%u; base=%u +%u tok)\n",
--                    cache, stream, before, after, pool_blocks, base, n_tokens);
-+                    cache, stream, seq, before, after, pool_blocks, base, n_tokens);
-         }
-     }
- 
-     return true;
- }
- 
--void release(const void * cache, int stream) {
--    auto it = g_managers.find({cache, stream});
--    if (it == g_managers.end()) {
-+size_t share_prefix(const void * cache, int stream, int seq,
-+                    const std::vector<int> & tokens,
-+                    uint32_t block_size, uint32_t pool_blocks) {
-+    paged::PagedKVManager * mgr = get_mgr(cache, stream, pool_blocks, block_size);
-+    const size_t shared_blocks = mgr->place_with_prefix(seq, tokens);
-+    const size_t shared_tokens = shared_blocks * (size_t) block_size;
-+    if (debug() && shared_blocks > 0) {
-+        fprintf(stderr,
-+                "[paged-alloc] cache=%p stream=%d seq=%d shares %zu prefix blocks "
-+                "(%zu tokens) - prefix NOT recomputed\n",
-+                cache, stream, seq, shared_blocks, shared_tokens);
-+    }
-+    return shared_tokens;
-+}
-+
-+int64_t slot(const void * cache, int stream, int seq, int pos) {
-+    paged::PagedKVManager * mgr = find_mgr(cache, stream);
-+    if (!mgr) {
-+        return -1;
-+    }
-+    if ((size_t) (pos / mgr->block_size()) >= mgr->num_blocks(seq)) {
-+        return -1;
-+    }
-+    return mgr->slot(seq, pos);
-+}
-+
-+void commit(const void * cache, int stream, int seq,
-+            const std::vector<int> & tokens, uint32_t block_size, uint32_t pool_blocks) {
-+    paged::PagedKVManager * mgr = get_mgr(cache, stream, pool_blocks, block_size);
-+    mgr->cache_blocks(seq, mgr->compute_block_hashes(tokens), tokens.size());
-+    if (debug()) {
-+        fprintf(stderr, "[paged-alloc] cache=%p stream=%d seq=%d committed %zu tokens\n",
-+                cache, stream, seq, tokens.size());
-+    }
-+}
-+
-+void release(const void * cache, int stream, int seq) {
-+    paged::PagedKVManager * mgr = find_mgr(cache, stream);
-+    if (!mgr) {
-         return;
-     }
--    it->second->free(0);
--    g_managers.erase(it);
-+    mgr->free(seq); // ref-counted: shared blocks survive while another seq holds them
-     if (debug()) {
--        fprintf(stderr, "[paged-alloc] released cache=%p stream=%d\n", cache, stream);
-+        fprintf(stderr, "[paged-alloc] released cache=%p stream=%d seq=%d (free=%zu)\n",
-+                cache, stream, seq, mgr->num_free_blocks());
-     }
- }
- 
-@@ -103,4 +146,21 @@ void release_all(const void * cache) {
-     }
- }
- 
-+int ref_cnt_at(const void * cache, int stream, int seq, int pos, uint32_t block_size) {
-+    paged::PagedKVManager * mgr = find_mgr(cache, stream);
-+    if (!mgr) {
-+        return -1;
-+    }
-+    const size_t bi = (size_t) pos / block_size;
-+    if (bi >= mgr->num_blocks(seq)) {
-+        return -1;
-+    }
-+    return mgr->block_ref_cnt_at(seq, bi);
-+}
-+
-+size_t num_free(const void * cache, int stream) {
-+    paged::PagedKVManager * mgr = find_mgr(cache, stream);
-+    return mgr ? mgr->num_free_blocks() : 0;
-+}
-+
- } // namespace paged_alloc
-diff --git a/src/paged-alloc.h b/src/paged-alloc.h
-index bf66665..88dedef 100644
---- a/src/paged-alloc.h
-+++ b/src/paged-alloc.h
-@@ -1,17 +1,27 @@
- #pragma once
--// On-demand paged KV block allocation (patch 0004, experimental).
-+// On-demand paged KV block allocation + cross-request prefix reuse
-+// (patches 0004 + 0007, experimental).
- //
--// Backs the paged placement in llama_kv_cache::find_slot (patch 0002) with the
--// vendored host-side PagedKVManager (patch 0001). Instead of mapping a
--// sequence's logical positions onto a fixed full-pool permutation, blocks are
--// popped from a free pool ON DEMAND as the sequence crosses block boundaries,
--// and returned to the pool on sequence end. This is where the paged memory-
--// capacity benefit begins: a short sequence holds only a few blocks, not the
--// whole reserved window.
-+// Backs the paged placement in llama_kv_cache::find_slot with the vendored
-+// host-side PagedKVManager (patch 0001). Two responsibilities:
- //
--// Gated behind env LLAMA_KV_PAGED; a no-op when unset. All state lives in this
--// unit (a static registry keyed by kv-cache + stream), so the core kv-cache
--// struct stays untouched - find_slot only gains a gated call.
-+//   * On-demand allocation (0004): a sequence's logical positions are mapped to
-+//     physical cells block-by-block, popped from a free pool only as the
-+//     sequence grows and returned on sequence end.
-+//
-+//   * Cross-request prefix reuse (0007): before a new sequence's suffix is
-+//     decoded, share_prefix() reuses the cached physical blocks of a matching
-+//     content prefix (ref_cnt++), so the engine shares the already-computed KV
-+//     cells and the caller decodes ONLY the divergent suffix - the prefix is not
-+//     recomputed. commit() publishes a sequence's full blocks into the content
-+//     cache so later sequences can hit them. Freeing is ref-counted: a shared
-+//     block returns to the pool only when every sharer has been released.
-+//
-+// One persistent PagedKVManager per (kv-cache, stream); requests inside it are
-+// keyed by the real llama_seq_id, so free(seq) releases exactly one sequence and
-+// shared blocks survive at ref>0. All state lives in this unit (a static
-+// registry), so the core kv-cache struct stays untouched - find_slot gains only
-+// gated calls. Gated behind env LLAMA_KV_PAGED; a no-op when unset.
- 
- #include <cstdint>
- #include <vector>
-@@ -21,19 +31,42 @@ namespace paged_alloc {
- // true iff env LLAMA_KV_PAGED is set (evaluated once).
- bool active();
- 
--// Place n_tokens logical positions [base, base+n_tokens) of one stream on
--// demand, appending their physical cell indices to `out`. pool_blocks =
--// cells.size()/block_size is this stream's block budget. Returns false (leaving
-+// Place n_tokens logical positions [base, base+n_tokens) of (cache,stream,seq)
-+// on demand, appending their physical cell indices to `out`. pool_blocks =
-+// cells.size()/block_size is the stream's block budget. Returns false (leaving
- // `out` unchanged) on pool exhaustion, so the caller falls back to the stock
- // allocator. The caller still validates each returned cell is empty.
--bool place(const void * cache, int stream, uint32_t base, uint32_t n_tokens,
-+bool place(const void * cache, int stream, int seq, uint32_t base, uint32_t n_tokens,
-            uint32_t block_size, uint32_t pool_blocks,
-            std::vector<uint32_t> & out);
- 
--// Return a stream's blocks to the pool (sequence end).
--void release(const void * cache, int stream);
-+// [0007] Reuse the longest cached content prefix of `tokens` for (cache,stream,
-+// seq): splice the shared physical blocks into seq (ref_cnt++) and reserve fresh
-+// blocks for the divergent suffix. Returns the number of shared PREFIX TOKENS
-+// (block-aligned); the caller marks those cells for seq and decodes only the
-+// suffix. 0 if nothing matched or on pool exhaustion (sequence rolled back).
-+size_t share_prefix(const void * cache, int stream, int seq,
-+                    const std::vector<int> & tokens,
-+                    uint32_t block_size, uint32_t pool_blocks);
-+
-+// [0007] Physical cell backing logical position `pos` of (cache,stream,seq), or
-+// -1 if seq is unknown. Used to map a shared prefix position to its cell.
-+int64_t slot(const void * cache, int stream, int seq, int pos);
- 
--// Return every stream's blocks for a kv-cache (clear() / teardown).
-+// [0007] Publish seq's full (block-aligned) blocks into the content cache so a
-+// later share_prefix() can reuse them. Call after the sequence's KV is computed.
-+void commit(const void * cache, int stream, int seq,
-+            const std::vector<int> & tokens, uint32_t block_size, uint32_t pool_blocks);
-+
-+// Return one sequence's blocks to the pool (ref-counted; sequence end).
-+void release(const void * cache, int stream, int seq);
-+
-+// Drop every manager for a kv-cache (clear() / teardown).
- void release_all(const void * cache);
- 
-+// Introspection for the prefix-share gate (debug/tests). ref_cnt_at returns the
-+// ref count of the block backing logical position `pos`, or -1 if unknown.
-+int    ref_cnt_at(const void * cache, int stream, int seq, int pos, uint32_t block_size);
-+size_t num_free(const void * cache, int stream);
-+
- } // namespace paged_alloc
-diff --git a/src/paged-prefix-api.cpp b/src/paged-prefix-api.cpp
-new file mode 100644
-index 0000000..8573cd2
---- /dev/null
-+++ b/src/paged-prefix-api.cpp
-@@ -0,0 +1,48 @@
-+#include "paged-prefix-api.h"
-+#include "paged-alloc.h"
-+#include "llama-kv-cache.h"
-+
-+#include <vector>
-+
-+namespace paged_prefix_api {
-+
-+static llama_kv_cache * kv_of(llama_context * ctx) {
-+    // The driver targets a plain unified KV-cache model; dynamic_cast yields null
-+    // for wrapped caches (iSWA / hybrid), where cross-request cell sharing does
-+    // not apply, so the shim degrades to a safe no-op.
-+    return dynamic_cast<llama_kv_cache *>(llama_get_memory(ctx));
-+}
-+
-+int32_t share(llama_context * ctx, llama_seq_id seq, const llama_token * tokens, int n) {
-+    llama_kv_cache * kv = kv_of(ctx);
-+    if (!kv || n <= 0) {
-+        return 0;
-+    }
-+    return kv->paged_prefix_share(seq, std::vector<llama_token>(tokens, tokens + n));
-+}
-+
-+void commit(llama_context * ctx, llama_seq_id seq, const llama_token * tokens, int n) {
-+    llama_kv_cache * kv = kv_of(ctx);
-+    if (!kv || n <= 0) {
-+        return;
-+    }
-+    kv->paged_prefix_commit(seq, std::vector<llama_token>(tokens, tokens + n));
-+}
-+
-+int ref_at(llama_context * ctx, llama_seq_id seq, int pos) {
-+    llama_kv_cache * kv = kv_of(ctx);
-+    if (!kv) {
-+        return -1;
-+    }
-+    return paged_alloc::ref_cnt_at((const void *) kv, /*stream=*/0, (int) seq, pos, /*block_size=*/16);
-+}
-+
-+long num_free(llama_context * ctx) {
-+    llama_kv_cache * kv = kv_of(ctx);
-+    if (!kv) {
-+        return 0;
-+    }
-+    return (long) paged_alloc::num_free((const void *) kv, /*stream=*/0);
-+}
-+
-+} // namespace paged_prefix_api
-diff --git a/src/paged-prefix-api.h b/src/paged-prefix-api.h
-new file mode 100644
-index 0000000..78a3864
---- /dev/null
-+++ b/src/paged-prefix-api.h
-@@ -0,0 +1,27 @@
-+#pragma once
-+// Thin test/diagnostic shim over the paged cross-request prefix engine seam
-+// (patch 0007). Lets a driver that only includes the public llama.h reach the
-+// gated llama_kv_cache::paged_prefix_* methods and the paged-alloc introspection
-+// without pulling in the internal kv-cache headers. All entry points are no-ops
-+// (return 0) unless env LLAMA_KV_PAGED is set. Experimental; not a stable API.
-+
-+#include "llama.h"
-+
-+namespace paged_prefix_api {
-+
-+// Reuse the longest cached content prefix of [tokens, tokens+n) for `seq` and
-+// return the number of shared prefix tokens (the caller decodes only the
-+// suffix). 0 if nothing was shared.
-+int32_t share(llama_context * ctx, llama_seq_id seq, const llama_token * tokens, int n);
-+
-+// Publish `seq`'s full blocks into the content cache (call after its KV is computed).
-+void commit(llama_context * ctx, llama_seq_id seq, const llama_token * tokens, int n);
-+
-+// Ref count of the paged block backing logical position `pos` of `seq` (unified
-+// stream 0), or -1 if unknown.
-+int ref_at(llama_context * ctx, llama_seq_id seq, int pos);
-+
-+// Number of free blocks in the unified stream-0 pool, or 0 if no manager.
-+long num_free(llama_context * ctx);
-+
-+} // namespace paged_prefix_api
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0008-paged-server-cross-request-prefix-share-env-LLAMA_KV_PAGED.patch

@@ -1,130 +0,0 @@
-From 088d58f3a0160cbc706226ac2e77ecfeae4c164a Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Mon, 22 Jun 2026 17:02:22 +0200
-Subject: [PATCH] paged server cross-request prefix share (env LLAMA_KV_PAGED)
- - patch 0008
-
-Wire the paged cross-request prefix recompute-skip (patch 0007's engine seam,
-paged_prefix_api::share/commit) into the llama-server continuous-batching loop
-(update_slots) so CONCURRENT requests that share a long prefix physically reuse
-one committed copy of the prefix blocks and prefill only their divergent suffix.
-Patch 0007 proved the engine seam correct via a standalone driver, but the server
-never called it: two concurrent shared-prefix requests each recomputed the full
-prefix. The server's native prompt cache only reuses a slot's OWN prior prompt
-(longest-common-prefix vs slot.prompt.tokens) - it does not share across distinct
-concurrent slots. 0008 adds that cross-slot share.
-
-Mechanism (all gated behind LLAMA_KV_PAGED; default off, stock byte-identical):
-
-  * In update_slots prompt-processing, after the native n_past is computed and
-    only for a FRESH slot (n_past < one block, i.e. the native cache did not
-    already cover the prefix), call paged_prefix_api::share() to splice the
-    longest committed cross-request prefix into this sequence (ref_cnt++ on the
-    shared physical blocks) and advance n_past past it, so the batch fill computes
-    ONLY the suffix. The slot's own divergent tail cells are removed first so the
-    shared cells own [n_past, kshare) without colliding (the native path removes
-    these later anyway). The n_past < block gate guarantees any block-aligned
-    share the engine returns is strictly larger than n_past and therefore always
-    adopted, so the engine's reservation always matches the suffix-only batch and
-    never leaves stale blocks (which otherwise fragment the paged pool).
-
-  * When a slot finishes prefill (SLOT_STATE_DONE_PROMPT -> GENERATING, the prefix
-    KV just computed), call paged_prefix_api::commit() to publish its prefix so
-    concurrent/later sharers can reuse it.
-
-The share() / commit() entry points are forward-declared (defined in libllama,
-src/paged-prefix-api.cpp) to avoid pulling internal kv-cache headers into the
-server translation unit.
-
-Verified in the server (32B NVFP4, CUDA, --kv-unified): with a live sequence
-holding the prefix, K=16/32 concurrent shared-prefix requests prefill only their
-~27-token suffix instead of the ~1003-token prefix (36x fewer prefill tokens;
-K=16 23.9s -> 1.5s, K=32 57.9s -> 2.3s), the engine logs "shares ... prefix
-blocks - NOT recomputed" with ref_cnt>1, and greedy output stays within the
-documented CUDA batch-shape non-determinism band (stock native prompt-caching
-shows the same magnitude). Cross-request sharing requires the unified KV cache.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- tools/server/server-context.cpp | 50 +++++++++++++++++++++++++++++++++
- 1 file changed, 50 insertions(+)
-
-diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
-index da6a475..04c6361 100644
---- a/tools/server/server-context.cpp
-+++ b/tools/server/server-context.cpp
-@@ -15,6 +15,16 @@
- #include "mtmd.h"
- #include "mtmd-helper.h"
- 
-+// [paged 0008] Cross-request prefix recompute-skip shim. share()/commit() are
-+// defined in libllama (src/paged-prefix-api.cpp, patch 0007) and are no-ops
-+// unless env LLAMA_KV_PAGED is set. Declared here so the paged cross-slot prefix
-+// cache wires into update_slots() without pulling in internal kv-cache headers.
-+// Fully gated; stock (paged off) is byte-identical.
-+namespace paged_prefix_api {
-+    int32_t share (llama_context * ctx, llama_seq_id seq, const llama_token * tokens, int n);
-+    void    commit(llama_context * ctx, llama_seq_id seq, const llama_token * tokens, int n);
-+}
-+
- #include <algorithm>
- #include <cstddef>
- #include <cinttypes>
-@@ -3007,6 +3017,37 @@ private:
-                             }
-                         }
- 
-+                        // [paged 0008] Cross-request prefix recompute-skip. The native prompt cache
-+                        // above only reuses THIS slot's own prior prompt; when the paged KV
-+                        // engine is active, also reuse a committed CROSS-slot prefix so
-+                        // concurrent requests sharing a long prefix skip recompute. Gated on
-+                        // LLAMA_KV_PAGED (paged_kv_share static); stock stays byte-identical.
-+                        static const bool paged_kv_share = getenv("LLAMA_KV_PAGED") != nullptr;
-+                        // Only attempt the cross-request share on a FRESH slot (the native
-+                        // cache above did not already cover the prefix). With n_past < a
-+                        // block, any block-aligned share the engine returns is strictly
-+                        // larger than n_past and is therefore always adopted below - so the
-+                        // engine's full-prompt reservation always matches the suffix-only
-+                        // submission and never leaves stale blocks (which fragmented the
-+                        // paged pool and crashed the server under high fan-out otherwise).
-+                        if (paged_kv_share && n_past < 16 && slot.task->params.cache_prompt && !input_tokens.has_mtmd) {
-+                            const llama_tokens ptoks = input_tokens.get_text_tokens();
-+                            // Drop this slot's own cells beyond the natively-cached prefix before
-+                            // splicing the shared physical prefix in, so the shared cells can own
-+                            // [n_past, kshare) without colliding (the native path removes exactly
-+                            // these later; a no-op for a fresh slot).
-+                            common_context_seq_rm(ctx_tgt, slot.id, n_past, -1);
-+                            const int32_t kshare = paged_prefix_api::share(ctx_tgt, slot.id, ptoks.data(), (int) ptoks.size());
-+                            if (kshare > n_past) {
-+                                slot.prompt.tokens.keep_first(n_past);
-+                                for (int i = n_past; i < kshare; ++i) {
-+                                    slot.prompt.tokens.push_back(ptoks[i]);
-+                                }
-+                                n_past = kshare;
-+                                SLT_INF(slot, "paged: reusing %d cross-request shared prefix tokens - not recomputed\n", n_past);
-+                            }
-+                        }
-+
-                         // [TAG_PROMPT_LOGITS]
-                         if (n_past == slot.task->n_tokens() && n_past > 0) {
-                             SLT_WRN(slot, "need to evaluate at least 1 token for each active slot (n_past = %d, task.n_tokens() = %d)\n", n_past, slot.task->n_tokens());
-@@ -3427,6 +3468,15 @@ private:
-                     // prompt evaluated for next-token prediction
-                     slot.state = SLOT_STATE_GENERATING;
- 
-+                    // [paged 0008] Publish this slot's computed prefix so concurrent/later
-+                    // slots can share it (no-op unless LLAMA_KV_PAGED). The prefill decode
-+                    // for [0, n_tokens) has just run, so the prefix KV is computed.
-+                    static const bool paged_kv_commit = getenv("LLAMA_KV_PAGED") != nullptr;
-+                    if (paged_kv_commit && slot.task->params.cache_prompt && !slot.prompt.tokens.has_mtmd) {
-+                        const llama_tokens ctoks = slot.prompt.tokens.get_text_tokens();
-+                        paged_prefix_api::commit(ctx_tgt, slot.id, ctoks.data(), (int) ctoks.size());
-+                    }
-+
-                     if (slot.can_speculate()) {
-                         common_speculative_begin(spec.get(), slot.id, slot.prompt.tokens.get_text_tokens());
-                     }
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0009-paged-in-kernel-decode-read-env-LLAMA_KV_PAGED-patch.patch

@@ -1,609 +0,0 @@
-From 59490d82e4d0d4ad05ffb5ca3cccc668f4a75281 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Mon, 22 Jun 2026 20:03:17 +0200
-Subject: [PATCH] paged in-kernel decode read (env LLAMA_KV_PAGED) - patch 0009
-
-Replace the per-layer per-step gather (patch 0003: ggml_get_rows of K/V into a
-contiguous buffer) with an in-kernel paged read on the decode step. build_attn
-passes the UNMODIFIED physical K/V views plus a block table (src[5] of
-ggml_flash_attn_ext: an I32 [n_view, n_stream] position-ordered physical-cell
-index, padded to FATTN_KQ_STRIDE). The CUDA fattn vec kernel and the CPU
-reference map logical KV index j -> physical cell block_table[seq*ne11+j] and
-read K_base+cell*nb11 / V_base+cell*nb21 in place, so the get_rows of K and V
-(the bulk of the gather) is gone. The mask stays a small compacted [n_view]
-causal mask in the same position order; KV_max / parallel_blocks / stream_k
-split-K are unchanged. The decode shape is forced onto the vec kernel (the only
-one wired for the block table); a nullptr block table => the stock contiguous
-read, byte-identical.
-
-Token-POSITION ordering keeps the flash-attn reduction order identical to stock,
-so CPU-paged logits == CPU-stock bit-for-bit (verified: 4-stream FA greedy, 64
-tokens). On GPU paged(vec) == stock(vec) at batch 1; at batch>1 it stays within
-the documented vec-vs-mma non-determinism band. Decode step at batch 32 / 1024
-ctx on GB10 (Qwen3-32B NVFP4): paged-gather 1279 ms -> in-kernel 696 ms (-46%),
-recovering the gather regression to stock parity (647 ms). Gated behind
-LLAMA_KV_PAGED; no-op (stock byte-identical) when unset.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- ggml/include/ggml.h                  |   6 ++
- ggml/src/ggml-cpu/ops.cpp            |  10 ++-
- ggml/src/ggml-cuda/fattn-common.cuh  |   8 +-
- ggml/src/ggml-cuda/fattn-mma-f16.cuh |   4 +-
- ggml/src/ggml-cuda/fattn-tile.cuh    |   4 +-
- ggml/src/ggml-cuda/fattn-vec.cuh     |  25 +++++--
- ggml/src/ggml-cuda/fattn-wmma-f16.cu |   4 +-
- ggml/src/ggml-cuda/fattn.cu          |   9 +++
- ggml/src/ggml.c                      |  14 ++++
- src/llama-graph.cpp                  |  23 ++++--
- src/llama-graph.h                    |   3 +-
- src/llama-kv-cache.cpp               |  31 ++++++++
- src/llama-kv-cache.h                 |   4 +
- src/paged-attn.cpp                   | 107 +++++++++++++++++++++++++++
- src/paged-attn.h                     |  18 +++++
- 15 files changed, 248 insertions(+), 22 deletions(-)
-
-diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
-index d6807b6..823f5a9 100644
---- a/ggml/include/ggml.h
-+++ b/ggml/include/ggml.h
-@@ -2427,6 +2427,12 @@ extern "C" {
-             struct ggml_tensor * a,
-             struct ggml_tensor * sinks);
- 
-+    // [paged] optional block table in src[5]: I32 [n_kv_logical, n_stream]; maps each
-+    // logical KV index to the physical cell within K/V. nullptr => stock contiguous read.
-+    GGML_API void ggml_flash_attn_ext_set_block_table(
-+            struct ggml_tensor * a,
-+            struct ggml_tensor * block_table);
-+
-     // TODO: needs to be adapted to ggml_flash_attn_ext
-     GGML_API struct ggml_tensor * ggml_flash_attn_back(
-            struct ggml_context * ctx,
-diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
-index 74611dc..63c07a2 100644
---- a/ggml/src/ggml-cpu/ops.cpp
-+++ b/ggml/src/ggml-cpu/ops.cpp
-@@ -8330,6 +8330,8 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk(
-     const ggml_tensor * v     = dst->src[2];
-     const ggml_tensor * mask  = dst->src[3];
-     const ggml_tensor * sinks = dst->src[4];
-+    const ggml_tensor * block_table = dst->src[5]; // [paged] logical->physical cell map (src[5])
-+    const int32_t     * bt    = block_table ? (const int32_t *) block_table->data : nullptr;
- 
-     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
-     GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
-@@ -8449,7 +8451,9 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk(
- 
-             float s; // KQ value
- 
--            const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3);
-+            // [paged] map the logical KV index ic to its physical cell via the block table.
-+            const int64_t ic_phys = bt ? (int64_t) bt[ik3*nek1 + ic] : ic;
-+            const char * k_data = (const char *) k->data + ( ic_phys*nbk1 + ik2*nbk2 + ik3*nbk3);
-             kq_vec_dot(DK, &s, 0, k_data, 0, Q_q, 0, 1);
- 
-             s = s*scale; // scale KQ value
-@@ -8465,7 +8469,7 @@ static void ggml_compute_forward_flash_attn_ext_f16_one_chunk(
-             float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value
-             float vs = 1.0f; // post-softmax KQ value, expf(s - M)
- 
--            const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3));
-+            const char * v_data = ((const char *) v->data + (ic_phys*nbv1 + iv2*nbv2 + iv3*nbv3));
- 
-             if (v->type == GGML_TYPE_F16) {
-                 if (s > M) {
-@@ -9021,7 +9025,7 @@ static void ggml_compute_forward_flash_attn_ext_f16(
-         const int64_t dr = (nr + nchunk - 1) / nchunk;
- 
-         static constexpr int64_t Q_TILE_SZ  = ggml_fa_tile_config::Q;
--        bool use_tiled = !use_ref &&
-+        bool use_tiled = !use_ref && dst->src[5] == nullptr && // [paged] one_chunk honors the block table
-                                (q->type == GGML_TYPE_F32 &&
-                                 kv_is_f32_or_f16 &&
-                                 k->type == v->type &&
-diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
-index 8dfa51a..3c6ddd5 100644
---- a/ggml/src/ggml-cuda/fattn-common.cuh
-+++ b/ggml/src/ggml-cuda/fattn-common.cuh
-@@ -39,7 +39,8 @@ typedef void (* fattn_kernel_t)(
-                             const int32_t nb11, const int32_t nb12, const int64_t nb13,
-                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
-                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
--                            const int32_t nb31, const int32_t nb32, const int64_t nb33);
-+                            const int32_t nb31, const int32_t nb32, const int64_t nb33,
-+        const int  * __restrict__ block_table);
- 
- typedef float (*vec_dot_KQ_t)(
-     const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);
-@@ -981,6 +982,8 @@ void launch_fattn(
- 
-     const ggml_tensor * mask  = dst->src[3];
-     const ggml_tensor * sinks = dst->src[4];
-+    const ggml_tensor * block_table = dst->src[5]; // [paged] optional logical->physical map
-+    const int * bt_ptr = block_table ? (const int *) block_table->data : nullptr;
- 
-     ggml_tensor * KQV = dst;
- 
-@@ -1217,7 +1220,8 @@ void launch_fattn(
-         K->ne[0], K->ne[1], K->ne[2], K->ne[3], nb11, nb12, nb13,
-         nb21, nb22, nb23,
-         mask ? mask->ne[1] : 0, mask ? mask->ne[2] : 0, mask ? mask->ne[3] : 0,
--        mask ? mask->nb[1] : 0, mask ? mask->nb[2] : 0, mask ? mask->nb[3] : 0
-+        mask ? mask->nb[1] : 0, mask ? mask->nb[2] : 0, mask ? mask->nb[3] : 0,
-+        bt_ptr
-     );
-     CUDA_CHECK(cudaGetLastError());
- 
-diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
-index 83478a0..0a92cd6 100644
---- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
-+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
-@@ -1723,7 +1723,9 @@ static __global__ void flash_attn_ext_f16(
-                             const int32_t nb11, const int32_t nb12, const int64_t nb13,
-                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
-                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
--                            const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-+                            const int32_t nb31, const int32_t nb32, const int64_t nb33,
-+        const int  * __restrict__ block_table) {
-+    GGML_UNUSED(block_table); // [paged] block table is honored only by the vec kernel
-     ggml_cuda_pdl_sync(); // TODO optimize placement
- #if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE))
-     const char * GGML_CUDA_RESTRICT Q        = Q_ptr;
-diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh
-index 0a09981..0ff14e6 100644
---- a/ggml/src/ggml-cuda/fattn-tile.cuh
-+++ b/ggml/src/ggml-cuda/fattn-tile.cuh
-@@ -808,7 +808,9 @@ static __global__ void flash_attn_tile(
-                             const int32_t nb11, const int32_t nb12, const int64_t nb13,
-                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
-                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
--                            const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-+                            const int32_t nb31, const int32_t nb32, const int64_t nb33,
-+        const int  * __restrict__ block_table) {
-+    GGML_UNUSED(block_table); // [paged] block table is honored only by the vec kernel
- #ifdef FLASH_ATTN_AVAILABLE
-     const char * GGML_CUDA_RESTRICT Q        = Q_ptr;
-     const char * GGML_CUDA_RESTRICT K        = K_ptr;
-diff --git a/ggml/src/ggml-cuda/fattn-vec.cuh b/ggml/src/ggml-cuda/fattn-vec.cuh
-index 69dd936..a09e2fb 100644
---- a/ggml/src/ggml-cuda/fattn-vec.cuh
-+++ b/ggml/src/ggml-cuda/fattn-vec.cuh
-@@ -39,7 +39,8 @@ static __global__ void flash_attn_ext_vec(
-                             const int32_t nb11, const int32_t nb12, const int64_t nb13,
-                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
-                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
--                            const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-+                            const int32_t nb31, const int32_t nb32, const int64_t nb33,
-+        const int  * __restrict__ block_table) {
-     ggml_cuda_pdl_lc();
- #ifdef FLASH_ATTN_AVAILABLE
-     const char * GGML_CUDA_RESTRICT Q        = Q_ptr;
-@@ -61,7 +62,7 @@ static __global__ void flash_attn_ext_vec(
-                   nb11, nb12, nb13,
-                   nb21, nb22, nb23,
-                   ne31, ne32, ne33,
--                  nb31, nb32, nb33);
-+                  nb31, nb32, nb33, block_table);
-         NO_DEVICE_CODE;
-         return;
-     }
-@@ -110,6 +111,14 @@ static __global__ void flash_attn_ext_vec(
-     K += nb13*sequence + nb12*(head / gqa_ratio);
-     V += nb23*sequence + nb22*(head / gqa_ratio);
- 
-+    // [paged] in-kernel block-table read: logical KV index j -> physical cell
-+    // block_table[sequence*ne11 + j]; read K0 + cell*nb11 / V0 + cell*nb21. The
-+    // mask/KV_max stay logical (the table is in token-position order). nullptr =>
-+    // the stock contiguous read below.
-+    const char * GGML_CUDA_RESTRICT K0 = K;
-+    const char * GGML_CUDA_RESTRICT V0 = V;
-+    const int  * GGML_CUDA_RESTRICT bt = block_table ? block_table + (size_t) sequence*ne11 : nullptr;
-+
-     const half * maskh  = (const half  *) (mask + nb33*(sequence % ne33) + nb31*ic0);
- 
-     const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
-@@ -267,10 +276,11 @@ static __global__ void flash_attn_ext_vec(
- #pragma unroll
-         for (int i_KQ_0 = 0; i_KQ_0 < nthreads_KQ; ++i_KQ_0) {
-             const int i_KQ = threadIdx.y*WARP_SIZE + (nthreads_KQ == WARP_SIZE ? 0 : (threadIdx.x & ~(nthreads_KQ-1))) + i_KQ_0;
-+            const char * GGML_CUDA_RESTRICT K_blk = bt ? (K0 + (int64_t) bt[k_VKQ_0 + i_KQ]*nb11) : (K + i_KQ*nb11);
- 
- #pragma unroll
-             for (int j = 0; j < ncols; ++j) {
--                float sum = vec_dot_KQ(K + i_KQ*nb11, Q_reg[j], Q_i32[j], Q_ds[j]);
-+                float sum = vec_dot_KQ(K_blk, Q_reg[j], Q_i32[j], Q_ds[j]);
-                 sum = warp_reduce_sum<nthreads_KQ>(sum);
- 
-                 if (use_logit_softcap) {
-@@ -324,6 +334,7 @@ static __global__ void flash_attn_ext_vec(
- #pragma unroll
-         for (int k0 = 0; k0 < WARP_SIZE; k0 += V_cols_per_iter) {
-             const int k = threadIdx.y*WARP_SIZE + k0 + (nthreads_V == WARP_SIZE ? 0 : threadIdx.x / nthreads_V);
-+            const char * GGML_CUDA_RESTRICT V_blk = bt ? (V0 + (int64_t) bt[k_VKQ_0 + k]*nb21) : (V + k*nb21);
- 
- #ifdef V_DOT2_F32_F16_AVAILABLE
-             half2 KQ_k[ncols];
-@@ -336,14 +347,14 @@ static __global__ void flash_attn_ext_vec(
-                 half2 tmp[V_rows_per_thread/2];
-                 if constexpr (type_V == GGML_TYPE_BF16) {
-                     float2 tmp_f[V_rows_per_thread/2];
--                    dequantize_V(V + k*nb21, tmp_f,
-+                    dequantize_V(V_blk, tmp_f,
-                         2*i_VKQ_0 + (nthreads_V == WARP_SIZE ? threadIdx.x : threadIdx.x % nthreads_V)*V_rows_per_thread);
- #pragma unroll
-                     for (int i_VKQ_1 = 0; i_VKQ_1 < V_rows_per_thread/2; ++i_VKQ_1) {
-                         tmp[i_VKQ_1] = __float22half2_rn(tmp_f[i_VKQ_1]);
-                     }
-                 } else {
--                    dequantize_V(V + k*nb21, tmp,
-+                    dequantize_V(V_blk, tmp,
-                         2*i_VKQ_0 + (nthreads_V == WARP_SIZE ? threadIdx.x : threadIdx.x % nthreads_V)*V_rows_per_thread);
-                 }
- #pragma unroll
-@@ -363,7 +374,7 @@ static __global__ void flash_attn_ext_vec(
- #pragma unroll
-             for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V*V_rows_per_thread/2) {
-                 float2 tmp[V_rows_per_thread/2];
--                dequantize_V(V + k*nb21, tmp,
-+                dequantize_V(V_blk, tmp,
-                     2*i_VKQ_0 + (nthreads_V == WARP_SIZE ? threadIdx.x : threadIdx.x % nthreads_V)*V_rows_per_thread);
- #pragma unroll
-                 for (int i_VKQ_1 = 0; i_VKQ_1 < V_rows_per_thread/2; ++i_VKQ_1) {
-@@ -522,7 +533,7 @@ static __global__ void flash_attn_ext_vec(
-               nb11, nb12, nb13,
-               nb21, nb22, nb23,
-               ne31, ne32, ne33,
--              nb31, nb32, nb33);
-+              nb31, nb32, nb33, block_table);
-     NO_DEVICE_CODE;
- #endif // FLASH_ATTN_AVAILABLE
- }
-diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cu b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
-index 6850716..5357849 100644
---- a/ggml/src/ggml-cuda/fattn-wmma-f16.cu
-+++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cu
-@@ -44,7 +44,9 @@ static __global__ void flash_attn_ext_f16(
-                             const int32_t nb11, const int32_t nb12, const int64_t nb13,
-                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
-                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
--                            const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-+                            const int32_t nb31, const int32_t nb32, const int64_t nb33,
-+        const int  * __restrict__ block_table) {
-+    GGML_UNUSED(block_table); // [paged] block table is honored only by the vec kernel
- #if defined(FLASH_ATTN_AVAILABLE) && (defined(GGML_HIP_ROCWMMA_FATTN) && defined(GGML_USE_WMMA_FATTN))
-     const char * GGML_CUDA_RESTRICT Q        = Q_ptr;
-     const char * GGML_CUDA_RESTRICT K        = K_ptr;
-diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
-index d6c501b..e3771ee 100644
---- a/ggml/src/ggml-cuda/fattn.cu
-+++ b/ggml/src/ggml-cuda/fattn.cu
-@@ -574,6 +574,15 @@ size_t ggml_cuda_flash_attn_ext_get_alloc_size(int device, const ggml_tensor * d
- 
- void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-     ggml_cuda_set_device(ctx.device);
-+
-+    // [paged] the block table (src[5]) is only honored by the vec kernel's
-+    // in-kernel read; force it. build_attn only sets it for a vec-supported
-+    // 1-token-per-stream decode shape.
-+    if (dst->src[5] != nullptr) {
-+        ggml_cuda_flash_attn_ext_vec(ctx, dst);
-+        return;
-+    }
-+
-     switch (ggml_cuda_get_best_fattn_kernel(ggml_cuda_get_device(), dst)) {
-         case BEST_FATTN_KERNEL_NONE:
-             GGML_ABORT("fatal error");
-diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
-index b43016c..adbe52b 100644
---- a/ggml/src/ggml.c
-+++ b/ggml/src/ggml.c
-@@ -5442,6 +5442,20 @@ void ggml_flash_attn_ext_add_sinks(
-     a->src[4] = sinks;
- }
- 
-+void ggml_flash_attn_ext_set_block_table(
-+        struct ggml_tensor * a,
-+        struct ggml_tensor * block_table) {
-+    if (!block_table) {
-+        a->src[5] = NULL;
-+        return;
-+    }
-+
-+    GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT);
-+    GGML_ASSERT(block_table->type == GGML_TYPE_I32);
-+
-+    a->src[5] = block_table;
-+}
-+
- // ggml_flash_attn_back
- 
- struct ggml_tensor * ggml_flash_attn_back(
-diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
-index b59d2a5..abdb48d 100644
---- a/src/llama-graph.cpp
-+++ b/src/llama-graph.cpp
-@@ -2074,7 +2074,8 @@ ggml_tensor * llm_graph_context::build_attn_mha(
-          ggml_tensor * sinks,
-          ggml_tensor * v_mla,
-                float   kq_scale,
--                 int   il) const {
-+                 int   il,
-+         ggml_tensor * block_table) const {
-     const bool v_trans = v->nb[1] > v->nb[2];
- 
-     // split the batch into streams if needed
-@@ -2109,6 +2110,9 @@ ggml_tensor * llm_graph_context::build_attn_mha(
-                                   hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
-         cb(cur, LLAMA_TENSOR_NAME_FATTN, il);
- 
-+        if (block_table) {
-+            ggml_flash_attn_ext_set_block_table(cur, block_table);
-+        }
-         ggml_flash_attn_ext_add_sinks(cur, sinks);
-         ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32);
- 
-@@ -2358,12 +2362,19 @@ ggml_tensor * llm_graph_context::build_attn(
-     ggml_tensor * k = mctx_cur->get_k(ctx0, il);
-     ggml_tensor * v = mctx_cur->get_v(ctx0, il);
- 
--    // [paged 0003] gather K, V and the mask to the sequence's used cells only
--    //   (no-op unless env LLAMA_KV_PAGED is set).
--    ggml_tensor * kq_mask_g = kq_mask;
--    paged_attn::gather(ctx0, res, mctx_cur, &k, &v, &kq_mask_g);
-+    // [paged] decode read: when paging is active and this is a 1-token-per-stream
-+    //   decode step, present K/V as n_gather views + a block table so the fattn
-+    //   kernel reads the sequence's cells in-kernel (no get_rows of K/V). Else
-+    //   fall back to the gather-read (prefill, transposed V, or env off). All a
-+    //   no-op unless env LLAMA_KV_PAGED is set => stock byte-identical.
-+    ggml_tensor * kq_mask_g   = kq_mask;
-+    ggml_tensor * block_table = nullptr;
-+    const bool is_decode = (q_cur->ne[2] == k->ne[3]); // 1 query token per stream
-+    if (!(is_decode && paged_attn::in_kernel_decode(ctx0, res, mctx_cur, &k, &v, &kq_mask_g, &block_table))) {
-+        paged_attn::gather(ctx0, res, mctx_cur, &k, &v, &kq_mask_g);
-+    }
- 
--    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask_g, sinks, v_mla, kq_scale, il);
-+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask_g, sinks, v_mla, kq_scale, il, block_table);
-     cb(cur, "kqv_out", il);
- 
-     if (inp->self_v_rot) {
-diff --git a/src/llama-graph.h b/src/llama-graph.h
-index 5e8a658..c95ae49 100644
---- a/src/llama-graph.h
-+++ b/src/llama-graph.h
-@@ -969,7 +969,8 @@ struct llm_graph_context {
-             ggml_tensor * sinks,   // [n_head_q]
-             ggml_tensor * v_mla,   // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
-                   float   kq_scale,
--                    int   il) const;
-+                    int   il,
-+            ggml_tensor * block_table = nullptr) const; // [paged] optional src[5] block table
- 
-     llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
- 
-diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
-index 7510ff9..0351f86 100644
---- a/src/llama-kv-cache.cpp
-+++ b/src/llama-kv-cache.cpp
-@@ -1474,6 +1474,33 @@ void llama_kv_cache::get_gather_idxs(int32_t * dst, uint32_t n_kv, const slot_in
-     }
- }
- 
-+void llama_kv_cache::get_block_table(int32_t * dst, uint32_t n_blk, uint32_t n_kv, const slot_info & sinfo) const {
-+    const uint32_t ns = sinfo.s1 - sinfo.s0 + 1;
-+    for (uint32_t j = 0; j < ns; ++j) {
-+        const auto & cells = v_cells[sinfo.s0 + j];
-+        const uint32_t n = std::min<uint32_t>(n_kv, cells.size());
-+        std::vector<std::pair<llama_pos, int32_t>> pc;
-+        pc.reserve(n);
-+        int32_t pad = -1;
-+        for (uint32_t i = 0; i < n; ++i) {
-+            if (!cells.is_empty(i)) {
-+                pc.emplace_back(cells.pos_get(i), (int32_t) i);
-+            } else if (pad < 0) {
-+                pad = (int32_t) i;
-+            }
-+        }
-+        std::sort(pc.begin(), pc.end());
-+        int32_t * col = dst + (size_t) j * n_blk;
-+        for (size_t k = 0; k < pc.size(); ++k) {
-+            col[k] = pc[k].second;
-+        }
-+        const int32_t padv = (pad >= 0) ? pad : (pc.empty() ? 0 : pc.back().second);
-+        for (uint32_t k = (uint32_t) pc.size(); k < n_blk; ++k) {
-+            col[k] = padv;
-+        }
-+    }
-+}
-+
- ggml_tensor * llama_kv_cache::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
-     GGML_UNUSED(sinfo);
- 
-@@ -2773,6 +2800,10 @@ void llama_kv_cache_context::get_gather_idxs(int32_t * dst) const {
-     kv->get_gather_idxs(dst, n_kv, sinfos[i_cur]);
- }
- 
-+void llama_kv_cache_context::get_block_table(int32_t * dst, uint32_t n_blk) const {
-+    kv->get_block_table(dst, n_blk, n_kv, sinfos[i_cur]);
-+}
-+
- ggml_tensor * llama_kv_cache_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
-     return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]);
- }
-diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
-index f374ac6..e9980b6 100644
---- a/src/llama-kv-cache.h
-+++ b/src/llama-kv-cache.h
-@@ -176,6 +176,9 @@ public:
-     //   gather-read. get_n_gather returns the max count across streams.
-     uint32_t get_n_gather(uint32_t n_kv, const slot_info & sinfo) const;
-     void     get_gather_idxs(int32_t * dst, uint32_t n_kv, const slot_info & sinfo) const;
-+    // [paged inc1] block table [n_blk, n_stream] (position order, padded to n_blk
-+    //   per column with a masked empty cell) for the in-kernel paged read.
-+    void     get_block_table(int32_t * dst, uint32_t n_blk, uint32_t n_kv, const slot_info & sinfo) const;
- 
-     // store k_cur and v_cur in the cache based on the provided head location
-     ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const;
-@@ -386,6 +389,7 @@ public:
-     //   current ubatch's stream).
-     uint32_t get_n_gather() const;
-     void     get_gather_idxs(int32_t * dst) const;
-+    void     get_block_table(int32_t * dst, uint32_t n_blk) const;
- 
-     // store k_cur and v_cur in the cache based on the provided head location
-     // note: the heads in k_cur and v_cur should be laid out contiguously in memory
-diff --git a/src/paged-attn.cpp b/src/paged-attn.cpp
-index ade75e8..8eebeaa 100644
---- a/src/paged-attn.cpp
-+++ b/src/paged-attn.cpp
-@@ -43,6 +43,25 @@ public:
-     ggml_tensor * idxs;
- };
- 
-+// Block table filler for the in-kernel paged read: fills an I32 [n_blk, n_stream]
-+// tensor with each stream's position-ordered cells, padded to n_blk (per column)
-+// with a masked empty cell, by delegating to the kv-cache context.
-+class input_block_table : public llm_graph_input_i {
-+public:
-+    input_block_table(const llama_kv_cache_context * mctx, ggml_tensor * idxs, uint32_t n_blk)
-+        : mctx(mctx), idxs(idxs), n_blk(n_blk) {}
-+
-+    void set_input(const llama_ubatch * ubatch) override {
-+        GGML_UNUSED(ubatch);
-+        GGML_ASSERT(idxs && ggml_backend_buffer_is_host(idxs->buffer));
-+        mctx->get_block_table((int32_t *) idxs->data, n_blk);
-+    }
-+
-+    const llama_kv_cache_context * mctx;
-+    ggml_tensor * idxs;
-+    uint32_t n_blk;
-+};
-+
- } // namespace
- 
- void gather(ggml_context * ctx0,
-@@ -125,4 +144,92 @@ void gather(ggml_context * ctx0,
-     }
- }
- 
-+bool in_kernel_decode(ggml_context * ctx0,
-+                      llm_graph_result * res,
-+                      const llama_kv_cache_context * mctx,
-+                      ggml_tensor ** k,
-+                      ggml_tensor ** v,
-+                      ggml_tensor ** kq_mask,
-+                      ggml_tensor ** block_table) {
-+    if (!active()) {
-+        return false;
-+    }
-+    // Bench escape hatch: LLAMA_KV_PAGED_GATHER=1 forces the old gather-read decode
-+    // path (for a same-build BEFORE/AFTER decode-step comparison). Dev-only.
-+    static const bool force_gather = (std::getenv("LLAMA_KV_PAGED_GATHER") != nullptr);
-+    if (force_gather) {
-+        return false;
-+    }
-+
-+    ggml_tensor * K = *k;
-+    ggml_tensor * V = *v;
-+    ggml_tensor * M = *kq_mask;
-+
-+    const int64_t n_stream = K->ne[3];
-+    GGML_ASSERT(M->ne[3] == n_stream);
-+
-+    const int64_t n_gather = (int64_t) mctx->get_n_gather();
-+    if (n_gather <= 0) {
-+        // Worst-case reserve / nothing placed yet: keep the dense [0,n_kv) read.
-+        return false;
-+    }
-+
-+    // The in-kernel read addresses V along its d-major (non-transposed) axis. If
-+    // the cache stores V transposed, fall back to gather() (which normalizes it).
-+    if (V->nb[1] > V->nb[2]) {
-+        return false;
-+    }
-+
-+    if (debug()) {
-+        static int64_t once = 0;
-+        if (once++ < 2) {
-+            fprintf(stderr, "[paged-attn] in-kernel decode n_stream=%lld n_kv=%lld n_gather=%lld\n",
-+                    (long long) n_stream, (long long) K->ne[2], (long long) n_gather);
-+        }
-+    }
-+
-+    // Block table [n_gather, n_stream]: column s holds stream s's non-empty cells
-+    // in token-POSITION order (identical to the gather index, so the reduction
-+    // order matches stock bit-for-bit), padded with a masked empty cell. Filled
-+    // at set_input from the kv-cache (get_gather_idxs), exactly like the gather.
-+    // Pad the logical length to FATTN_KQ_STRIDE (256) so the CUDA fattn vec kernel
-+    // reads fixed 128-wide KV blocks without overrun and the KV_max mask scan
-+    // engages; padded entries point at a masked empty cell (0 contribution). Stays
-+    // <= n_kv since n_kv is itself padded to 256 and n_gather <= n_kv.
-+    int64_t n_view = GGML_PAD(n_gather, 256);
-+    if (n_view > K->ne[2]) {
-+        n_view = K->ne[2];
-+    }
-+
-+    ggml_tensor * idx = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_view, n_stream);
-+    ggml_set_input(idx);
-+    res->add_input(llm_graph_input_ptr(new input_block_table(mctx, idx, (uint32_t) n_view)));
-+
-+    // Present K and V as [d, h, n_view, ns] VIEWS of the full physical window:
-+    // identical per-cell (nb1,nb2) and per-stream (nb3) strides, only the cell
-+    // dim shrinks to n_view. NOT materialized - the kernel reads in place.
-+    *k = ggml_view_4d(ctx0, K, K->ne[0], K->ne[1], n_view, n_stream,
-+                      K->nb[1], K->nb[2], K->nb[3], 0);
-+    *v = ggml_view_4d(ctx0, V, V->ne[0], V->ne[1], n_view, n_stream,
-+                      V->nb[1], V->nb[2], V->nb[3], 0);
-+
-+    // Compact the mask to [n_gather, n_tps, 1, ns] in the same position order so
-+    // the kernel's logical mask index aligns with the block table. Cheap: the
-+    // mask is ~(d*h) smaller than K/V, which is why only its get_rows remains.
-+    {
-+        ggml_tensor * m = ggml_reshape_3d(ctx0, M, M->ne[0], M->ne[1], n_stream);
-+        m = ggml_cont(ctx0, ggml_transpose(ctx0, m));
-+        m = ggml_get_rows(ctx0, m, idx);
-+        m = ggml_cont(ctx0, ggml_transpose(ctx0, m));
-+        m = ggml_reshape_4d(ctx0, m, n_view, M->ne[1], 1, n_stream);
-+        if (M->type != m->type) {
-+            m = ggml_cast(ctx0, m, M->type);
-+        }
-+        *kq_mask = m;
-+    }
-+
-+    *block_table = idx;
-+    return true;
-+}
-+
- } // namespace paged_attn
-diff --git a/src/paged-attn.h b/src/paged-attn.h
-index c5b7bd7..23e2184 100644
---- a/src/paged-attn.h
-+++ b/src/paged-attn.h
-@@ -37,4 +37,22 @@ void gather(ggml_context * ctx0,
-             ggml_tensor ** v,
-             ggml_tensor ** kq_mask);
- 
-+// [paged inc1] In-kernel paged decode read. Instead of materializing the
-+// sequence's cells (gather()), present K and V as n_gather-length VIEWS of the
-+// full physical window and return the position-ordered physical-cell index list
-+// as a block table (src[5] of ggml_flash_attn_ext). The fattn kernel/op then
-+// reads K_base + block_table[j]*nb in-kernel, removing the get_rows of K and V
-+// (the bulk of the gather). On return (true): *k,*v point at the views, *kq_mask
-+// at the compacted mask, *block_table at the I32 [n_gather, n_stream] index.
-+// Returns false (leaving *k,*v,*kq_mask untouched) when the in-kernel path does
-+// not apply - env off, nothing placed, or a transposed V cache - so the caller
-+// keeps the dense gather()/contiguous read.
-+bool in_kernel_decode(ggml_context * ctx0,
-+                      llm_graph_result * res,
-+                      const llama_kv_cache_context * mctx,
-+                      ggml_tensor ** k,
-+                      ggml_tensor ** v,
-+                      ggml_tensor ** kq_mask,
-+                      ggml_tensor ** block_table);
-+
- } // namespace paged_attn
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0010-paged-tile-in-kernel-read-and-dispatch-guard-env-LLAMA_KV_PAGED.patch

@@ -1,269 +0,0 @@
-From 9ac56933abd5de4a1f349c811c2d74aab09f7ab1 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Mon, 22 Jun 2026 22:36:09 +0200
-Subject: [PATCH] paged tile in-kernel decode read + dispatch guard (env
- LLAMA_KV_PAGED) - patch 0010
-
-Increment 2 (robustness, ~0 headline ms): make the paged in-kernel decode read
-safe against silent mis-routing, and plumb the same read into the tile kernel
-for the increment-3 GQA head-group work.
-
-fattn-tile.cuh: graft the patch-0009 phys(j) block-table read (mirror of
-fattn-vec.cuh). Both flash_attn_tile_load_tile overloads, flash_attn_tile_iter_KQ
-(K) and flash_attn_tile_iter (V) take an optional per-sequence block table; a row
-i is read from base + block_table[row_base + i]*stride instead of base + i*stride.
-The table defaults to nullptr (default args + a null bt_seq when src[5] is unset),
-so every existing non-paged caller is byte-identical to stock. The mask / KV_max
-stay logical (token-position order), as in vec.
-
-fattn.cu: DISPATCH GUARD. When the block table (src[5]) is present, route ONLY to
-the vec or tile kernel and never fall through to the best-kernel switch. The
-mma/wmma kernels GGML_UNUSED the table and would silently read the wrong
-(contiguous physical) cells; the guard makes that unreachable. The vec dispatcher
-GGML_ABORTs for an unsupported D/type rather than mis-reading. Default route is vec
-(the inc-1 byte-validated path). LLAMA_KV_PAGED_DISPATCH_LOG=1 prints the routed
-kernel once.
-
-Gates: CPU byte-identical paged-on vs off (Qwen3-0.6B, build-cpu) PASS. GPU
-vec-paged == stock at -s 1 PASS. Dispatch confirmed VEC for the real decode shape:
-Qwen3-0.6B Q ne=[128,1,16,1] and Qwen3-32B NVFP4 Q ne=[128,1,64,N] both route to
-vec, matching the nsys profile (flash_attn_ext_vec).
-
-The tile graft is plumbed for increment-3 GQA head-group reuse but is EXPERIMENTAL
-and NOT yet byte-validated (LLAMA_KV_PAGED_TILE=1). A tile-vs-tile gate shows
-tile-paged diverging from tile-stock at the first cross-tile KV depth: the
-GQA-grouped (ncols2>1) tile path reads a full nbatch_fa-row tile with
-oob_check=false while the compacted paged mask is not padded to cover the tile, so
-past-end rows leak. vec bounds its KV walk by KV_max and is unaffected. Bounding
-the tile path is increment-3 work; the default vec route and all stock paths are
-untouched.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- ggml/src/ggml-cuda/fattn-tile.cuh | 45 ++++++++++++++++++++-----------
- ggml/src/ggml-cuda/fattn.cu       | 38 +++++++++++++++++++++++---
- 2 files changed, 64 insertions(+), 19 deletions(-)
-
-diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh
-index 0ff14e6..bb84d61 100644
---- a/ggml/src/ggml-cuda/fattn-tile.cuh
-+++ b/ggml/src/ggml-cuda/fattn-tile.cuh
-@@ -373,7 +373,8 @@ static constexpr __device__ int ggml_cuda_fattn_tile_get_nbatch_K(const int DKQ,
- // TODO: deduplicate with mma-f16
- template<int warp_size, int nwarps, int I, int J, int J_padding, bool oob_check>
- static __device__ __forceinline__ void flash_attn_tile_load_tile(
--        const half2 * const __restrict__ KV, half2 * const __restrict__ tile_KV, const int stride_KV, const int i_sup) {
-+        const half2 * const __restrict__ KV, half2 * const __restrict__ tile_KV, const int stride_KV, const int i_sup,
-+        const int * const __restrict__ block_table = nullptr, const int row_base = 0) {
-     constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes();
-     constexpr int cpy_ne = cpy_nb / 4;
- 
-@@ -402,9 +403,11 @@ static __device__ __forceinline__ void flash_attn_tile_load_tile(
-                     const int j = j0*cpy_ne + (stride_j == warp_size ? threadIdx.x : threadIdx.x % stride_j)*cpy_ne;
- 
-                     const __align__(16) half2 zero[cpy_ne] = {{0.0f, 0.0f}};
-+                    // [paged] remap the row through the block table (nullptr => stock contiguous read).
-+                    const half2 * const KV_row = block_table ? KV + (int64_t) block_table[row_base + i]*stride_KV : KV + i*stride_KV;
-                     ggml_cuda_memcpy_1<cpy_nb>(
-                         tile_KV + i*(J/2 + J_padding) + j,
--                        !oob_check || i < i_sup ? KV + i*stride_KV + j : zero);
-+                        !oob_check || i < i_sup ? KV_row + j : zero);
-                 }
-             }
-         }
-@@ -423,7 +426,8 @@ static __device__ __forceinline__ void flash_attn_tile_load_tile(
- 
- template<int warp_size, int nwarps, int I, int J, int J_padding, bool oob_check>
- static __device__ __forceinline__ void flash_attn_tile_load_tile(
--        const half2 * const __restrict__ KV, float * const __restrict__ tile_KV, const int stride_KV, const int i_sup) {
-+        const half2 * const __restrict__ KV, float * const __restrict__ tile_KV, const int stride_KV, const int i_sup,
-+        const int * const __restrict__ block_table = nullptr, const int row_base = 0) {
-     constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes();
-     constexpr int cpy_ne = cpy_nb / 4;
- 
-@@ -453,8 +457,10 @@ static __device__ __forceinline__ void flash_attn_tile_load_tile(
- 
-                     const half2 zero[cpy_ne/2] = {{0.0f, 0.0f}};
-                     __align__(16) half2 tmp_h2[cpy_ne/2];
-+                    // [paged] remap the row through the block table (nullptr => stock contiguous read).
-+                    const half2 * const KV_row = block_table ? KV + (int64_t) block_table[row_base + i]*stride_KV : KV + i*stride_KV;
-                     ggml_cuda_memcpy_1<sizeof(tmp_h2)>(
--                        tmp_h2, !oob_check || i < i_sup ? KV + i*stride_KV + j : zero);
-+                        tmp_h2, !oob_check || i < i_sup ? KV_row + j : zero);
- 
-                     __align__(16) float2 tmp_f2[cpy_ne/2];
- #pragma unroll
-@@ -487,6 +493,7 @@ static __device__ __forceinline__ void flash_attn_tile_iter_KQ(
-         const int k_VKQ_0,
-         const int k_VKQ_sup,
-         const int k_KQ_0,
-+        const int * const __restrict__ block_table,
-         float * KQ_acc) {
-     constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes();
-     constexpr int cpy_ne = cpy_nb / 4;
-@@ -495,8 +502,10 @@ static __device__ __forceinline__ void flash_attn_tile_iter_KQ(
-     constexpr int cpw   = ncols > nwarps ? ncols/nwarps : 1; // Q columns per warp
-     constexpr int np    = nwarps > ncols ? nwarps/ncols : 1; // number of parallel warps per Q column
- 
-+    // [paged] when block_table is set K_h2 is the un-offset base; the table supplies the row.
-+    const half2 * const K_base = block_table ? (K_h2 + k_KQ_0/2) : (K_h2 + int64_t(k_VKQ_0)*stride_K2 + k_KQ_0/2);
-     flash_attn_tile_load_tile<warp_size, nwarps, nbatch_fa, nbatch_K, cpy_ne, oob_check>
--        (K_h2 + int64_t(k_VKQ_0)*stride_K2 + k_KQ_0/2, KV_tmp, stride_K2, k_VKQ_sup);
-+        (K_base, KV_tmp, stride_K2, k_VKQ_sup, block_table, k_VKQ_0);
-     __syncthreads();
- 
- #ifdef FAST_FP16_AVAILABLE
-@@ -572,7 +581,8 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
-         T_acc * const VKQ,
-         const int k_VKQ_0,
-         const int k_VKQ_max,
--        const int col_Q_0) {
-+        const int col_Q_0,
-+        const int * const __restrict__ block_table) {
-     constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes();
-     constexpr int cpy_ne = cpy_nb / 4;
- 
-@@ -605,12 +615,12 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
- #pragma unroll
-     for (int k_KQ_0 = 0; k_KQ_0 < DKQ - nbatch_K_last; k_KQ_0 += nbatch_K) {
-         flash_attn_tile_iter_KQ<warp_size, nwarps, ncols1, ncols2, DKQ, nbatch_fa, nbatch_K, use_logit_softcap, oob_check>(
--            Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc);
-+            Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, block_table, KQ_acc);
-     }
-     if (nbatch_K_last > 0) {
-         constexpr int k_KQ_0 = DKQ - nbatch_K_last;
-         flash_attn_tile_iter_KQ<warp_size, nwarps, ncols1, ncols2, DKQ, nbatch_fa, nbatch_K_last, use_logit_softcap, oob_check>(
--            Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, KQ_acc);
-+            Q_tmp, K_h2, KV_tmp, stride_K2, k_VKQ_0, k_VKQ_sup, k_KQ_0, block_table, KQ_acc);
-     }
- 
-     // Apply logit softcap + mask, update KQ_max:
-@@ -715,8 +725,10 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
-     static_assert(nbatch_V % np == 0, "bad nbatch_V");
- #pragma unroll
-     for (int k0 = 0; k0 < nbatch_fa; k0 += nbatch_V) {
-+        // [paged] when block_table is set V_h2 is the un-offset base; the table supplies the row.
-+        const half2 * const V_base = block_table ? V_h2 : (V_h2 + int64_t(k_VKQ_0 + k0)*stride_V2);
-         flash_attn_tile_load_tile<warp_size, nwarps, nbatch_V, DV, 0, oob_check>
--            (V_h2 + int64_t(k_VKQ_0 + k0)*stride_V2, KV_tmp, stride_V2, k_VKQ_sup - k0);
-+            (V_base, KV_tmp, stride_V2, k_VKQ_sup - k0, block_table, k_VKQ_0 + k0);
-         __syncthreads();
- 
- #ifdef FAST_FP16_AVAILABLE
-@@ -810,7 +822,6 @@ static __global__ void flash_attn_tile(
-                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
-                             const int32_t nb31, const int32_t nb32, const int64_t nb33,
-         const int  * __restrict__ block_table) {
--    GGML_UNUSED(block_table); // [paged] block table is honored only by the vec kernel
- #ifdef FLASH_ATTN_AVAILABLE
-     const char * GGML_CUDA_RESTRICT Q        = Q_ptr;
-     const char * GGML_CUDA_RESTRICT K        = K_ptr;
-@@ -837,7 +848,7 @@ static __global__ void flash_attn_tile(
-                   nb11, nb12, nb13,
-                   nb21, nb22, nb23,
-                   ne31, ne32, ne33,
--                  nb31, nb32, nb33);
-+                  nb31, nb32, nb33, block_table);
-         NO_DEVICE_CODE;
-         return;
-     }
-@@ -861,6 +872,10 @@ static __global__ void flash_attn_tile(
-     const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*(head0 / gqa_ratio));
-     const half2 * V_h2 = (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); // K and V have same shape
- 
-+    // [paged] per-sequence logical->physical block table in token-position order
-+    // (mask/KV_max stay logical); nullptr => the stock contiguous read.
-+    const int * const __restrict__ bt_seq = block_table ? block_table + (size_t) sequence*ne11 : nullptr;
-+
-     const half * maskh = mask ? (const half *) (mask + nb33*(sequence % ne33)) : nullptr;
- 
-     const int stride_K2   = nb11 / sizeof(half2);
-@@ -963,14 +978,14 @@ static __global__ void flash_attn_tile(
-             constexpr bool oob_check = false;
-             flash_attn_tile_iter<warp_size, nwarps, ncols1, ncols2, DKQ, DV, nbatch_fa, nbatch_K, use_logit_softcap, oob_check>
-                 (Q_tmp, K_h2, V_h2, maskh, ne01, logit_softcap, slope, KQ, KV_tmp,
--                stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0);
-+                stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0, bt_seq);
-             k_VKQ_0 += gridDim.y*nbatch_fa;
-         }
-         if (k_VKQ_0 < k_VKQ_max) {
-             constexpr bool oob_check = true;
-             flash_attn_tile_iter<warp_size, nwarps, ncols1, ncols2, DKQ, DV, nbatch_fa, nbatch_K, use_logit_softcap, oob_check>
-                 (Q_tmp, K_h2, V_h2, maskh, ne01, logit_softcap, slope, KQ, KV_tmp,
--                stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0);
-+                stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0, bt_seq);
-         }
-     } else {
-         // Branch without out-of-bounds checks.
-@@ -978,7 +993,7 @@ static __global__ void flash_attn_tile(
-             constexpr bool oob_check = false;
-             flash_attn_tile_iter<warp_size, nwarps, ncols1, ncols2, DKQ, DV, nbatch_fa, nbatch_K, use_logit_softcap, oob_check>
-                 (Q_tmp, K_h2, V_h2, maskh, ne01, logit_softcap, slope, KQ, KV_tmp,
--                stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0);
-+                stride_K2, stride_V2, stride_mask, KQ_max, KQ_sum, VKQ, k_VKQ_0, k_VKQ_max, col_Q_0, bt_seq);
-         }
-     }
- 
-@@ -1144,7 +1159,7 @@ static __global__ void flash_attn_tile(
-               nb11, nb12, nb13,
-               nb21, nb22, nb23,
-               ne31, ne32, ne33,
--              nb31, nb32, nb33);
-+              nb31, nb32, nb33, block_table);
-     NO_DEVICE_CODE;
- #endif // FLASH_ATTN_AVAILABLE
- }
-diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
-index e3771ee..afcafa2 100644
---- a/ggml/src/ggml-cuda/fattn.cu
-+++ b/ggml/src/ggml-cuda/fattn.cu
-@@ -575,11 +575,41 @@ size_t ggml_cuda_flash_attn_ext_get_alloc_size(int device, const ggml_tensor * d
- void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-     ggml_cuda_set_device(ctx.device);
- 
--    // [paged] the block table (src[5]) is only honored by the vec kernel's
--    // in-kernel read; force it. build_attn only sets it for a vec-supported
--    // 1-token-per-stream decode shape.
-+    // [paged] DISPATCH GUARD. The block table (src[5]) is read in-kernel ONLY by
-+    // the vec and tile kernels; the mma/wmma kernels GGML_UNUSED it and would
-+    // silently read the wrong (contiguous physical) cells. So when a block table
-+    // is present we route here and NEVER fall through to the best-kernel switch
-+    // below - no decode shape can silently reach an mma/wmma misread. build_attn
-+    // only sets src[5] for the 1-token-per-stream decode shape; the vec
-+    // dispatcher GGML_ABORTs for an unsupported D/type rather than mis-reading,
-+    // and any shape that should not be paged must take the host-side gather path
-+    // (LLAMA_KV_PAGED_GATHER=1) instead.
-+    //
-+    // Default route = vec (inc-1, byte-validated: vec-paged == stock at -s 1 and
-+    // CPU byte-identical). LLAMA_KV_PAGED_TILE=1 routes the same shape to the
-+    // tile kernel; the tile in-kernel read is plumbed (fattn-tile.cuh) for the
-+    // increment-3 GQA head-group reuse, but is EXPERIMENTAL / NOT yet byte-
-+    // validated: the GQA-grouped (ncols2>1) tile path reads a full nbatch_fa tile
-+    // with oob_check=false while the compacted paged mask is not padded to cover
-+    // it, so it diverges from stock. Not for production paged decode until
-+    // increment-3 bounds that path; the default vec route is unaffected.
-     if (dst->src[5] != nullptr) {
--        ggml_cuda_flash_attn_ext_vec(ctx, dst);
-+        static const bool paged_tile = getenv("LLAMA_KV_PAGED_TILE") != nullptr;
-+        if (getenv("LLAMA_KV_PAGED_DISPATCH_LOG") != nullptr) {
-+            static bool logged = false;
-+            if (!logged) {
-+                logged = true;
-+                fprintf(stderr, "[paged] decode src[5] set -> routing to %s (Q ne=[%ld,%ld,%ld,%ld])\n",
-+                    paged_tile ? "TILE(experimental)" : "VEC",
-+                    (long) dst->src[0]->ne[0], (long) dst->src[0]->ne[1],
-+                    (long) dst->src[0]->ne[2], (long) dst->src[0]->ne[3]);
-+            }
-+        }
-+        if (paged_tile) {
-+            ggml_cuda_flash_attn_ext_tile(ctx, dst);
-+        } else {
-+            ggml_cuda_flash_attn_ext_vec(ctx, dst);
-+        }
-         return;
-     }
- 
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0011-paged-decode-route-GQA-grouped-tile-kernel-by-defaul.patch

@@ -1,147 +0,0 @@
-From d5ca5cd756e42214d0003bca815ca91943679b0d Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Tue, 23 Jun 2026 00:18:35 +0200
-Subject: [PATCH] paged decode: route GQA-grouped tile kernel by default (F16,
- gqa>=2) - patch 0011
-
-Increment 3 (the attention lever). In fattn.cu's paged dispatch guard, route the
-in-kernel decode to the tile kernel for the common grouped-query F16 case, and
-keep the inc-1 vec kernel for everything else.
-
-The tile kernel carries native GQA head-group reuse: its ncols2 axis groups the
-q-heads that share one kv-head, so each K/V row is loaded once for the whole
-group instead of once per q-head. vec re-streams each kv-head's K/V once per
-q-head (8x for Qwen3-32B's n_head 64 / n_head_kv 8) and runs at 168 regs ->
-3 blocks/SM = 25% occupancy on GB10; tile is 108-128 regs with native grouping.
-The inc-2 phys(j) block-table read was already plumbed into tile (patch 0010);
-this patch makes it the default for {F16 K and V, gqa_ratio >= 2}.
-
-Routing guard (why conditional): the tile kernel has no K/V type template - it
-loads half2 - so a non-F16 cache (BF16 / quantized) would be converted by
-launch_fattn to a contiguous F16 copy, which breaks the in-kernel block-table
-read (the table indexes the original paged layout, not the copy). So tile is
-correct only for an F16 cache; non-F16 caches and the non-grouped gqa==1 shape
-fall back to the inc-1 vec path, exactly as before this change. The head-group
-reuse also only helps at gqa_ratio >= 2. LLAMA_KV_PAGED_VEC=1 forces vec for A/B.
-Note: paged decode is currently exercised with an F16 cache only; quantized +
-paged is a separate pre-existing limitation, independent of this change
-(verified: stock + q8_0 cache works, but paged + q8_0 aborts both before and
-after this patch, since both route the non-F16 cache to vec).
-
-Measured GB10 (sm_121, 48 SM), Qwen3-32B NVFP4 dense, F16 cache, gqa 8, batch 32,
-1024 ctx, llama-batched-bench npp=1024 ntg=128 npl=32, GGML_CUDA_DISABLE_GRAPHS=1,
-same build, env-toggled:
-  STOCK (mma)            174.8 ms/step  183.1 t/s
-  PAGED-VEC  (inc-1)     186.3 ms/step  171.8 t/s   (+6.6% vs stock)
-  PAGED-TILE (inc-3)     177.9 ms/step  179.8 t/s   (+1.8% vs stock)
-GQA grouping recovers 8.4 ms/step (-4.5%) over the inc-1 vec default and brings
-paged decode to within 1.8% of stock. The win grows with context (npl=8, tile vs
-vec decode step): 1024 -2.3%, 4096 -3.3%, 8192 and 16384 wider, as attention
-takes a larger share of the step.
-
-Why not the split-K tune: the vec decode grid is already block-saturated
-(1 x parallel_blocks 3 x 2048 = 6144 blocks ~ 43 waves over 144 resident on 48
-SM), so raising parallel_blocks / KV_max adds no SM fill. The under-saturation is
-intra-SM (occupancy + the 8x KV re-streaming), which GQA grouping attacks
-directly; more split-K does not.
-
-Correctness (greedy, GGML_CUDA_DISABLE_GRAPHS=1):
-  - CPU plumbing gate (Qwen3-0.6B, build-cpu, paged-on vs off): BYTE-IDENTICAL.
-  - GPU 0.6B gqa=2, 8 seq x 48 tok: tile is token-identical to the inc-1 vec path
-    in 7/8 sequences; the 8th diverges at token 5, within the same kernel-noise
-    band where vec also drifts from stock. Stock uses the mma kernel for this
-    multi-stream GQA shape, so a different kernel = different rounding =
-    autoregressive token drift; vec and tile agree with each other while both
-    differ from stock (both pick 15678 where stock picks 38835), confirming the
-    drift is kernel choice, not a paging error.
-  - GPU 32B gqa=8, 4 seq x 40 tok: tile tracks stock at least as well as vec
-    (seq3: tile == stock == 624 at the token where vec picked 13).
-
-Stock is byte-identical: the dispatch guard only diverts when the block table
-(src[5]) is set; the non-paged best-kernel switch is untouched. The ncols2>1 tile
-path reads the last nbatch_fa tile with oob_check=false and relies on the mask
--inf padding - the same pattern stock uses for ncols2>1 - and the compacted paged
-mask is gathered to the n_view (GGML_PAD 256) width so it carries that padding.
-
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
-Assisted-by: Claude:opus-4.8 [Claude Code]
----
- ggml/src/ggml-cuda/fattn.cu | 51 ++++++++++++++++++++++++++-----------
- 1 file changed, 36 insertions(+), 15 deletions(-)
-
-diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
-index afcafa2..6b15810 100644
---- a/ggml/src/ggml-cuda/fattn.cu
-+++ b/ggml/src/ggml-cuda/fattn.cu
-@@ -580,32 +580,53 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
-     // silently read the wrong (contiguous physical) cells. So when a block table
-     // is present we route here and NEVER fall through to the best-kernel switch
-     // below - no decode shape can silently reach an mma/wmma misread. build_attn
--    // only sets src[5] for the 1-token-per-stream decode shape; the vec
-+    // only sets src[5] for the 1-token-per-stream decode shape; the vec/tile
-     // dispatcher GGML_ABORTs for an unsupported D/type rather than mis-reading,
-     // and any shape that should not be paged must take the host-side gather path
-     // (LLAMA_KV_PAGED_GATHER=1) instead.
-     //
--    // Default route = vec (inc-1, byte-validated: vec-paged == stock at -s 1 and
--    // CPU byte-identical). LLAMA_KV_PAGED_TILE=1 routes the same shape to the
--    // tile kernel; the tile in-kernel read is plumbed (fattn-tile.cuh) for the
--    // increment-3 GQA head-group reuse, but is EXPERIMENTAL / NOT yet byte-
--    // validated: the GQA-grouped (ncols2>1) tile path reads a full nbatch_fa tile
--    // with oob_check=false while the compacted paged mask is not padded to cover
--    // it, so it diverges from stock. Not for production paged decode until
--    // increment-3 bounds that path; the default vec route is unaffected.
-+    // Default route = the GQA-grouped TILE kernel (inc-3) WHEN it is both correct
-+    // and a win, else the inc-1 vec path. Tile groups the q-heads that share one
-+    // kv-head (ncols2), loading each K/V row once for the whole group instead of
-+    // once per q-head, and runs at higher occupancy than vec (108-128 regs vs 168).
-+    // Two constraints make this conditional: (1) the tile kernel has no K/V type
-+    // template - it loads half2 - so a non-F16 cache (BF16/quantized) would be
-+    // converted by launch_fattn to a contiguous F16 copy, which breaks the
-+    // in-kernel block-table read (the table indexes the original paged layout, not
-+    // the copy); vec instead reads the original cache with in-kernel dequant, so it
-+    // is the only correct paged path for non-F16 caches. (2) the head-group reuse
-+    // only helps when gqa_ratio>=2. So route to tile only for {F16 K and V,
-+    // gqa_ratio>=2}; everything else stays on vec, matching stock (which also sends
-+    // quantized-cache decode to the vector kernel). Measured on GB10 (Qwen3-32B
-+    // nvfp4, F16 cache, gqa 8, batch 32, 1024 ctx): tile 177.9 ms/step vs vec 186.3
-+    // vs stock 174.8 - GQA grouping recovers ~4.5% over the inc-1 vec default and
-+    // brings paged decode to ~1.8% of stock. Validated token-coherent with vec:
-+    // 0.6B 8-seq 7/8 identical (8th within the kernel-noise band where vec also
-+    // drifts from stock), 32B gqa=8 tile tracks stock at least as well as vec, CPU
-+    // plumbing gate byte-identical. The ncols2>1 tile path reads the last nbatch_fa
-+    // tile with oob_check=false relying on mask -inf padding (the SAME pattern stock
-+    // uses for ncols2>1); the compacted paged mask is gathered to the n_view
-+    // (GGML_PAD 256) width so it carries that padding. LLAMA_KV_PAGED_VEC=1 forces
-+    // the inc-1 vec path for A/B.
-     if (dst->src[5] != nullptr) {
--        static const bool paged_tile = getenv("LLAMA_KV_PAGED_TILE") != nullptr;
-+        const ggml_tensor * Qp = dst->src[0];
-+        const ggml_tensor * Kp = dst->src[1];
-+        const ggml_tensor * Vp = dst->src[2];
-+        const bool kv_f16    = Kp->type == GGML_TYPE_F16 && Vp->type == GGML_TYPE_F16;
-+        const int64_t gqa_ratio = Kp->ne[2] > 0 ? Qp->ne[2] / Kp->ne[2] : 1;
-+        const bool force_vec = getenv("LLAMA_KV_PAGED_VEC") != nullptr;
-+        const bool use_tile  = !force_vec && kv_f16 && gqa_ratio >= 2;
-         if (getenv("LLAMA_KV_PAGED_DISPATCH_LOG") != nullptr) {
-             static bool logged = false;
-             if (!logged) {
-                 logged = true;
--                fprintf(stderr, "[paged] decode src[5] set -> routing to %s (Q ne=[%ld,%ld,%ld,%ld])\n",
--                    paged_tile ? "TILE(experimental)" : "VEC",
--                    (long) dst->src[0]->ne[0], (long) dst->src[0]->ne[1],
--                    (long) dst->src[0]->ne[2], (long) dst->src[0]->ne[3]);
-+                fprintf(stderr, "[paged] decode src[5] set -> routing to %s (Q ne=[%ld,%ld,%ld,%ld] gqa=%ld kv_f16=%d)\n",
-+                    use_tile ? "TILE(gqa)" : "VEC",
-+                    (long) Qp->ne[0], (long) Qp->ne[1], (long) Qp->ne[2], (long) Qp->ne[3],
-+                    (long) gqa_ratio, (int) kv_f16);
-             }
-         }
--        if (paged_tile) {
-+        if (use_tile) {
-             ggml_cuda_flash_attn_ext_tile(ctx, dst);
-         } else {
-             ggml_cuda_flash_attn_ext_vec(ctx, dst);
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0012-paged-mask-pad-invariant-assert.patch

@@ -1,50 +0,0 @@
-From 6e3e976e2b11adb05519f31dd5aad0c204678f5c Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Tue, 23 Jun 2026 11:12:05 +0200
-Subject: [PATCH] feat(paged): assert mask-pad invariant for the paged tile
- route (patch 0012)
-
-The now-default paged decode route (GQA-grouped fattn-tile kernel) does not
-leak past-end KV rows only because the compacted mask/block-table length is
-padded to a whole number of flash-attn KV tiles: n_view = GGML_PAD(n_gather,
-256), and the tile (nbatch_fa = 64 for head_dim 128) divides 256, so the last
-tile sits entirely inside the -inf pad window. That invariant was implicit.
-
-Add a defensive GGML_ASSERT(n_view % 64 == 0) right after the pad/clamp so a
-future change to the pad (e.g. < 256) or the tile (> 256) that broke the
-whole-tile property cannot silently reintroduce the leak. Additive only, no
-behaviour change.
-
-Verified: build-cpu compiles, and the paged CPU byte gate (LLAMA_KV_PAGED off
-vs on, Qwen3-0.6B-Q8_0, greedy, -ngl 0) stays byte-identical while the assert
-stays silent (n_view remains a whole number of tiles across all decode steps).
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- src/paged-attn.cpp | 9 +++++++++
- 1 file changed, 9 insertions(+)
-
-diff --git a/src/paged-attn.cpp b/src/paged-attn.cpp
-index 8eebeaa..fed8ca9 100644
---- a/src/paged-attn.cpp
-+++ b/src/paged-attn.cpp
-@@ -201,6 +201,15 @@ bool in_kernel_decode(ggml_context * ctx0,
-         n_view = K->ne[2];
-     }
- 
-+    // The flash-attn KV tile is 64 rows wide (nbatch_fa for head_dim 128). n_view must be
-+    // a whole number of such tiles so the in-kernel decode never reads past the gathered
-+    // rows: the trailing pad cells [n_gather, n_view) are all -inf, so any tile straddling
-+    // the boundary still contributes zero. This holds today only because the pad (256) is a
-+    // multiple of the tile; a future pad < 256 (or nbatch_fa > 256) that broke it would
-+    // silently reintroduce a past-end KV leak, so assert it rather than trust it.
-+    // pad must be a multiple of the flash-attn KV tile so the last tile is fully inside the -inf pad
-+    GGML_ASSERT(n_view % 64 == 0);
-+
-     ggml_tensor * idx = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_view, n_stream);
-     ggml_set_input(idx);
-     res->add_input(llm_graph_input_ptr(new input_block_table(mctx, idx, (uint32_t) n_view)));
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0013-paged-decoupled-prefill-token-budget.patch

@@ -1,137 +0,0 @@
-From 17d97cb74e3e8c93751afd33f5c183e57056fde9 Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Tue, 23 Jun 2026 11:52:45 +0200
-Subject: [PATCH] feat(paged): decoupled per-step prefill-token budget (patch
- 0013)
-
-llama-server already co-batches decode with chunked prefill: update_slots()
-appends every generating slot's sampled token first, then fills the rest of the
-n_batch budget with prompt tokens, deferring the overflow to the next step. But
-the prefill chunk size is hard-wired to n_batch (default 2048): one slot's
-~2048-token prefill chunk lands in a single compute-heavy step, and every decode
-co-batched into that step sees a multi-second inter-token-latency (ITL) spike.
-Lowering n_batch shrinks the chunk but also caps decode-concurrency width and
-prefill throughput, because they are coupled.
-
-Add LLAMA_PREFILL_BUDGET: a per-step prefill-token budget decoupled from n_batch
-(the analogue of vLLM's --max-num-batched-tokens / long_prefill_token_threshold).
-The prompt-fill loop and the outer slot loop now also stop once this many prompt
-tokens have been added in the current update_slots() step, so a long prefill is
-split across more steps that each still advance in-flight decode. Default (env
-unset or <= 0) = disabled, so stock behaviour is byte-identical. Orthogonal to
-LLAMA_KV_PAGED: this is a pure scheduler knob and works with paged off.
-
-Measured on GB10 (sm_121), dense Qwen3-32B-NVFP4, paged build, 8 steady decode
-streams with one 6000-token prefill injected mid-stream; same binary, only
-LLAMA_PREFILL_BUDGET differs:
-
-  metric                        stock(off)  budget=256   budget=512
-  worst decode freeze (ms)         3380      482 (7.0x)   778 (4.3x)
-  median decode ITL in window      2264      411 (5.5x)   689
-  decode_stall (ms)                3285      387 (8.5x)   684 (4.8x)
-  decode steps during prefill        38      201 (5.3x)   108
-  injected-req TTFT (ms)           8493     10172 (+20%)  8432 (~0%)
-  steady-state baseline ITL          94        95          94
-
-This is a LATENCY/fairness lever, not an aggregate-throughput lever: it flattens
-the decode ITL spike a long prefill inflicts on co-batched decoders (8.5x smaller
-worst freeze and 5.3x more decode progress during the prefill at budget=256), in
-exchange for a modest TTFT rise on the long request (the classic chunked-prefill
-trade-off; budget=512 buys 4.8x with ~no TTFT cost). Steady aggregate decode is
-unchanged: it is bandwidth/weight-capped on GB10 (the NVFP4 weight-read floor),
-which the scheduler cannot lift.
-
-Correctness (same model, greedy temp 0, fa on):
-- budget unset or >= n_batch: byte-identical to stock (the added break never
-  fires before the existing n_batch break; the off-path is a no-op by
-  construction).
-- short prompt (<= budget): byte-identical to stock.
-- the knob is exactly equivalent to stock's native -b chunking: budget=512 ==
-  stock -b512 and budget=256 == stock -b256, both BYTE-IDENTICAL, while keeping
-  n_batch=2048 for decode width.
-- on a prompt larger than the budget the chunked greedy output diverges from the
-  single n_batch chunk only by intrinsic flash-attn chunk-size FP grouping: PURE
-  stock -b256 diverges from stock -b2048 the same way with the patch inactive,
-  and the output stays coherent and answers correctly.
-
-Productisation (LocalAI): surface as a model options knob (max_prefill_tokens /
-mpt) parsed in grpc-server.cpp, default 0 = disabled, per CHUNKED_PREFILL_PLAN
-Phase B; the vendored update_slots() hunk here is that plan's scheduler patch and
-stays disjoint from the paged allocation hunks.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- tools/server/server-context.cpp | 35 ++++++++++++++++++++++++++++++++-
- 1 file changed, 34 insertions(+), 1 deletion(-)
-
-diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
-index 04c6361..5d83b30 100644
---- a/tools/server/server-context.cpp
-+++ b/tools/server/server-context.cpp
-@@ -2723,6 +2723,29 @@ private:
-         int32_t n_batch  = llama_n_batch(ctx_tgt);
-         int32_t n_ubatch = llama_n_ubatch(ctx_tgt);
- 
-+        // PAGED serving lever (patch 0013): decoupled per-step prefill-token budget.
-+        // Analogue of vLLM's --max-num-batched-tokens. Stock llama-server caps the prompt
-+        // tokens ingested per update_slots() step at n_batch only; with cont_batching the
-+        // sampled decode tokens of every generating slot are appended FIRST, then prompt
-+        // tokens fill the batch up to n_batch. A long prompt therefore grabs an ~n_batch
-+        // chunk in a SINGLE compute-heavy step, spiking the inter-token latency of every
-+        // co-batched decoder (head-of-line jitter). LLAMA_PREFILL_BUDGET caps the prompt
-+        // tokens added per step independently of n_batch, splitting a long prefill across
-+        // more steps so in-flight decode keeps advancing smoothly. Default (env unset or
-+        // <=0) = disabled => stock behavior is byte-identical. Orthogonal to LLAMA_KV_PAGED
-+        // (this is a pure scheduler knob; works with paged off).
-+        int32_t n_prefill_budget = 0; // 0 = disabled (stock n_batch-only chunking)
-+        {
-+            const char * env_pb = getenv("LLAMA_PREFILL_BUDGET");
-+            if (env_pb) {
-+                const int v = atoi(env_pb);
-+                if (v > 0) {
-+                    n_prefill_budget = std::min(n_batch, std::max(1, v));
-+                }
-+            }
-+        }
-+        int32_t n_prompt_budgeted = 0; // prompt tokens added to the batch this step (across slots)
-+
-         float  alora_scale       = -1.0f;
-         size_t alora_disabled_id = 0;
- 
-@@ -3159,7 +3182,10 @@ private:
-                     const bool n_before_user_known = n_before_user > 0;
- 
-                     // add prompt tokens for processing in the current batch
--                    while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.n_tokens < n_batch) {
-+                    // (patch 0013) also stop once the per-step prefill budget is spent, so a long
-+                    // prompt is split across more steps and leaves batch room for co-batched decode
-+                    while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.n_tokens < n_batch &&
-+                           (n_prefill_budget == 0 || n_prompt_budgeted < n_prefill_budget)) {
-                         // get next token to process
-                         llama_token cur_tok = input_tokens[slot.prompt.n_tokens()];
-                         if (cur_tok == LLAMA_TOKEN_NULL) {
-@@ -3185,6 +3211,7 @@ private:
-                         slot.prompt.tokens.push_back(cur_tok);
- 
-                         slot.n_prompt_tokens_processed++;
-+                        n_prompt_budgeted++; // (patch 0013) count toward the per-step prefill budget
- 
-                         // stop the prompt batch exactly before the latest user input, so a checkpoint
-                         // can be created after the previous messages
-@@ -3293,6 +3320,12 @@ private:
-                 if (batch.n_tokens >= n_batch) {
-                     break;
-                 }
-+
-+                // (patch 0013) stop adding prompts once the per-step prefill budget is spent,
-+                // leaving the remaining batch capacity for co-batched decode of other slots
-+                if (n_prefill_budget > 0 && n_prompt_budgeted >= n_prefill_budget) {
-+                    break;
-+                }
-             }
-         }
- 
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0014-paged-expert-aware-moe-token-tile-cap.patch

@@ -1,140 +0,0 @@
-From 652b858252b354f4d4fb49e5ed7468eeee8e32fc Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Tue, 23 Jun 2026 15:47:06 +0200
-Subject: [PATCH] feat(paged): expert-aware MoE token-tile cap (patch 0014)
-
-On GB10 (sm_121) the Qwen3-30B-A3B-class mxfp4 MoE decode path already uses the
-sorted grouped FP4-MMA GEMM (MUL_MAT_ID -> ggml_cuda_mul_mat_q ids branch:
-mm_ids_helper moe_align/scatter + one persistent stream-k mul_mat_q), so the
-originally reported npl128 throughput cliff does NOT reproduce on this build.
-llama-batched-bench decode (S_TG t/s) is monotonic across batch:
-
-  npl        1     8    32    64   128   256
-  S_TG     85   282   629   935  1295  1779   (stock, mxfp4 MoE, -fa on)
-
-There is no knee to erase; the old cliff (a real high-batch regression, 620 t/s
-at npl128) was fixed upstream by grouped-mmq + MoE stream-k load balancing.
-
-What remains is a pure tile-shape micro-inefficiency. In mul_mat_q_case the
-token-tile width mmq_x is chosen to cover ncols_max (= ne12, the per-expert
-column upper bound = token count, up to 128) in one column-tile. At MoE decode
-the per-expert token density is ~ne12*k/n_experts (top-8 of 128 => ~1/16 of
-ne12, e.g. ~8 tokens/expert at npl128), so each expert's single mmq_x-wide
-col-tile is only ~6% filled: the MMA accumulator tile is mmq_x-wide at compile
-time and burns throughput on the padding columns while the larger y-tile lowers
-occupancy. Stock picks the LARGEST tile (128) where the SMALLEST tile that still
-covers the density would raise fill + occupancy at no extra weight read (at
-tokens/expert <= mmq_x there is exactly one non-empty col-tile per expert; the
-emptier tiles are skipped by the jt*mmq_x >= col_diff guard in the stream-k
-kernel) - the inverse of vLLM's small per-expert BLOCK_SIZE_M.
-
-Add LLAMA_MOE_MMQ_X: an env cap on mmq_x for the MUL_MAT_ID path only
-(expert_bounds != nullptr). Default (unset or <= 0) = disabled, so the mmq_x
-selection, and therefore every kernel launched, is byte-identical to stock. The
-cap only ever lowers the loop's upper bound and still selects from the same
-granularity- and shared-memory-validated mmq_x set stock already uses for
-smaller batches, so no new kernel configuration is exercised.
-
-Measured on GB10, qwen3coder-mxfp4.gguf, -fa on, -npp 128 -ntg 128, same binary,
-only LLAMA_MOE_MMQ_X differs (decode S_TG t/s / prefill S_PP t/s):
-
-  npl     stock S_TG   cap64 S_TG    d%     stock S_PP   cap64 S_PP
-   64        936          938      +0.1       2924         2883
-  128       1295         1357      +4.8       3075         3038
-  256       1784         1825      +2.3       3085         3046
-
-  (reproduced across interleaved reps; cap64 npl128 = 1357.5/1357.0, very stable)
-
-cap64 lifts high-batch decode +4.8% (npl128) / +2.3% (npl256), neutral at
-npl <= 64, for a consistent ~1.3% prefill cost. Smaller caps are net-negative:
-cap16 / cap32 crater prefill -41% / -17% (a 512-token prefill ubatch has ~32
-tokens/expert, which overflows a 16/32-wide tile into extra col-tiles + weight
-re-reads), so 64 is the recommended value and the only one that helps net.
-
-Honest framing: this is NOT a cliff fix (no cliff exists) and not a real-server
-throughput unlock (llama-server continuous batching already scales). It is a
-modest high-effective-batch DECODE micro-optimization that matches vLLM's
-smaller per-expert M-tiling, surfaced as an opt-in, default-off knob. The
-durable density-aware auto-select (drop the blunt global cap, choose mmq_x from
-ne_get_rows / n_active_experts so prefill keeps its large tile) is scoped in
-patches/paged/MOE_GROUPED_GEMM_SCOPE.md.
-
-Correctness: greedy temp-0 llama-server output with cap64 is byte-identical to
-stock for single-stream generation (fibonacci / capital-of-France / photosynthesis
-prompts) and stays coherent; batched-bench ran thousands of capped MoE matmuls at
-npl128/256 (mmq_x forced 128 -> 64) with no CUDA error / NaN and stable output.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- ggml/src/ggml-cuda/mmq.cuh | 37 ++++++++++++++++++++++++++++++++++++-
- 1 file changed, 36 insertions(+), 1 deletion(-)
-
-diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
-index edf546d..cff608e 100644
---- a/ggml/src/ggml-cuda/mmq.cuh
-+++ b/ggml/src/ggml-cuda/mmq.cuh
-@@ -6,6 +6,7 @@
- 
- #include <climits>
- #include <cstdint>
-+#include <cstdlib>
- 
- using namespace ggml_cuda_mma;
- 
-@@ -4052,6 +4053,18 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
-     }
- }
- 
-+// [paged patch 0014] MoE token-tile (mmq_x) cap, read once from env LLAMA_MOE_MMQ_X.
-+// Returns 0 when unset / non-positive => disabled (stock mmq_x selection, byte-identical).
-+// On the MUL_MAT_ID grouped-GEMM path this caps the per-expert column-tile width toward the
-+// low MoE-decode per-expert token density, raising tile fill + occupancy (see mul_mat_q_case).
-+static inline int ggml_cuda_moe_mmq_x_cap() {
-+    static const int cap = []() -> int {
-+        const char * s = getenv("LLAMA_MOE_MMQ_X");
-+        return s ? atoi(s) : 0;
-+    }();
-+    return cap;
-+}
-+
- template <ggml_type type>
- void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
-     const int    id     = ggml_cuda_get_device();
-@@ -4063,10 +4076,32 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
-     const int mmq_x_max = get_mmq_x_max_host(cc);
-     const int mmq_y = get_mmq_y_host(cc);
- 
-+    // [paged patch 0014] expert-aware MoE token-tile (mmq_x) cap.
-+    // On the MUL_MAT_ID grouped-GEMM path (expert_bounds != nullptr) the GEMM columns are
-+    // tokens sorted by expert; stock picks mmq_x to cover ncols_max (= ne12, the token count,
-+    // up to 128) in a single column-tile. At MoE decode the per-expert token density is low
-+    // (top-k of many experts: ~ne12*k/n_experts tokens/expert, e.g. ~8 at npl128 for
-+    // Qwen3-30B-A3B top-8/128), so each expert's single mmq_x-wide col-tile is mostly empty:
-+    // the MMA accumulator tile is mmq_x-wide at compile time and wastes throughput on the
-+    // padding columns while the larger y-tile lowers occupancy. Capping mmq_x toward the
-+    // per-expert density raises tile fill + occupancy with no extra weight reads (at
-+    // tokens/expert <= mmq_x there is still exactly one non-empty col-tile per expert; the
-+    // emptier tiles are skipped by the jt*mmq_x >= col_diff guard in the stream-k kernel).
-+    // Default (env unset or <= 0) = disabled => mmq_x selection is byte-identical to stock;
-+    // off the ids path the cap never applies.
-+    int mmq_x_lim = mmq_x_max;
-+    if (args.expert_bounds != nullptr) {
-+        const int moe_cap = ggml_cuda_moe_mmq_x_cap();
-+        if (moe_cap > 0) {
-+            const int cap = moe_cap < 8 ? 8 : moe_cap;
-+            mmq_x_lim = cap < mmq_x_max ? cap : mmq_x_max;
-+        }
-+    }
-+
-     int mmq_x_best  = 0;
-     int ntiles_x_best = INT_MAX;
- 
--    for (int mmq_x = 8; mmq_x <= mmq_x_max && ntiles_x_best > 1; mmq_x += 8) {
-+    for (int mmq_x = 8; mmq_x <= mmq_x_lim && ntiles_x_best > 1; mmq_x += 8) {
-         const int granularity = mmq_get_granularity_host(mmq_x, cc);
- 
-         if (mmq_x % granularity != 0 || mmq_get_nbytes_shared<type>(mmq_x, mmq_y, cc, warp_size, nwarps) > smpbo) {
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/0015-paged-expert-density-aware-moe-token-tile-auto-select.patch

@@ -1,238 +0,0 @@
-From 151343bc8c7b956c99eafc855704b70d44637a3b Mon Sep 17 00:00:00 2001
-From: Ettore Di Giacinto <mudler@localai.io>
-Date: Tue, 23 Jun 2026 21:03:00 +0200
-Subject: [PATCH] feat(paged): expert-density-aware MoE token-tile auto-select
- (patch 0015)
-
-The durable follow-up to patch 0014's blunt LLAMA_MOE_MMQ_X global cap (which the
-0014 doc itself scoped): replace the manual env cap with a host-side, default-on
-auto-select inside mul_mat_q_case that picks a small token-tile (mmq_x) for the
-MUL_MAT_ID grouped FP4-MMA GEMM only when the per-expert token density is low
-(decode), and keeps the large 128-wide tile when density is high (prefill). No new
-kernel: the selection only lowers the loop's upper bound to an already-compiled,
-granularity- and shared-memory-validated mmq_x.
-
-Density is estimated host-side from the args the ids path already passes:
-  ne_get_rows = ncols_dst   = ne12 * n_expert_used   (token-expert assignments)
-  n_experts   = nchannels_x = ne02
-  density     = ceil(ne_get_rows / min(ne_get_rows, n_experts))   (tokens/expert)
-Cap to the small tile (default 64) only when density <= density_max. Unlike 0014's
-global cap, the high-density prefill ubatch stays on the big tile, so S_PP does not
-regress by construction.
-
-density_max default = 8 (not tile/4 = 16). The cap must fire for decode but not for
-a prefill ubatch, and each has per-expert density n_tokens*n_used/n_experts. At the
-standard n_ubatch=512, n_used=8: prefill density = 4096/n_experts (32 at 128 experts,
-16 at 256), decode at npl<=128 is <= 1024/n_experts (8 at 128, 4 at 256). Default 8
-sits strictly between for every n_experts in [128,511], so it caps decode and leaves
-prefill on the big tile. tile/4 (=16) equalled the 256-expert prefill density and
-cratered its S_PP by ~2%, the regression this threshold exists to avoid.
-
-Measured on GB10 (sm_121), Qwen3.6-35B-A3B NVFP4 (256 experts, top-8, GDN linear
-attention), llama-batched-bench -fa on -npp 128 -ntg 128, default-on vs stock
-(LLAMA_MOE_AUTO_TILE=0), median of 5 reps:
-
-  npl   S_TG stock  S_TG 0015   dTG%    S_PP stock  S_PP 0015   dPP%
-    8      183.59     183.18  -0.22%       1489.2     1500.1  +0.73%
-   32      264.02     263.44  -0.22%       2034.5     2033.5  -0.05%
-   64      311.76     310.41  -0.43%       2028.3     2027.6  -0.03%
-  128      336.10     337.32  +0.36%       2025.0     2027.7  +0.13%
-
-Honest read: on THIS model the decode effect is within run-to-run noise (neutral)
-and prefill is neutral. q36-35b-a3b decode is bound by the GDN/SSM recurrence and
-256 tiny-expert weight bandwidth, not the MoE col-tile occupancy, so the col-tile
-lever (worth +4.8% @npl128 on Qwen3-Coder-30B, 128 larger experts, patch 0014
-cap64) does not move it. A npl128 tile sweep on this model confirms 64 is the only
-useful width (TILE8 -6.3%, TILE16 -3.2%, TILE32 -0.2%, TILE64 +0.7%, TILE96 -0.8%):
-smaller tiles lose to grid/scheduling overhead and the FP4-MMA minimum width.
-
-Value banked default-on: (1) removes 0014's ~1.3% prefill cost by construction
-(density-gated, not global); (2) auto-selects the small tile for col-tile-bound MoE
-decode, reproducing 0014 cap64's tile=64 at npl128 by construction, so it preserves
-the +4.8% on Qwen3-Coder-30B without the prefill cost; (3) prefill-safe and decode-
-neutral on the SSM model, harmless where it does not help. Conservative by design:
-at npl256 the qwen3coder decode density (16) equals the 256-expert prefill density
-(16), indistinguishable to a pure-density gate, so density_max=8 forgoes 0014's
-+2.3% @npl256 to keep 256-expert prefill safe; an ne12-aware refinement is future
-work.
-
-LLAMA_MOE_MMQ_X (patch 0014) is KEPT as a manual override that, when > 0, forces the
-old blunt global cap and bypasses the auto-select (explicit A/B knob). The auto-
-select is the default; LLAMA_MOE_AUTO_TILE=0 restores exact stock mmq_x selection.
-LLAMA_MOE_DECODE_TILE / LLAMA_MOE_DENSITY_MAX tune the small tile / threshold.
-
-Correctness: extends tests/test-backend-ops test_mul_mat_id with a ragged small-M
-NVFP4/MXFP4 MoE decode-density gate (128 experts, top-8, m=768, k=2048, n in
-{16,33,64,128,130,200,256,512} spanning the cap boundary and ragged token counts).
-All 16 shapes pass CUDA-vs-CPU oracle on GB10 both default-on and with
-LLAMA_MOE_AUTO_TILE=0; full MUL_MAT_ID suite 2/2 backends OK. Off the ids path
-nothing changes (non-MoE mul_mat byte-identical to stock).
-
-Assisted-by: Claude:opus-4.8 [Claude Code]
-Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
----
- ggml/src/ggml-cuda/mmq.cuh | 100 ++++++++++++++++++++++++++++++-------
- tests/test-backend-ops.cpp |  16 ++++++
- 2 files changed, 99 insertions(+), 17 deletions(-)
-
-diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
-index cff608e..9718b12 100644
---- a/ggml/src/ggml-cuda/mmq.cuh
-+++ b/ggml/src/ggml-cuda/mmq.cuh
-@@ -4053,10 +4053,11 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
-     }
- }
- 
--// [paged patch 0014] MoE token-tile (mmq_x) cap, read once from env LLAMA_MOE_MMQ_X.
--// Returns 0 when unset / non-positive => disabled (stock mmq_x selection, byte-identical).
--// On the MUL_MAT_ID grouped-GEMM path this caps the per-expert column-tile width toward the
--// low MoE-decode per-expert token density, raising tile fill + occupancy (see mul_mat_q_case).
-+// [paged patch 0014] MoE token-tile (mmq_x) MANUAL cap, read once from env LLAMA_MOE_MMQ_X.
-+// Returns 0 when unset / non-positive => disabled (fall through to the patch-0015 auto-select).
-+// When > 0 it forces a blunt GLOBAL cap on the per-expert column-tile width for the MUL_MAT_ID
-+// grouped-GEMM path (decode AND prefill), overriding the density-aware auto-select below. Kept
-+// as an explicit override / A-B knob; the default path is now the auto-select.
- static inline int ggml_cuda_moe_mmq_x_cap() {
-     static const int cap = []() -> int {
-         const char * s = getenv("LLAMA_MOE_MMQ_X");
-@@ -4065,6 +4066,43 @@ static inline int ggml_cuda_moe_mmq_x_cap() {
-     return cap;
- }
- 
-+// [paged patch 0015] expert-density-aware MoE token-tile (mmq_x) auto-select knobs (DEFAULT-ON).
-+// LLAMA_MOE_AUTO_TILE=0 disables the auto-select => exact stock mmq_x selection.
-+static inline bool ggml_cuda_moe_auto_tile_enabled() {
-+    static const bool en = []() -> bool {
-+        const char * s = getenv("LLAMA_MOE_AUTO_TILE");
-+        return !(s && atoi(s) == 0);
-+    }();
-+    return en;
-+}
-+// The small high-occupancy token-tile chosen for low-density (decode) MoE matmuls. Default 64:
-+// the measured GB10 sweet spot (full per-expert fill with >=4x routing-imbalance headroom).
-+static inline int ggml_cuda_moe_decode_tile() {
-+    static const int t = []() -> int {
-+        const char * s = getenv("LLAMA_MOE_DECODE_TILE");
-+        const int v = s ? atoi(s) : 0;
-+        return v >= 8 ? v : 64;
-+    }();
-+    return t;
-+}
-+// Per-expert token-density ceiling under which the small tile is selected. Default 8: the cap must
-+// fire for decode but NOT for a prefill ubatch, and the per-expert density of each is
-+// n_tokens*n_used/n_experts. For the standard n_ubatch=512, n_used=8 the prefill density is
-+// 4096/n_experts (= 32 at 128 experts, 16 at 256 experts); decode at npl<=128 is <=1024/n_experts
-+// (= 8 at 128 experts, 4 at 256). Default 8 sits strictly between the two for every n_experts in
-+// [128,511], so it caps decode and leaves the prefill ubatch on the big 128 tile - whereas the old
-+// tile/4 (=16) equalled the 256-expert prefill density and cratered its S_PP by ~2% (measured on
-+// Qwen3.6-35B-A3B NVFP4). 8 also keeps >=8x fill headroom at tile 64 so an imbalanced expert
-+// segment never splits into an extra col-tile.
-+static inline int ggml_cuda_moe_density_max() {
-+    static const int d = []() -> int {
-+        const char * s = getenv("LLAMA_MOE_DENSITY_MAX");
-+        const int v = s ? atoi(s) : 0;
-+        return v > 0 ? v : 8;
-+    }();
-+    return d;
-+}
-+
- template <ggml_type type>
- void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
-     const int    id     = ggml_cuda_get_device();
-@@ -4076,25 +4114,53 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
-     const int mmq_x_max = get_mmq_x_max_host(cc);
-     const int mmq_y = get_mmq_y_host(cc);
- 
--    // [paged patch 0014] expert-aware MoE token-tile (mmq_x) cap.
--    // On the MUL_MAT_ID grouped-GEMM path (expert_bounds != nullptr) the GEMM columns are
--    // tokens sorted by expert; stock picks mmq_x to cover ncols_max (= ne12, the token count,
--    // up to 128) in a single column-tile. At MoE decode the per-expert token density is low
--    // (top-k of many experts: ~ne12*k/n_experts tokens/expert, e.g. ~8 at npl128 for
--    // Qwen3-30B-A3B top-8/128), so each expert's single mmq_x-wide col-tile is mostly empty:
--    // the MMA accumulator tile is mmq_x-wide at compile time and wastes throughput on the
--    // padding columns while the larger y-tile lowers occupancy. Capping mmq_x toward the
--    // per-expert density raises tile fill + occupancy with no extra weight reads (at
--    // tokens/expert <= mmq_x there is still exactly one non-empty col-tile per expert; the
--    // emptier tiles are skipped by the jt*mmq_x >= col_diff guard in the stream-k kernel).
--    // Default (env unset or <= 0) = disabled => mmq_x selection is byte-identical to stock;
--    // off the ids path the cap never applies.
-+    // [paged patch 0015] expert-density-aware MoE token-tile (mmq_x) auto-select (DEFAULT-ON).
-+    // On the MUL_MAT_ID grouped-GEMM path (expert_bounds != nullptr) the GEMM columns are tokens
-+    // sorted by expert; stock picks mmq_x to cover ncols_max (= ne12, the token count, up to 128)
-+    // in a single column-tile, i.e. it MAXIMIZES the tile (128 on Blackwell) for the aggregate
-+    // batch. But the tile is then applied PER EXPERT, and at MoE decode the per-expert token
-+    // density is tiny (top-k of many experts), so each expert's single 128-wide col-tile is mostly
-+    // empty: the MMA accumulator tile is mmq_x-wide at compile time and burns throughput on the
-+    // padding columns while the larger y-tile lowers occupancy. vLLM's fused-MoE does the opposite
-+    // (a small per-expert BLOCK_SIZE_M). We reproduce that here, host-side only, by picking a
-+    // SMALLER mmq_x when - and only when - the per-expert density is low:
-+    //
-+    //   ne_get_rows  = args.ncols_dst    = ne12 * n_expert_used  (total token-expert assignments)
-+    //   n_experts    = args.nchannels_x  = ne02
-+    //   n_active_est = min(n_experts, ne_get_rows)               (upper bound on active experts)
-+    //   density      = ceil(ne_get_rows / n_active_est)          (avg tokens per active expert)
-+    //
-+    // Cap to the small tile (default 64) only when density <= density_max (default 8). 8 sits below
-+    // every prefill-ubatch density and above every decode density for n_experts in [128,511] at the
-+    // standard n_ubatch=512 (prefill 4096/n_experts, decode <=1024/n_experts), with >=8x fill headroom
-+    // so a capped expert segment never splits a col-tile. Decode (per-expert density 4 at 256 experts,
-+    // 8 at 128 experts @npl128) gets the fuller high-occupancy tile; the prefill ubatch (density 16 at
-+    // 256 / 32 at 128 experts) stays ABOVE the threshold and keeps the big
-+    // 128 compute tile - so unlike the blunt global cap (LLAMA_MOE_MMQ_X / patch 0014) this is
-+    // prefill-safe by construction. The selection only ever picks an already-compiled, granularity-
-+    // and shared-memory-validated mmq_x that the loop below would consider for a smaller batch; no
-+    // new kernel. Off the ids path (expert_bounds == nullptr) nothing changes => non-MoE mul_mat
-+    // and the gated f16/bf16 host-loop fallback stay byte-identical to stock.
-+    //   - LLAMA_MOE_MMQ_X=<n>   : manual blunt global cap, overrides the auto-select (patch 0014).
-+    //   - LLAMA_MOE_AUTO_TILE=0 : disable the auto-select (exact stock selection).
-+    //   - LLAMA_MOE_DECODE_TILE=<n>, LLAMA_MOE_DENSITY_MAX=<n> : tune the tile / threshold.
-     int mmq_x_lim = mmq_x_max;
-     if (args.expert_bounds != nullptr) {
-         const int moe_cap = ggml_cuda_moe_mmq_x_cap();
-         if (moe_cap > 0) {
-             const int cap = moe_cap < 8 ? 8 : moe_cap;
-             mmq_x_lim = cap < mmq_x_max ? cap : mmq_x_max;
-+        } else if (ggml_cuda_moe_auto_tile_enabled()) {
-+            const int64_t ne_get_rows = args.ncols_dst;
-+            const int64_t n_experts   = args.nchannels_x;
-+            if (ne_get_rows > 0 && n_experts > 0) {
-+                const int64_t n_active = ne_get_rows < n_experts ? ne_get_rows : n_experts;
-+                const int64_t density  = (ne_get_rows + n_active - 1) / n_active;
-+                const int     tile     = ggml_cuda_moe_decode_tile();
-+                if (density <= (int64_t) ggml_cuda_moe_density_max() && tile < mmq_x_max) {
-+                    mmq_x_lim = tile;
-+                }
-+            }
-         }
-     }
- 
-diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
-index 15ae389..f219309 100644
---- a/tests/test-backend-ops.cpp
-+++ b/tests/test-backend-ops.cpp
-@@ -8575,6 +8575,22 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
-     // gpt-oss issue with Vulkan mmq_id
-     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_MXFP4, GGML_TYPE_F32, 32, 2, false, 2880, 32, 2880));
- 
-+    // [paged P0] MXFP4/NVFP4 qwen3-30b-a3b MoE decode-density regression gate for the expert-
-+    // density-aware mmq_x auto-select (patch 0015). Real expert-FFN slice (128 experts, top-8,
-+    // m=768, k=2048) so this exercises the exact grouped FP4-MMA mmq kernel the model runs.
-+    // Per-expert token density = n*n_used/n_mats = n/16; cover the decode band (density 1/4/8/16
-+    // at n 16/64/128/256), ragged token counts (n 33/130/200: experts with 0/1/2 tokens, n not a
-+    // multiple of the tile) where the tiny-M col-tiles change geometry and any masking can leak,
-+    // and a prefill-density shape (n 512 => density 32) the auto-select must leave on the large
-+    // 128 tile. n>=128 is exactly where stock picks mmq_x=128 and the auto-select picks 64, so the
-+    // op-test (CPU oracle vs CUDA, deterministic) is the bit-exact regression gate for P1: it must
-+    // pass with the auto-select on (default) and with LLAMA_MOE_AUTO_TILE=0 (stock selection).
-+    for (ggml_type type_a : {GGML_TYPE_MXFP4, GGML_TYPE_NVFP4}) {
-+        for (int n : {16, 33, 64, 128, 130, 200, 256, 512}) {
-+            test_cases.emplace_back(new test_mul_mat_id(type_a, GGML_TYPE_F32, 128, 8, false, 768, n, 2048));
-+        }
-+    }
-+
-     for (ggml_type type_a : all_types) {
-         test_cases.emplace_back(new test_mul_mat_id(type_a, GGML_TYPE_F32, 4, 2, false, 64, 16, 3*ggml_blck_size(type_a)));
-     }
--- 
-2.43.0
-

backend/cpp/llama-cpp/patches/paged/ADDITIVE_DESIGN.md

@@ -1,107 +0,0 @@
-# Additive layout for the paged-KV patch series - "hook, don't edit"
-
-Goal: ship paged KV as a vendored patch series that **survives llama.cpp pin bumps with
-minimal rebase pain**. PR #22569 (the upstream draft) was rejected by maintainers as
-"slop" and is far too invasive to vendor - it rewrites core attention. Our series must be
-the opposite: **additive**. This document is the design rule and the per-patch core-touch
-budget.
-
-## The rule
-
-> Every change is either (a) **new code in a new vendored file** under `src/`, or (b) a
-> **single, env-gated hook** at one call site in a core file that delegates to the new
-> file. No logic lives in a core file. No core struct/signature is edited.
-
-Why it works: a hook is a 1-3 line diff against a core file. When upstream churns that file,
-`git apply` either still lands the hook (context unchanged) or fails *only on that tiny
-hunk*, which is trivial to re-place. Logic embedded inside a core function (the PR #22569 /
-old-0003 approach) conflicts on every bump and must be re-understood each time.
-
-This is enforceable as a **core-touch budget**: each patch declares the core files it
-touches and the line count; review rejects anything that grows logic in core.
-
-## Why it's achievable here (grounded in the pinned source)
-
-The two seams paged KV needs are both already abstract in llama.cpp at the pin
-(`LLAMA_VERSION=f3e1828`), so new behavior plugs in without editing core types:
-
-- **KV placement** - `llama_kv_cache::find_slot` already returns a `slot_info` of physical
-  cell indices. Paged placement is just *different indices*. 0002 already does this as one
-  gated block (`if (paged_mode) { ... continue; }`, 41 lines, one file). Ideal.
-- **Graph inputs** - `llm_graph_input_i` is a pure-virtual base (`set_input()`), and
-  `llm_graph_result::add_input(llm_graph_input_ptr)` lets *any* code register a new input
-  subclass. So a paged graph input (the gather index) can be **a new class in a new file**,
-  added from a one-line hook - no edit to `llm_graph_input_attn_kv` or `llama-graph.h`.
-
-## Per-patch core-touch budget
-
-| # | Patch | New files (additive) | Core hooks (gated, minimal) | Core lines |
-|---|-------|----------------------|------------------------------|-----------:|
-| 0001 | vendor manager | `paged-kv-manager.{h,cpp}` | `CMakeLists.txt` +1 | 1 |
-| 0002 | block placement | - | one `if(paged_mode){...continue;}` in `find_slot` | ~41 |
-| 0003 | gather-read | `paged-attn.{h,cpp}` | `CMakeLists.txt` +1; **one** hook in `build_attn`; 2 tiny accessors on `llama_kv_cache_context` | ~8 |
-| 0004 | on-demand alloc | (uses 0001 manager) | one branch in `find_slot` calling the manager | ~10 |
-| 0005 | continuous batching | - | **LocalAI `grpc-server.cpp`** (already a LocalAI override, not a core patch) | 0 core |
-| 0006 | prefix caching | (uses 0001 manager) | one hash-lookup hook in the 0004 alloc branch | ~6 |
-
-Net core surface for the *entire* engine: `find_slot` (placement/alloc - where physical
-cells are already chosen) + **one** line in `build_attn` + two accessors. Everything else
-is new files or the LocalAI-side server loop.
-
-## 0003 redesigned to the rule (replaces the 4-file-surgery plan)
-
-The old `0003-gather-read-plan.md` edited `llama-kv-cache.{h,cpp}` + `llama-graph.{h,cpp}`
-(including a field added to `llm_graph_input_attn_kv` and fill logic in its `set_input`).
-The additive form removes the core-struct and core-`set_input` edits entirely:
-
-**New file `src/paged-attn.{h,cpp}`** holds *all* logic:
-- `class llm_graph_input_paged_gather : public llm_graph_input_i` - owns the `I32 [n_gather]`
-  gather-index tensor and a `const llama_kv_cache_context * mctx`. Its `set_input()` fills
-  the index with the sequence's used cells (`{ i in [0,n_kv) : !cells.is_empty(i) }`, the
-  same set the `kq_mask` keeps), in the canonical order.
-- `paged_attn::gather(ctx0, res, mctx, v_trans, &k, &v, &kq_mask)` - when paged is active,
-  constructs that input via `res->add_input(...)`, and applies `ggml_get_rows` to `k`, `v`,
-  and the transposed `kq_mask` by the shared index (mask: `transpose -> get_rows ->
-  transpose`). When not active it returns immediately -> **stock path byte-identical**.
-
-**Core hooks (the whole core diff for 0003):**
-1. `src/llama-graph.cpp`, in `build_attn` right before `build_attn_mha` (~line 2357):
-   ```cpp
-   paged_attn::gather(ctx0, res, mctx_cur, v_trans, &k, &v, &kq_mask); // no-op unless LLAMA_KV_PAGED
-   ```
-   One line. No new field on `llm_graph_input_attn_kv`; the gather input is a *separate*
-   registered input, so `llama-graph.h` is untouched.
-2. `src/llama-kv-cache.{h,cpp}`: two thin accessors on `llama_kv_cache_context` so the new
-   file can read the used-cell set without reaching into internals -
-   `uint32_t get_n_gather() const;` and `void get_gather_idxs(int32_t * dst) const;`
-   (delegate to `kv`/`sinfos[i_cur]`, mirroring the existing `get_n_kv` / `set_input_k_idxs`
-   pattern). ~8 lines total, no signature changes to existing methods.
-3. `src/CMakeLists.txt`: `+ paged-attn.cpp`.
-
-First cut: gate to **flash-attn + single-stream** (`GGML_ASSERT` otherwise) - the V-transposed
-(non-FA) and multi-stream gathers are a localized follow-up entirely inside `paged-attn.cpp`,
-no new core touch. Gate 0 stays the same: `diff` of greedy `llama-simple` output, stock vs
-`LLAMA_KV_PAGED=1`, must be identical (attention is permutation-invariant over the gathered
-KV set; `n_gather < n_kv` proves compaction, not identity).
-
-## Anti-drift practices (already in `README.md`, restated as policy)
-
-- **Stacking patches, one concern each**, exported 1:1 from a dev branch via
-  `git format-patch`. On a pin bump, rebase the branch; only the conflicting small patch
-  needs a touch, and the failure names the exact step.
-- **Default-off (`LLAMA_KV_PAGED`)** until each gate is green, so a partial series never
-  changes stock behavior - and the hooks compile to a no-op branch when the env is unset.
-- **Dev tree:** `git worktree add <dev> <LLAMA_VERSION>` off any checkout that has the pin
-  (e.g. the existing llama.cpp clone), `git apply` the series, develop the next patch as one
-  commit, re-export. (Set up and verified for this pin during this work.)
-
-## Status / next step
-
-- 0001, 0002: done, additive, verified token-identical.
-- 0003: **redesigned to the additive form above** (this doc). Dev tree at the pin with
-  0001+0002 applied is ready (`paged` branch). Remaining work is the focused
-  implement-and-verify block for `paged-attn.{h,cpp}` + the one `build_attn` hook, driven to
-  the token-identical Gate 0. That is a numerical-correctness task (mask/gather alignment,
-  FA-first), not a structural one - the structure is settled here.
-- 0004-0006: follow the budget above; 0005 lands in LocalAI's `grpc-server.cpp` (no core
-  patch at all).

backend/cpp/llama-cpp/patches/paged/DECODE_GAP_STUDY.md

@@ -1,185 +0,0 @@
-# llama-server vs vLLM: decode-step gap decomposition (DGX Spark, GB10 / sm_121)
-
-Profiling study (no engine changes). Question: matched apples-to-apples (both
-batched servers, NVFP4-class weights, prefix caching on, both eager), why is
-`llama-server` ~4-6x slower **per decode step** than vLLM on Qwen3-32B at a
-1024-token shared-prefix / batch-32 fan-out, and what is closable vs structural.
-
-Hardware: NVIDIA GB10 (sm_121), unified LPDDR5X. Model: Qwen3-32B, 64 layers.
-llama side: `~/llama-paged-dev/build-cuda/bin/llama-server`, `q3-32b-nvfp4-dense.gguf`
-(NVFP4 weights, type-40 FP4-MMA path), `-ngl 99 --parallel 32 -c 40960 -fa on`,
-`GGML_CUDA_DISABLE_GRAPHS=1` (eager). vLLM 0.23.0 NVFP4A16 (W4A16/Marlin),
-`--enforce-eager`. Workload: 1024-token shared prefix + unique 32-token suffix,
-K=32 concurrent, generate 64. All profiling scripts are dev-tree only
-(`~/bench/decode_study/`); minimal in-code timers were not needed (server already
-reports per-slot `eval time`, which excludes prompt-eval = pure decode).
-
-## TL;DR
-
-1. **The real-server decode is GPU-BOUND, not host-bound.** During steady decode
-   the GPU is **~94.6% utilized** (nvidia-smi, real run) / 85-95% busy (nsys).
-   Per-slot CPU sampling, detokenize, and `update_slots` are fully hidden: a 5-stage
-   sampler chain gives the *identical* step time as greedy (1346 vs 1343 ms). The
-   "GPU stalls on the CPU serving loop" hypothesis is **refuted** for this workload.
-2. **At 1024 context the decode step is ~84% KV/attention, ~16% weight GEMM** - the
-   opposite of the thin-batch-GEMM story. Attention scaling with context length, not
-   the matmul, is the load-bearing cost.
-3. **The worktree's paged KV engine is a decode REGRESSION: ~1.85x slower than
-   stock** at 1024 ctx (paged 1279-1343 ms/step vs stock 650-729 ms/step). It
-   gathers K/V/mask into a contiguous buffer (`ggml_get_rows`) every layer every
-   step, then runs a dense FA kernel - paying a full extra KV read+copy that vLLM's
-   in-kernel PagedAttention never pays. Paging helps prefix-prefill memory; it hurts
-   decode latency.
-4. Even **stock** llama-server (~650-729 ms/step) is **~4-5x slower than vLLM**
-   (~120-185 ms/step). The residual gap is the **long-context decode-attention
-   kernel** and, secondarily, the **thin-batch FP4 weight GEMM** - both kernel-maturity
-   gaps vs vLLM's FlashInfer/FA paged-decode + Marlin, not serving-loop gaps.
-
-## The measured numbers (batch 32, server-reported pure-decode step time)
-
-`server_decode_step_ms` = max / mean-of-top-8 of per-slot `eval time ms-per-token`
-(the most-contended, full-batch-32 slots; excludes prompt eval).
-
-| config                                   | decode step ms (max / top8) | client wall ms/step |
-|------------------------------------------|-----------------------------|---------------------|
-| paged, ctx 1024, greedy                  | 1343 / 1279                 | 1468                |
-| paged, ctx 1024, **heavy 5-sampler**     | 1346 / 1280                 | 1470                |
-| **stock** (no paging), ctx 1024, greedy  | **729 / 650**               | 768                 |
-| paged, **ctx 64** (short), greedy        | **215 / 215**               | 253                 |
-| vLLM NVFP4A16, ctx 1024 (K=32)           | **~120-185** (270 tok/s)    | -                   |
-
-The brief's reference ~828 ms/step sits between the stock (650-729) and paged
-(1279-1343) numbers measured here; the decomposition below is what is robust. Our
-fan-out shares no prefix across the 32 slots (each slot independently prefills 1056
-tokens - confirmed in the log), so the 32 sequences are genuinely concurrent and the
-"max" slot is maximally contended, which is why our paged max runs a little above 828.
-
-### Context sweep - decode step is attention-scaling, not fixed overhead
-
-Pure-decode step vs shared-prefix length (paged, batch 32):
-
-| prefix ctx | decode step ms |
-|-----------|----------------|
-| 64        | 215            |
-| 128       | ~290           |
-| 256       | ~410           |
-| 512       | ~660           |
-| 1024      | ~1280          |
-
-Roughly linear in context length: ~1 ms of added step time per added context token.
-The **215 ms at ctx 64 is the fixed floor** (weight GEMM + activations + norm/rope +
-loop + sampling, attention negligible). Everything above it scales with KV length =
-attention + KV plumbing. At 1024 ctx the fixed floor is only ~16% of the step.
-
-## Where the ~1280 ms paged decode step goes (nsys, pure-decode window)
-
-`nsys profile --delay=70 --duration=25 --trace=cuda` windowed onto steady 32-way
-decode (`srv_decode2.nsys-rep`; an earlier 25-60s window was discarded because nsys's
-own slowdown stretched the 32 prefills into it, inflating GEMM to a misleading 58%).
-GPU busy in-window 85.5% (nsys adds gaps; the real run is ~94.6% by nvidia-smi).
-
-| bucket                         | % GPU time | abs (of ~1280 ms) | what it is |
-|--------------------------------|-----------:|------------------:|------------|
-| `flash_attn_ext_f16` ATTENTION | **47.7%**  | ~610 ms           | decode attention over the 1056-cell KV |
-| `cpy_scalar` KV copy/cast      | 18.3%      | ~234 ms           | KV write + f32->f16 casts |
-| `get_rows/set_rows` KV gather  | 17.8%      | ~228 ms           | **paged** gather of K/V/mask to contiguous |
-| `mul_mat_q` + `quantize_mmq`   | 15.7%      | ~201 ms           | NVFP4 weight GEMM (+ activation requant) |
-| rmsnorm / silu / rope / add    | ~0.6%      | ~8 ms             | elementwise |
-
-Cross-check: the GEMM bucket (~201 ms) matches the ctx-64 floor (215 ms) - i.e. the
-weight matmul is ~the entire short-context step, and is context-independent, as
-expected. KV/attention buckets (47.7+18.3+17.8 = **83.8%**) match the context-sweep
-finding that ~84% of the step scales with context.
-
-Power signature: ~33-36 W at 94% "utilization" (GB10 can pull far more). High util%
-+ low power = the kernels are **memory/latency-bound, not compute-saturated** - the
-classic decode signature (stream 19 GB of NVFP4 weights + a growing KV every step).
-
-### Stock vs paged decomposition
-
-- **Stock** (~650 ms): ~215 ms GEMM floor + ~435 ms attention/KV (contiguous KV read
-  directly by the FA kernel, **no gather**).
-- **Paged** (~1280 ms): same ~215 ms floor + ~610 ms attention + **~455 ms paged
-  gather/copy overhead** (the `get_rows` of K/V/mask plus the extra KV copy that
-  feeds the dense FA kernel). That ~455 ms (~36% of the step) is the paged engine's
-  self-inflicted cost and is the entire ~1.85x stock->paged regression.
-
-## vLLM decode architecture mapped onto each llama bucket
-
-vLLM at ~120-185 ms/step is faster on **every** bucket:
-
-| llama bucket (paged)        | ms    | vLLM equivalent | does vLLM avoid it? |
-|-----------------------------|-------|-----------------|---------------------|
-| paged KV gather (get_rows)  | ~228  | PagedAttention reads blocks **in-kernel** via a block table | **Yes - entirely.** No gather op exists. |
-| KV copy/cast                | ~234  | KV written once into block pool; FA reads it in place | Mostly - no per-step recopy |
-| decode attention            | ~610  | FlashInfer / FA paged-decode GQA kernel, split over KV | Same op, far faster kernel on sm_121 |
-| weight GEMM + act quant     | ~201  | fused Marlin/Machete W4A16 dequant+MMA, no separate quant pass | Faster + removes the requant kernel |
-| CPU sampling / loop         | ~0 (hidden) | on-GPU batched sampling | N/A here - already hidden on llama side too |
-
-vLLM's whole-step (~150 ms) is **less than llama's GEMM floor alone (~215 ms)**, so
-vLLM is ahead on the matmul *and* the attention *and* avoids the gather. The gap is a
-stack of kernel-efficiency wins, not one silver bullet.
-
-## Ranked levers - closable vs structural
-
-1. **Remove the paged gather regression. [Tractable, ~455 ms / ~36% on the paged
-   path; net-zero risk - it is a regression]** The worktree's paged engine makes
-   decode 1.85x slower than stock by gathering K/V/mask to contiguous every layer
-   every step (patch 0003 `ggml_get_rows`). For latency-bound decode, **do not enable
-   paged KV** - it only ever helps prefix-prefill *memory*, never decode latency.
-   Fully recovering this *and* keeping paging requires reading paged blocks
-   in-kernel like vLLM (a from-scratch paged-attention CUDA kernel) - see lever 2.
-
-2. **Long-context decode-attention kernel. [Biggest real lever, ~435 ms of stock /
-   ~610 ms of paged; partly structural]** Even stock is attention-bound at 1024 ctx.
-   llama.cpp's `flash_attn_ext_f16` decode path is ~4-5x slower than vLLM's
-   FlashInfer/FA paged-decode GQA kernel on this Blackwell-class part. This is the
-   cost that *grows with context* - exactly the regime the brief targets. Tractable in
-   principle (a proper flash-decoding / split-K-over-KV kernel, and a true in-kernel
-   paged read that also kills lever 1's gather), but it is deep CUDA work on a new
-   arch and partly gated by kernel maturity on sm_121. **Highest-impact, hardest.**
-
-3. **Thin-batch FP4 weight GEMM floor. [Tractable, ~201-215 ms / 15-30%; bounded]**
-   The NVFP4 `mul_mat_q` + separate `quantize_mmq` activation pass is memory-bound and
-   less efficient than vLLM's fused Marlin/Machete W4A16. Fusing dequant into the MMA
-   and folding the activation quant into the GEMM is tractable kernel work. Bounded
-   impact: the floor cannot drop below weight-read-bound (~19 GB / HBM BW per step).
-
-4. **Host serving loop / per-slot sampling. [NOT a lever]** Measured zero: greedy ==
-   heavy-sampler step time; GPU 94.6% busy. On-GPU/batched sampling buys nothing until
-   the kernels (levers 1-3) get fast enough to expose host overhead. Refutes the
-   "host-bound serving loop" hypothesis for this decode-bound workload.
-
-5. **Continuous-batch scheduler. [NOT the gap / structural elsewhere]** llama-server
-   already fuses all 32 slots into one decode step (one set of kernels per step over
-   batch 32 - confirmed in the trace). vLLM's continuous/chunked-prefill batching wins
-   on *mixed* prefill+decode overlap, but the steady decode-step gap measured here is
-   kernel-bound, not scheduler-bound.
-
-## Honest bottom line
-
-The ~4-6x per-step gap is **GPU-kernel-bound**, and it decomposes as:
-
-- ~36% of the *paged* step is a **self-inflicted gather regression** - remove it
-  (don't run paged for decode-latency workloads).
-- The remaining ~4-5x vs vLLM (true even for stock) is **kernel efficiency**:
-  llama.cpp's long-context decode-attention and thin-batch FP4 GEMM are slower than
-  vLLM's PagedAttention + Marlin on GB10. That is a **kernel project** (in-kernel
-  paged attention + flash-decoding + fused W4A16 GEMM), not a serving-loop project.
-- Sampling, detokenize, `update_slots`, and the continuous-batch scheduler are **not**
-  the gap; the GPU is ~95% busy on memory-bound kernels the whole step.
-
-What is closable: lever 1 (immediately, by not paging), lever 3 (bounded, with kernel
-work). What is structural / hard: lever 2 (the decode-attention kernel + a real
-in-kernel paged read), which is where the context-scaling gap actually lives and where
-any serious effort to approach vLLM on GB10 must go.
-
-## Reproduction (dev-tree only, `~/bench/decode_study/`)
-
-- `launch_srv.sh` / `runcfg.sh` - launch llama-server (paged on/off) and a config.
-- `client.py` - K=32 token-id fan-out (1024 prefix + 32 suffix), `SAMP=greedy|heavy`.
-- `d2drv.sh` - nsys pure-decode window (delay 70s past prefill) -> `srv_decode2.nsys-rep`.
-- `cat2.py` - kernel-time categorization from the sqlite export.
-- vLLM side: `~/bench/run_vllm.sh` + `vllm_prefix.py` (K=32, ~270 tok/s).
-</content>
-</invoke>

backend/cpp/llama-cpp/patches/paged/MOE_DENSITY_AUTO_TILE.md

@@ -1,143 +0,0 @@
-# Patch 0015 findings: expert-density-aware MoE token-tile auto-select
-
-The durable follow-up to patch 0014 (`MOE_TOKEN_TILE_CAP.md`): replace the blunt,
-opt-in `LLAMA_MOE_MMQ_X` global cap with a host-side, **default-on** density-aware
-`mmq_x` auto-select in `mul_mat_q_case`. Companion to
-`0015-paged-expert-density-aware-moe-token-tile-auto-select.patch`. Dev tree
-`~/llama-paged-dev` (branch `paged`), `build-cuda` sm_121.
-
-Primary model: **Qwen3.6-35B-A3B NVFP4** (`~/bench/q36-35b-a3b-nvfp4.gguf`),
-**256 experts, top-8**, expert FFN 512, GDN linear attention (SSM inner 4096),
-41 layers. This is a different beast from 0014's Qwen3-Coder-30B-A3B (128 experts,
-larger expert FFN, standard attention).
-
-## What it does (vs 0014)
-
-`mul_mat_q_case` picks the token-tile width `mmq_x` to cover `ncols_max` (= `ne12`,
-the per-expert column upper bound = token count) in one column-tile, i.e. stock
-**maximizes** the tile (128 on Blackwell). Applied per expert at MoE decode, where
-per-expert density is tiny, that 128-wide tile is mostly padding.
-
-Patch 0014 capped `mmq_x` globally on the ids path via `LLAMA_MOE_MMQ_X` (decode
-**and** prefill), which cost ~1.3% prefill. Patch 0015 instead estimates the
-per-expert density host-side, from args the ids path already passes:
-
-```
-ne_get_rows = ncols_dst   = ne12 * n_expert_used        (token-expert assignments)
-n_experts   = nchannels_x = ne02
-density     = ceil(ne_get_rows / min(ne_get_rows, n_experts))   (tokens/expert)
-```
-
-and caps to the small tile (default 64) **only when `density <= density_max`**, so
-the high-density prefill ubatch keeps the big 128 tile. Prefill-safe by construction.
-No new kernel: the selection only lowers the loop's upper bound to an
-already-compiled, granularity- and shared-memory-validated `mmq_x`.
-
-## The threshold matters: `density_max = 8`, not `tile/4 = 16`
-
-The cap must fire for decode but not for a prefill ubatch. Each has per-expert
-density `n_tokens * n_used / n_experts`. At the standard `n_ubatch=512`, `n_used=8`:
-
-```
-                       128 experts   256 experts
-prefill ubatch (512)        32            16
-decode npl128 (128)          8             4
-```
-
-`tile/4 = 16` (0014's first auto-select draft default) **equals the 256-expert
-prefill density** and caps prefill: measured -2.0% to -2.9% S_PP on q36-35b-a3b.
-`density_max = 8` sits strictly between decode and prefill for every `n_experts` in
-`[128, 511]`, so it caps decode and leaves prefill on the big tile. This single
-default change is what makes the patch prefill-safe on the 256-expert model.
-
-## Measurements (default-on vs stock, median of 5 reps)
-
-`llama-batched-bench`, q36-35b-a3b-nvfp4.gguf, `-fa on -npp 128 -ntg 128`, GB10
-sm_121. STOCK = `LLAMA_MOE_AUTO_TILE=0` (exact stock selection); 0015 = default.
-
-```
-  npl   S_TG stock  S_TG 0015   dTG%     S_PP stock  S_PP 0015   dPP%
-    8      183.59     183.18  -0.22%        1489.2     1500.1  +0.73%
-   32      264.02     263.44  -0.22%        2034.5     2033.5  -0.05%
-   64      311.76     310.41  -0.43%        2028.3     2027.6  -0.03%
-  128      336.10     337.32  +0.36%        2025.0     2027.7  +0.13%
-```
-
-Raw npl128 reps: S_TG 0015 `[337.3, 336.9, 336.4, 338.9, 338.1]` vs stock
-`[336.2, 336.1, 335.9, 336.9, 335.8]` (distributions overlap); S_PP 0015
-`[2028.6, 2023.0, 2024.9, 2028.0, 2027.7]` vs stock `[2024.9, 2025.0, 2023.2,
-2029.4, 2029.0]`.
-
-### Honest read: neutral on this model
-
-On q36-35b-a3b the decode delta is **within run-to-run noise** (npl128 +0.36%,
-npl<=64 slightly negative) and prefill is **neutral** (within +/-0.7%, well inside
-the 1% target). The `+5%` decode target from the localmaxxing reference does **not**
-materialize here. q36-35b-a3b decode is bound by the GDN/SSM recurrence and
-256-tiny-expert weight bandwidth, not the MoE col-tile occupancy, so the col-tile
-lever has nothing to bite on.
-
-### npl128 decode tile sweep confirms 64 is the only useful width
-
-`density_max=8` fixed, varying `LLAMA_MOE_DECODE_TILE`, S_TG @ npl128 vs stock:
-
-```
-  TILE8   TILE16  TILE32  TILE64  TILE96
- -6.31%   -3.18%  -0.17%  +0.70%  -0.76%
-```
-
-Smaller tiles are **worse**, not better: more column-tiles per expert = more
-grid/scheduling overhead, and the FP4-MMA has a minimum efficient width. So matching
-the tile to the literal density (4) is counterproductive; 64 is the sweet spot,
-same as 0014.
-
-## Why ship it default-on anyway
-
-1. **Removes 0014's prefill cost by construction.** The cap is density-gated, not
-   global, so prefill keeps its 128 tile (S_PP neutral above).
-2. **Banks the col-tile-bound gain for free.** At npl128 the auto-select picks
-   `tile=64` for a 128-expert model (decode density 8 <= 8), i.e. exactly 0014's
-   `cap64`, so it reproduces 0014's **+4.8% @npl128 on Qwen3-Coder-30B** without the
-   -1.3% prefill cost. (That model was unavailable to re-bench here; the tile choice
-   is identical by construction.)
-3. **Prefill-safe and decode-neutral on the SSM model**, so it is harmless where it
-   does not help.
-4. **Correctness-gated** by the P0 harness (below).
-
-## Conservative by design (known limitation)
-
-A pure-density gate cannot separate two cases with the **same** per-expert density:
-Qwen3-Coder npl256 decode (density 16) and the 256-expert prefill ubatch (density
-16) are identical to the estimator. `density_max=8` therefore **forgoes 0014's
-+2.3% @npl256** on the 128-expert model to keep 256-expert prefill safe. Recovering
-it needs an `ne12`-aware (absolute token count) gate in addition to density; scoped
-as future work, not implemented.
-
-## Knobs
-
-- `LLAMA_MOE_AUTO_TILE=0` : disable the auto-select, exact stock `mmq_x` selection.
-- `LLAMA_MOE_MMQ_X=<n>` (patch 0014) : **kept** as a manual override; when > 0 it
-  forces the old blunt global cap and bypasses the auto-select (explicit A/B knob).
-- `LLAMA_MOE_DECODE_TILE=<n>` : the small tile (default 64).
-- `LLAMA_MOE_DENSITY_MAX=<n>` : the density ceiling (default 8).
-
-## P0 correctness gate
-
-`tests/test-backend-ops` `test_mul_mat_id` is extended with a ragged small-M
-NVFP4/MXFP4 MoE decode-density block: 128 experts, top-8, m=768, k=2048, n in
-`{16,33,64,128,130,200,256,512}` spanning the cap boundary (n>=130 keeps the 128
-tile at `density_max=8`, n<=128 takes tile 64) and ragged token counts (experts with
-0/1/2 tokens, n not a multiple of the tile). All 16 shapes pass the CUDA-vs-CPU
-oracle on GB10 both default-on and with `LLAMA_MOE_AUTO_TILE=0`; full `MUL_MAT_ID`
-suite 2/2 backends OK. Off the ids path nothing changes (non-MoE `mul_mat`
-byte-identical to stock).
-
-## Verdict
-
-- Correct, prefill-safe, default-on density-aware tile select; the durable design
-  0014's own doc scoped. Supersedes 0014's global cap as the default path; the
-  `LLAMA_MOE_MMQ_X` knob is retained as a manual override.
-- **Net effect on q36-35b-a3b NVFP4: neutral** (decode within noise, prefill neutral)
-  because the model is SSM/bandwidth-bound, not col-tile-bound. The lever's real win
-  lives on col-tile-bound MoE (Qwen3-Coder-30B, +4.8% @npl128), banked here at zero
-  prefill cost.

backend/cpp/llama-cpp/patches/paged/MOE_GROUPED_GEMM_SCOPE.md

@@ -1,220 +0,0 @@
-# Durable scope: grouped FP4-MMA MoE GEMM for ggml CUDA on GB10 (sm_121)
-
-Build-ready plan. **Not implemented in this workflow** (large kernel work). This
-document scopes the durable path to match or beat vLLM MoE grouped-GEMM efficiency
-on GB10 for the Qwen3-30B-A3B-class mxfp4 MoE, and records the single honest
-finding that re-shapes the whole effort.
-
-Hardware: NVIDIA GB10 (sm_121, CC=1210 = `GGML_CUDA_CC_DGX_SPARK`), unified
-LPDDR5X ~273 GB/s. Model: Qwen3-Coder-30B-A3B, 128 experts, top-8, mxfp4 experts
-(`~/bench/qwen3coder-mxfp4.gguf`). Dev tree `~/llama-paged-dev` (branch `paged`,
-HEAD at patch 0013), `build-cuda` sm_121.
-
-## TL;DR (the honest reframe)
-
-**The grouped GEMM the mission scoped to build from scratch already exists in
-upstream ggml, and it already runs on GB10 for mxfp4.** For mxfp4 experts on
-sm_121 `ggml_cuda_should_use_mmq()` returns true (`turing_mma_available`), so
-MUL_MAT_ID takes the **grouped mmq path**, which already contains both vLLM
-building blocks:
-
-1. a moe_align / token-sort-by-expert (`mmid.cu` `mm_ids_helper`:
-   count -> warp-scan/cumsum -> scatter into expert-sorted contiguous buffers),
-2. a **single persistent stream-k grouped FP4-MMA GEMM** (one `mul_mat_q` launch;
-   grid flattened into kbc-continuous space over expert x col-tile x row-tile x
-   k-block; native FP4 MMA via `block_fp4_mmq` under `BLACKWELL_MMA_AVAILABLE`).
-
-The per-expert host-side row-gather loop in `ggml-cuda.cu`
-`ggml_cuda_mul_mat_id()` (~L2632-2790) - the path the mission's root-cause
-analysis describes as "the cliff" - is a **fallback only reached when
-`should_use_mmq()==false`** (f16/bf16 experts, non-Blackwell). It is **never the
-GB10 mxfp4 path.**
-
-Consequence: the "npl128 MoE cliff" does not exist on the current dev HEAD.
-Re-measured batched-bench decode (`S_TG` t/s) on the mxfp4 MoE rises monotonically
-`85 / 278 / 637 / 950 / 1306 / 1771` at npl `1 / 8 / 32 / 64 / 128 / 256`. The
-original `253/505/830/620` cliff was a real high-batch regression that has since
-been **fixed upstream** (FP4-native grouped mmq + MoE stream-k balancing), not a
-batched-bench artifact.
-
-**Therefore the durable work is NOT "port moe_align + a grouped GEMM."** It is a
-**surgical fix to the one place ggml diverges from vLLM: the M-tile (token-tile)
-sizing heuristic.** This document scopes that delta, plus the optional
-block-padded align, plus the parity gate and phased plan. It also records what is
-intentionally NOT built and why (the W4A16 occupancy wall).
-
-## The one structural gap: M-tile sizing
-
-`mul_mat_q_case` / `launch_mul_mat_q` pick `mmq_x` (the token/M tile) by
-**minimizing** `ntiles_x = ceil(ncols_max / mmq_x)` over the **aggregate** token
-count (`ncols_max = ne12`). On Blackwell `get_mmq_x_max = 128`, so the heuristic
-always selects the **largest** `mmq_x` that fits shared memory. vLLM's
-CUTLASS/Triton fused_moe does the **opposite**: a small tuned `BLOCK_SIZE_M`
-(typ. 16/32/64), padded **per expert**.
-
-ggml then applies its over-large `mmq_x` **per expert**. In MoE decode the tokens
-per expert is tiny - Qwen3-30B-A3B top-8 of 128: at npl64 ~512 assignments over
-~126 activated experts ~= 4 tok/expert; at npl128 ~1024 over ~128 ~= 8 tok/expert.
-So each expert's single M-tile of width 128 is **3-6% filled** -> ragged tiny-M
-tiles run a dense-GEMM-tuned config, wasting MMA M-throughput, and (with
-`need_check`) every expert runs as a masked partial tail.
-
-The FP4 MMA N-fragment (`tile_C::J`) is 8, so the **ideal M-tile ~= tokens/expert
-(~8)**, 16x smaller than the 128 ggml picks. This mismatch is the durable gap.
-
-Critically for GB10: at tokens/expert <= 8 there is exactly **one col-tile per
-expert**, so a smaller `mmq_x` causes **no extra weight re-read** (weight rows are
-re-read only across multiple col-tiles, of which there is one) while it **lowers
-shared-mem footprint and raises occupancy** - strictly aligned with the GB10
-occupancy lessons.
-
-## What already exists (reuse, do NOT rebuild)
-
-Engine files on DGX `~/llama-paged-dev/ggml/src/ggml-cuda/`:
-
-- **[A] moe_align / scatter** = `mmid.cu` `mm_ids_helper`. One CUDA block per
-  expert (`gridDim.x = n_experts`); warp counts tokens routed to this expert,
-  warp-scan for the compaction index, scatters into `ids_src1` (column gather
-  permutation, expert-sorted contiguous), `ids_dst` (output scatter), and writes
-  `expert_bounds[expert] = prefix start`, `expert_bounds[n_experts] = total`.
-  This **is** count -> cumsum -> permute; `expert_bounds` is the analogue of
-  vLLM's `num_tokens_post_padded` boundaries. No `-1` pad today because segments
-  are exact (not block-padded).
-- **[B] persistent grouped FP4 GEMM** = `mmq.cuh` `mul_mat_q` stream-k
-  (kernel ~L3542, `process_tile` ~L3447, launch ~L3943, case-select ~L4055).
-  Single launch, fixed grid (`nsm` CTAs, or `ntiles` when >=90% tile efficiency).
-  Each CTA walks a contiguous `kbc` slice of (expert `zt` via `expert_bounds`,
-  col-tile `jt`, row-tile `it`, k-block) space; the weight row-tile (`mmq_y=128`
-  x K) is loaded once per col-tile in the `process_tile` k-loop; empty col-tiles
-  past `col_diff` are SKIPPED by advancing `kbc += blocks_per_ne00`; a
-  `stream_k_fixup` pass recombines split tiles.
-- **[C] native FP4-MMA expert weights** = `block_fp4_mmq` + `MMQ_MMA_TILE_X_K_FP4`
-  (== Q8_1 tile, skew-pad +4) under `BLACKWELL_MMA_AVAILABLE`;
-  `quantize_mmq_fp4_cuda` quantizes activations to the q8-style y-layout **with
-  the `ids_src1` gather fused** (one pass, no separate row-copy).
-
-Dispatch seam: `ggml-cuda.cu` `ggml_cuda_mul_mat_id()` (~L2632-2790). For mxfp4
-with `ne2`(tokens) > 7, `should_use_mmq()` -> true -> `ggml_cuda_mul_mat_q()`
-(`mmq.cu` id-branch ~L162-225) -> `mm_ids_helper` then ONE
-`mul_mat_q_switch_type`. The per-expert host loop below it is the gated fallback.
-
-(Below npl8, MXFP4 mmid routes through `mmvq` - `MMVQ_MAX_BATCH_SIZE=8`, mmid max
-7 for turing_plus - which is fine for thin batch and out of scope here.)
-
-## What to add (the durable delta, priority order)
-
-### [1] Expert-aware M-tile selection (host-side only, zero new kernel)
-
-In `mul_mat_q_case` / `launch_mul_mat_q`, when `ids != null`, choose `mmq_x` from
-**per-expert density** (~`ne_get_rows / n_active_experts`, derivable cheaply, or
-capped via env) instead of minimizing `ntiles` over aggregate `ncols_max`.
-
-- `mmq_x` is a **compile-time template** (switch 8..128 step 8), so this is a pure
-  host-side SELECTION change - it picks a different already-compiled instantiation.
-  **Zero new kernel. Very low risk, high leverage.** Matches vLLM `BLOCK_SIZE_M`.
-- Doubles as near-term lever-1: env-gated `LLAMA_MOE_MMQ_X` cap at the knee.
-- GB10-aligned: smaller `mmq_x` -> smaller shared mem -> higher occupancy, and at
-  tokens/expert <= 8 (one col-tile/expert) it costs no extra weight read.
-
-This is the single highest-leverage change and the seed of the durable port.
-
-### [2] Block-padded moe_align (the moe_align_block_size port proper)
-
-Extend `mm_ids_helper` to pad each expert segment up to a multiple of the chosen
-block: write a sentinel (`-1`) `ids_dst` for pad lanes, put `expert_bounds` on
-block boundaries. Then every col-tile is **full**, which:
-
-- drops the `need_check` masking + per-expert partial-tail MMA,
-- makes the stream-k `kbc` space exact (no skipped tiles, cleaner persistent
-  schedule), removing the `col_diff` skip branch.
-
-Medium risk: touches the scatter, the `col_diff`/`need_check` logic, and the
-`write_back` masking (pad rows must not write output). This is the proper
-`moe_align_block_size` analogue and the durable second step.
-
-### [3] Bespoke masked-grouped FP4 kernel - ONLY if [1]+[2] insufficient
-
-A CUTLASS/DeepGEMM-style masked-grouped FP4 kernel. **Largest risk, likely
-unnecessary** given [B] is already a persistent stream-k grouped GEMM. Listed for
-completeness; do not start without [1]+[2] measured as insufficient.
-
-## Integration into ggml_mul_mat_id (dispatch seam + gated fallback)
-
-- The seam is unchanged: `ggml_cuda_mul_mat_id()` -> `should_use_mmq()` ->
-  `ggml_cuda_mul_mat_q()`. [1] and [2] live entirely inside the mmq id-branch
-  (`mmq.cu` ~L162-225) and its callees (`mmq.cuh` selection/launch, `mmid.cu`
-  scatter). No change to the host dispatch decision.
-- **Gated fallback preserved**: the existing per-expert host loop
-  (`should_use_mmq()==false` path) stays as-is for f16/bf16 experts and
-  non-Blackwell GPUs. The new selection only fires on the grouped path.
-- **Env gates** (off = exact current behavior):
-  - `LLAMA_MOE_MMQ_X=<8..128>` - cap/override the token tile for the id-path
-    (lever-1 + [1] manual knob).
-  - `LLAMA_MOE_BLOCK_ALIGN=0|1` - enable block-padded scatter ([2]).
-  Default both off until parity + throughput proven, then flip [1]'s
-  auto-selection on by default.
-
-## Correctness / parity gate
-
-Primary: `tests/test-backend-ops.cpp` `test_mul_mat_id` (~L4181). The CPU
-reference is **deterministic** - the op test must be **bit-exact**.
-
-- Sweep `type_a` in {`MXFP4`, `NVFP4`}, `type_b = F32`, `n_mats = 128`,
-  `n_expert_used = 8`, `n_tokens` in {8, 32, 64, 128} (the decode-density band).
-- **Add ragged small-M shapes** to the harness if absent (n_tokens not a multiple
-  of mmq_x; experts with 0/1/2 tokens) - these are exactly where [1]/[2] change
-  tile geometry and where block-pad masking can leak.
-- Pass criterion: new `mmq_x` selection and padded-align produce dst **identical**
-  to current op-test output (op test is exact; the GB10 CUDA greedy-decode
-  non-determinism band applies only to end-to-end, never to the op test).
-- End-to-end sanity: `llama-batched-bench` on `~/bench/qwen3coder-mxfp4.gguf`,
-  `-fa on -npp 128 -ntg 128`, npl 8/32/64/128/256; confirm `S_TG` stays monotonic
-  and `S_PP` flat ~3050-3090. Verify greedy-decode output within the documented
-  CUDA batch-shape non-determinism band (CPU is the deterministic oracle).
-
-Bench/parity scripts stay **dev-tree-only** (`~/llama-paged-dev/benches/`).
-
-## Phased plan, expected payoff, risk per phase
-
-| Phase | Work | Expected payoff | Risk |
-|-------|------|-----------------|------|
-| **P0** harness | Add ragged small-M + MXFP4/NVFP4 mmid shapes to `test_mul_mat_id`; capture current bit-exact baseline + the monotonic batched-bench curve as the reference. | None (gate). Locks correctness + the 85->1771 t/s baseline so any regression is caught. | Low. |
-| **P1** sort op | Confirm `mm_ids_helper` is the moe_align; if [2] is pursued, prototype the block-pad scatter behind `LLAMA_MOE_BLOCK_ALIGN`. | Enables exact stream-k schedule; removes `need_check` masking (P3 payoff). | Medium (scatter + write-back masking). |
-| **P2** grouped GEMM ([1]) | Expert-aware `mmq_x` selection in `mul_mat_q_case`/launch, `LLAMA_MOE_MMQ_X` gate. | The headline: reclaim the 3-6% M-tile fill waste at npl64-128. Modeled as removing wasted MMA M-throughput on every activated expert; net throughput up at high batch with no extra weight read. | **Low** (host-side template selection, no new kernel). |
-| **P3** tune ([2] + fixup) | Land block-padded align; tune `mmq_x` per density, profile stream-k `fixup` overhead and `mmq_x`/`mmq_y` tile choice with nsys on the grouped `mul_mat_q<MXFP4>` kernel. | Remove per-expert partial-tail MMA; tighten the persistent schedule. Diminishing vs P2; this is pure micro-efficiency toward/past vLLM's saturated grouped-GEMM. | Medium-high (kernel masking paths). |
-
-**Honest payoff framing:** the npl128 "cliff" is already gone on HEAD, so there is
-no broken path to unlock. The durable win is **matching vLLM's saturated
-grouped-GEMM M-tiling** (small per-expert block) and erasing the dense-GEMM-tuned
-M-tile mismatch - a micro-efficiency gain at large effective batch, not a
-step-change. vLLM 0.23.0 cannot even serve this model on GB10 (bf16 MoE-warmup
-hang + hard reboot; GGUF loader can't map fused qwen3moe experts), and llama
-already uses the same sorted-grouped-GEMM algorithm, so structural parity is
-**already met**; this closes the residual kernel micro-gap.
-
-## The biggest risk: the GB10 W4A16 occupancy wall
-
-The dominant risk is **repeating the W4A16 dead-end** that hit only ~9 TFLOPS /
-178 t/s on GB10. GB10 is **occupancy-dominated**: deep `cp.async` pipelines and
-XOR-swizzle shared layouts **collapse occupancy** there. Any P3 kernel work MUST:
-
-- keep **small shared mem + high occupancy** (do NOT add deep `cp.async` stages
-  or XOR-swizzle - they are exactly what killed W4A16);
-- preserve the **skew-pad (+4)** tile layout already in `MMQ_MMA_TILE_X_K_FP4`;
-- stay on the **FP4-MMA path** (`block_fp4_mmq`), the only path that hits Blackwell
-  FP4 = 2x INT8/BF16 rate;
-- respect the ~273 GB/s LPDDR5X weight-read floor (dense decode is already at it;
-  MoE wins come from occupancy/tile fit, not bandwidth).
-
-Smaller `mmq_x` ([1]) is **strictly consistent** with these lessons: it reduces
-shared-mem footprint, raises occupancy, and at tokens/expert <= 8 adds no weight
-re-read. So the low-risk lever ([1]) is also the one most aligned with what GB10
-rewards - which is why it leads the plan and [3] is gated behind it.
-
-## Commit / hygiene
-
-Scope doc only (this file). No engine change committed in this workflow. Bench and
-parity scripts are dev-tree-only. Commit with `git -s`, trailer
-`Assisted-by: Claude:opus-4.8 [Claude Code]`, no `Co-Authored-By`, no em-dashes.
-Do not push (human pushes). When [1]/[2] are implemented they mirror to
-`backend/cpp/llama-cpp/patches/paged/0014-*` (next free slot).

backend/cpp/llama-cpp/patches/paged/MOE_TOKEN_TILE_CAP.md

@@ -1,99 +0,0 @@
-# Patch 0014 findings: expert-aware MoE token-tile cap (LLAMA_MOE_MMQ_X)
-
-Near-term lever for the MoE-vs-vLLM workflow on GB10 (sm_121). Companion to
-`0014-paged-expert-aware-moe-token-tile-cap.patch`. Model:
-Qwen3-Coder-30B-A3B, 128 experts, top-8, mxfp4 experts
-(`~/bench/qwen3coder-mxfp4.gguf`). Dev tree `~/llama-paged-dev` (branch `paged`),
-`build-cuda` sm_121.
-
-## Headline (honest): there is no npl128 cliff to erase on this build
-
-The mission premise was a 25% decode drop at npl128 (batched-bench 253/505/830/620
-@ npl 8/32/64/128). It does **not** reproduce. Stock decode is monotonic:
-
-```
-llama-batched-bench, qwen3coder-mxfp4.gguf, -fa on, -npp 128 -ntg 128, S_TG t/s
-  npl        1     8    32    64   128   256
-  stock     85   282   629   935  1295  1779     <- monotonic, no knee
-```
-
-The old cliff was a real high-batch regression since fixed upstream: mxfp4 MoE
-decode on GB10 already takes the sorted grouped FP4-MMA GEMM (MUL_MAT_ID ->
-`ggml_cuda_mul_mat_q` ids branch: `mm_ids_helper` moe_align/scatter + one
-persistent stream-k `mul_mat_q`), i.e. vLLM's algorithm. See
-`MOE_GROUPED_GEMM_SCOPE.md`.
-
-## What the knob does
-
-`mul_mat_q_case` picks the token-tile width `mmq_x` to cover `ncols_max`
-(= `ne12`, the per-expert column upper bound = token count, up to 128) in one
-column-tile. At MoE decode the per-expert density is `~ne12*k/n_experts`
-(top-8/128 => ~1/16 of `ne12`), so each expert's `mmq_x`-wide col-tile is only
-~6% filled: the MMA accumulator tile is `mmq_x`-wide at compile time and wastes
-throughput on the padding columns, and the larger y-tile lowers occupancy.
-
-`LLAMA_MOE_MMQ_X=<n>` caps `mmq_x` on the MUL_MAT_ID path only
-(`expert_bounds != nullptr`). It only lowers the selection-loop upper bound and
-still chooses from the same granularity/shared-memory-validated `mmq_x` set stock
-already uses for smaller batches - no new kernel configuration. Default
-(unset/<=0) = disabled => byte-identical to stock.
-
-## Measurements (same binary, only LLAMA_MOE_MMQ_X differs)
-
-Decode throughput, S_TG t/s:
-
-```
-  npl     stock   cap16   cap32   cap64
-   1       85      85      85      85
-   8      282     280     282     282
-  32      629     623     629     628
-  64      935     915     949     934
- 128     1295    1204    1344    1357     <- cap64 +4.8% (cap16 -7%)
- 256     1779    1370    1723    1820     <- cap64 +2.3% (cap16 -23%)
-```
-
-Prefill throughput, S_PP t/s (the cost):
-
-```
-  npl     stock   cap16   cap32   cap64
- 128     3083    1817    2559    3038
- 256     3084    1818    2560    3046
-                 -41%    -17%    -1.3%
-```
-
-Reproducibility (interleaved off/cap64, two reps each):
-
-```
-  npl    off rep1/rep2   cap64 rep1/rep2
-  128    1300 / 1290     1357.5 / 1357.0
-  256    1786 / 1782     1826.3 / 1824.5
-```
-
-cap64 is stable to <0.1% and the gain sits well above the ~1% run-to-run band.
-
-## Why 64 is the only value that helps net
-
-A 512-token prefill ubatch routes ~32 tokens/expert. cap16/cap32 force those into
-16/32-wide tiles, overflowing into extra col-tiles + weight re-reads -> prefill
-craters (-41% / -17%). cap64 still holds the prefill density in one tile (32 < 64)
-so prefill is near-neutral (-1.3%), while decode (~8 tokens/expert at npl128) gets
-the fuller, higher-occupancy tile.
-
-## Verdict
-
-- Real but **modest** high-effective-batch DECODE micro-optimization
-  (+4.8% npl128, +2.3% npl256), neutral at npl<=64, ~1.3% prefill cost at cap64.
-- **Not** a cliff fix (no cliff) and **not** a real-server unlock (llama-server
-  continuous batching already scales). Shipped as an opt-in, default-off knob;
-  recommended value 64 for decode-heavy high-concurrency deployments.
-- Correctness: greedy temp-0 server output with cap64 is byte-identical to stock
-  for single-stream generation and stays coherent; thousands of capped MoE
-  matmuls at npl128/256 ran with no CUDA error / NaN.
-
-## Durable follow-up (scoped, not implemented)
-
-Replace the blunt global cap with a density-aware auto-select: choose `mmq_x`
-from `ne_get_rows / n_active_experts` inside `mul_mat_q_case` so decode gets the
-small tile while prefill keeps its large tile automatically (removes the ~1.3%
-prefill cost). Plus the block-padded `moe_align` in `mm_ids_helper`. See
-`MOE_GROUPED_GEMM_SCOPE.md`.

backend/cpp/llama-cpp/patches/paged/PAGED_BENCH.md

@@ -1,107 +0,0 @@
-# Paged-KV: GPU 0007 re-run + shared-prefix throughput benchmark
-
-DGX Spark (NVIDIA GB10, sm_121 / cc 12.1), CUDA 13, dev tree `~/llama-paged-dev`
-branch `paged`, base pin `f3e182816421c648188b5eab269853bf1531d950`, full paged
-engine (0001-0004, 0006, 0007). All paged behaviour stays gated by
-`LLAMA_KV_PAGED`; default-off is byte-identical to stock. Models:
-`Qwen3-0.6B-Q8_0.gguf` and `Qwen3-32B-Q4_K_M.gguf`.
-
-## Deliverable 1 - GPU run of the 0007 prefix-engine correctness driver
-
-The committed driver `examples/simple/paged-prefix-engine.cpp` hardcodes
-`n_gpu_layers = 0`. For this GPU run it was given a dev-only
-`PAGED_NGL` env override (`mp.n_gpu_layers = getenv("PAGED_NGL") ? atoi(...) : 0`),
-rebuilt in `build-cuda`, run, then the edit was **reverted** so the committed
-driver stays byte-clean (it is dev scaffolding, never shipped in a patch).
-
-Three runs of the same Gate-0 driver, Qwen3-0.6B, `LLAMA_KV_PAGED=1`:
-
-| binary / offload                         | result                  |
-|------------------------------------------|-------------------------|
-| committed `build-cpu` driver             | **ALL PASS (failures=0)** |
-| `build-cuda`, `PAGED_NGL=99` (all layers)| GATE FAILED (failures=3)|
-| `build-cuda`, `PAGED_NGL=0` (same binary)| GATE FAILED (failures=2)|
-
-**The GPU run did NOT print ALL PASS - reported honestly.** But the failures are
-narrow and are not a paged-engine bug:
-
-- Every **structural / mechanical** paged invariant PASSES on GPU, in both
-  scenarios (boundary and mid-block): prefill computed ONLY the suffix (32 prefix
-  tokens skipped), shared prefix block-aligned, shared-block `ref_cnt == 2` while
-  both sequences hold it, ref drops `2 -> 1` on freeing one sharer, only the
-  private (suffix) blocks are returned, and the prefix block returns to the pool
-  once all sharers free. The cross-request KV reuse mechanism itself is GPU-clean.
-- The only failures are the **exact greedy-token byte-identical** assertions
-  (e.g. boundary `B-shared` vs `B-from-scratch`). They diverge at a single near-tie
-  token (boundary: 2nd generated token `17971` vs `5671`) and then cascade
-  autoregressively.
-
-Root cause is **CUDA float-kernel non-determinism, not the paged logic**: the
-*same* CUDA binary fails the exact-token assertions even with `PAGED_NGL=0` (zero
-layers offloaded), whereas the genuine `build-cpu` binary passes all 16/16. The
-CUDA backend (loaded via `ggml_backend_load_all`) uses non-associative reductions
-whose result differs between the full-prefill batch shape and the
-incremental-suffix batch shape; under greedy decode a single logit near-tie flips
-and the sequences cascade apart. This refines the earlier note in
-`PAGED_GPU_VERIFY.md` (which framed it as "not GPU-specific" and had no CPU pass
-to compare against): the CPU build now passes clean, so the divergence is a strict
-test-assertion artefact of CUDA float ordering, not a defect in 0006/0007.
-
-## Deliverable 2 - shared-prefix throughput benchmark (the real-win test)
-
-Dev-only driver `examples/simple/paged-prefix-bench.cpp` (registered in
-`examples/simple/CMakeLists.txt`, dev tree only - not in any shipped patch).
-Workload: `K` sequences that all share a `P`-token common prefix (a system /
-RAG preamble), each with a unique `S`-token suffix; prefill only (`G=0`,
-generation is identical compute in both modes so it is excluded from the
-headline). GPU, `-ngl 99`, `kv_unified = true`.
-
-- **NO-SHARE (stock):** `LLAMA_KV_PAGED` unset; every sequence prefills the full
-  `P+S` tokens. Total prefill work `= K*(P+S)`.
-- **PAGED-SHARE:** `LLAMA_KV_PAGED=1`; the prefix is computed ONCE on seq 0,
-  committed via `paged_prefix_api::commit`, then every other seq calls
-  `paged_prefix_api::share` to physically reuse the ref-counted prefix blocks and
-  prefills ONLY its suffix. Total prefill work `= P + K*S`.
-
-**`kv_unified` note:** this engine's cross-request share is built around the
-*unified* stream-0 pool (ref-counted shared cells), so `kv_unified = true` is what
-makes the share engage - the same setting the committed 0007 driver uses. With
-`kv_unified = true` the share engaged in every run (evidence below).
-
-### Reuse actually engaged (share mode)
-
-In every share run: `kshare(seq 1) = 1024` (the full block-aligned prefix is
-reused, not recomputed), the shared prefix block's `ref_cnt == K` (all sharers
-point at one physical copy), and `prefill_tokens_submitted` collapses from
-`K*(P+S)` to `P + K*S`.
-
-### Results (P=1024, S=32, prefill-only)
-
-| model        | K  | mode      | prefill tokens | prefill time | raw tok/s | shared ref_cnt |
-|--------------|----|-----------|----------------|--------------|-----------|----------------|
-| Qwen3-0.6B   | 32 | no-share  | 33792          | 4.659 s      | 7253      | -              |
-| Qwen3-0.6B   | 32 | **share** | 2048           | **0.554 s**  | 3695      | 32             |
-| Qwen3-32B    | 16 | no-share  | 16896          | 26.14 s      | 647       | -              |
-| Qwen3-32B    | 16 | **share** | 1536           | **3.64 s**   | 422       | 16             |
-| Qwen3-32B    | 32 | no-share  | 33792          | 61.91 s      | 546       | -              |
-| Qwen3-32B    | 32 | **share** | 2048           | **6.02 s**   | 340       | 32             |
-
-### Verdict: YES, a real and substantial win, and it grows with K
-
-- Prefill wall-time speedup: **0.6B K=32 -> 8.4x**, **32B K=16 -> 7.2x**,
-  **32B K=32 -> 10.3x**. The win grows with the number of sharers because
-  no-share prefix recompute is `O(K)` while the shared prefix is `O(1)` plus
-  `K` tiny suffixes.
-- Note the honest caveat in the raw-throughput column: share mode submits small
-  32-token suffix batches that are *less* GPU-efficient (340-422 tok/s) than the
-  large no-share batches (546-7253 tok/s). The win is **not** higher tok/s - it is
-  computing ~11-16x **fewer** tokens. On a fast GB10 prefill that still nets a
-  7-10x wall-time reduction because prefill is compute-bound and the shared prefix
-  dominates the token count.
-- This is exactly the many-users-one-system-prompt / RAG-preamble fan-out
-  scenario, and the paged cross-request prefix cache delivers there.
-
-Scaffolding (`paged-prefix-bench.cpp`, the `PAGED_NGL` driver tweak) stays
-dev-tree-only and is not part of any shipped patch.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]

backend/cpp/llama-cpp/patches/paged/PAGED_GPU_VERIFY.md

@@ -1,81 +0,0 @@
-# Paged-KV GPU verification + full backend CUDA build
-
-Verification run on a DGX Spark (NVIDIA GB10, compute capability 12.1 / sm_121),
-CUDA 13.0, against pin `f3e182816421c648188b5eab269853bf1531d950`. Models:
-`Qwen3-0.6B-Q8_0.gguf` (core gate) and `Qwen3-32B-Q4_K_M.gguf` (sanity).
-
-All paged behaviour stays gated by `LLAMA_KV_PAGED` (env) / the `kv_paged`
-server option; default-off is byte-identical to stock.
-
-## Deliverable 1 - GPU-path correctness (all on GPU, `-ngl 99`)
-
-CUDA build of the dev tree configured with
-`-DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=121 -DCMAKE_BUILD_TYPE=Release`;
-all paged drivers (`llama-simple`, `llama-paged-multiseq`,
-`llama-paged-prefix`, `llama-paged-prefix-engine`) compiled clean under sm_121.
-
-1. Core token-identical gate - PASS. `llama-simple` greedy, Qwen3-0.6B, `-ngl 99`:
-   stock (env unset) vs `LLAMA_KV_PAGED=1` output is BYTE-IDENTICAL. The paged
-   path is genuinely engaged: `LLAMA_KV_PAGED_DEBUG=1` shows the device gather
-   firing (`[paged-attn] gather n_stream=1 ...`), per-token block placement
-   (`[paged-alloc] ... grew`), and the stock run uses CUDA Graphs while the paged
-   run takes the distinct gather path - yet output matches exactly.
-
-2. Multi-stream - PASS. `llama-paged-multiseq -s 4 -ngl 99`, stock vs paged:
-   all 4 concurrent sequences BYTE-IDENTICAL on GPU (n_seqs=4, CUDA0 compute
-   buffer matches expectation). Same result reproduced on the CPU build.
-
-   Prefix recompute-skip (`llama-paged-prefix-engine`, patch 0007) - MIXED, and
-   this is a dev-scaffolding driver ("not shipped"); it was never built on CPU
-   (absent from the CPU Gate-0 set), so there is no prior CPU pass to match.
-   The driver hardcodes `n_gpu_layers = 0`; a reported test-harness-only env
-   override (`PAGED_NGL`) was added to run it at `-ngl 99` (29/29 layers
-   offloaded confirmed), then reverted. Results are IDENTICAL on CPU and GPU
-   (so not a GPU issue):
-   - PASS: measured recompute-skip (32 prefix tokens skipped, block-aligned),
-     ref-count == 2 on shared block, ref drop 2->1 on free, only-private-blocks
-     returned, block returned to pool.
-   - FAIL: 2 of ~16 greedy-token-equality assertions. `boundary` case diverges
-     from the from-scratch baseline at the 2nd generated token (`17971` vs
-     `5671`) and then completely; `mid-block` "A re-shareable after free, output
-     unchanged" also differs. Driver prints `GATE FAILED (failures=2)`.
-   This is a divergence in the prefix recompute-skip path (0006/0007), NOT in the
-   core gather gate, and not GPU-specific. Reported, not fixed (out of scope).
-
-3. 32B GPU sanity - PASS. `LLAMA_KV_PAGED=1 llama-simple -ngl 99 -n 16` on
-   Qwen3-32B-Q4_K_M (65/65 layers offloaded): coherent output
-   ("The capital of France is Paris..."), no crash, no OOM.
-
-## Deliverable 2 - full backend build with the paged patches
-
-Built in a nested LocalAI tree on the DGX; gRPC v1.59.0 built from source
-(LocalAI bundle; the system protobuf ships no CMake CONFIG) in ~26 min.
-
-- (2a) `make llama.cpp LLAMA_PAGED=on` - PASS. All 6 paged patches
-  (0001,0002,0003,0004,0006,0007) `git apply` cleanly to the pin (EXIT=0). The 8
-  vendored paged sources land in `llama.cpp/src/` and are BYTE-IDENTICAL to the
-  dev tree; `grpc-server.cpp` carries the `kv_paged`/`paged_attention` option
-  (patch 0005); `llama-kv-cache.cpp` has the env-gated hooks.
-
-- (2b) grpc-server under CUDA sm_121 - PASS (with the single-application caveat
-  below). 89 MB ARM aarch64 executable, build ~139 s, linked against
-  libcudart.so.13 / libcublas.so.13; binary contains the paged option strings
-  and `paged_alloc`/`paged_attn`/gather symbols.
-
-- (2c) `make llama.cpp LLAMA_PAGED=off` - PASS. "skipping paged-attention patch
-  series", EXIT=0, NO `paged-*` sources in the checkout (clean escape hatch).
-
-### Build-flow finding: paged patches are applied TWICE in the on-flow
-
-A plain `make grpc-server LLAMA_PAGED=on` FAILS to compile. The paged series is
-applied by BOTH the Makefile `llama.cpp` target (`git apply`) AND `prepare.sh`
-(`patch -p1`). On the already-git-applied tree, `prepare.sh` hits "Reversed (or
-previously applied) patch detected! Assume -R? [n]", declines, and re-applies the
-pure-addition hunks a second time. `llama_kv_cache::get_n_gather` etc. end up
-defined twice -> redefinition errors in `llama-kv-cache.cpp` (`.rej`/`.orig`
-litter `src/`). Single application (one of the two appliers) compiles clean -
-the 2b build above used a single git-apply with `prepare.sh` patching suppressed.
-Reported only; the fix (drop one of the two application sites for
-`patches/paged/`) is out of scope for this verification.
-
-Assisted-by: Claude:opus-4.8 [Claude Code]

backend/cpp/llama-cpp/patches/paged/PAGED_VLLM_APPLES.md

@@ -1,111 +0,0 @@
-# Paged llama.cpp vs vLLM - apples-to-apples (batched + NVFP4 + prefix cache)
-
-Definitive matched comparison on a DGX Spark (GB10, sm_121). Both engines batched,
-both NVFP4-class weights, both with prefix caching on, both eager (no CUDA graphs).
-Workload: shared 1024-token system prefix + unique 32-token suffix, generate 64
-tokens, K requests fired concurrently (cold fan-out), one client hitting both
-OpenAI-compatible servers with identical token-id prompts.
-
-This run fixes the two confounders in the earlier comparison (a *serial* Q4_K dev
-driver vs a *batched* FP4 vLLM server). Here both sides are batched and NVFP4.
-
-## Setup
-
-- llama.cpp: `llama-server` built from the paged dev tree (`~/llama-paged-dev`,
-  branch `paged`, patches 0001-0007), CUDA `build-cuda/` (sm_121).
-  `LLAMA_KV_PAGED=1`, `-ngl 99 --parallel 32 -c 40960`, model
-  `q3-32b-nvfp4-dense.gguf` (NVFP4 weights, FP4-MMA kernel). OpenAI `/completion`.
-- vLLM 0.23.0: `vllm serve q3-32b-nvfp4a16/` (compressed-tensors W4A16 / Marlin),
-  `--enforce-eager --max-model-len 4096 --gpu-memory-utilization 0.9
-  --max-num-seqs 64`, APC on (default). OpenAI `/v1/completions`.
-
-## Finding 1 - the paged cross-request prefix cache does NOT engage in llama-server
-
-This is itself a key result. The paged engine has two distinct mechanisms:
-
-1. Physical paged block placement (patches 0002/0004) - runs inside
-   `llama_kv_cache::find_slot`, gated only by `LLAMA_KV_PAGED`. This DOES engage in
-   the server: with `LLAMA_KV_PAGED_DEBUG=1`, 2 concurrent shared-prefix requests
-   produced 14 `[paged-alloc] ... grew` lines, one stream per `seq`.
-
-2. Cross-request prefix recompute-skip (patch 0007) - the actual fan-out win
-   (`shares N prefix blocks ... prefix NOT recomputed`, ref-counted block sharing).
-   This is reachable ONLY through `paged_prefix_api::share/commit`
-   (`src/paged-prefix-api.cpp`), which only the standalone driver calls.
-
-Evidence it does not reach the server:
-- Static: `grep -rn "paged_prefix\|share_prefix\|LLAMA_KV_PAGED" tools/server/`
-  returns nothing; `nm` on the binary finds no `paged_prefix` symbol use from the
-  server path. Nothing in `llama_decode` or the server calls `share`/`commit`.
-- Runtime: the 2-request verify run logged **0** `shares prefix blocks` /
-  `NOT recomputed` lines. Both `seq=0` and `seq=1` independently grew to 65 blocks,
-  each allocating and recomputing the full ~972-token prefix separately - no
-  cross-slot KV block sharing, no `ref_cnt>1`.
-
-So the 0007 recompute-skip, proven in the driver, does **not** yet reach the
-server. Closing it needs server-side wiring: when admitting a slot whose prompt
-shares a prefix with another live/committed slot, the server would have to call
-the `paged_prefix_api::share` / `commit` seam. That is a future patch.
-
-Note: llama-server has its OWN native prefix reuse (the slot prompt cache /
-"context checkpoints"). In the K=32 wave the server reused the prefix cached by the
-earlier wave, so prefill was only the 32-token suffix (`prompt eval ... / 32
-tokens`). But that is a separate mechanism, it only helps prefill, and prefill is
-not the bottleneck here (see below), so it does not change the verdict.
-
-## Finding 2 - the matched comparison
-
-Both batched, both NVFP4, both prefix-cache on, both eager. Cold concurrent fan-out,
-identical token-id prompts via one client.
-
-| K  | engine   | wall (s) | aggregate gen tok/s | req/s | vLLM speedup |
-|----|----------|----------|---------------------|-------|--------------|
-| 16 | llama.cpp| 50.7     | 18.9                | 0.30  | -            |
-| 16 | vLLM     | 8.57     | 119.5               | 1.87  | ~5.9x        |
-| 32 | llama.cpp| 58.3     | 34.0                | 0.53  | -            |
-| 32 | vLLM     | 8.86     | 231.1               | 3.61  | ~6.6x        |
-
-vLLM APC confirmed engaged: prefix cache hit rate 90.9% (K=16), 95.5% (K=32),
-enforce_eager (CUDA graphs disabled), `enable_prefix_caching=True`.
-
-### Verdict: not competitive - vLLM ~6x faster, and prefix caching is not why
-
-With every confounder removed (both batched, both NVFP4, both eager, both with
-prefix caching on), vLLM is still ~6x faster end-to-end. The gap is decode-bound,
-not prefill/cache-bound:
-
-- The G=64 workload is dominated by decode. In the llama K=32 run, decode was
-  52.98s of the 58.3s wall; prefill was ~3.5s (and only the 32-token suffix, since
-  the server's native prompt cache already reused the prefix). So even perfect
-  prefix sharing - paged or native - cannot move the total much.
-- llama.cpp batched decode: **~828 ms per decode step** at batch 32
-  (1.21 tok/s per sequence).
-- vLLM batched decode: ~170 tok/s aggregate gen at 32 running reqs ->
-  **~185 ms per step**, roughly **4-5x faster per decode step**.
-- CUDA graphs are NOT the differentiator: both sides are eager (llama
-  `graphs reused = 0`, vLLM `--enforce-eager`). The win is vLLM's batched-decode
-  efficiency: PagedAttention + fused W4A16 (Marlin) GEMMs + chunked-prefill
-  scheduler, versus llama.cpp's per-step eager graph and NVFP4-GGUF decode path on
-  this Blackwell-class part.
-
-Because decode dominates, wiring the paged 0007 recompute-skip into the server
-(Finding 1) would mainly remove redundant prefill across slots - a real saving for
-short-generation / long-prefix RAG fan-out, but at G=64 it is a few seconds against
-a decode floor that is already ~6x slower than vLLM. The fan-out win does not, on
-its own, make llama.cpp competitive here; the decode kernel/batching gap is the
-load-bearing factor.
-
-## Caveats
-
-- NVFP4-GGUF is double-quant and is speed-representative (it routes onto the
-  FP4-MMA kernel); output quality is not the subject of this run.
-- vLLM side is NVFP4A16 (W4A16 / Marlin) - 4-bit weights, 16-bit activations;
-  llama side is NVFP4 weights on FP4-MMA. Both are NVFP4-weight class.
-- One llama request per run hit an intermittent HTTP 500 ("output does not match
-  the expected Content-only format" - a Qwen3 thinking-output quirk on
-  `/completion`), so llama counts were 15/16 and 31/32. The failed request returns
-  early and reduces batch contention for the rest, so a clean 16/16 / 32/32 llama
-  run would be marginally slower - i.e. the ~6x gap reported here is conservative
-  (favorable to llama.cpp).
-- Both servers cold-started; numbers are end-to-end wall from the concurrent
-  client. Disk healthy (~325 GB free), GPU otherwise idle.

backend/cpp/llama-cpp/patches/paged/PAGED_VLLM_COMPARE.md

@@ -1,165 +0,0 @@
-# Paged-attention closing measurements: stock GPU determinism + vLLM comparison
-
-Two closing measurements for the paged-attention series, run on a DGX Spark
-(NVIDIA GB10, compute capability 12.1 / sm_121), CUDA 13. Dev tree
-`~/llama-paged-dev` branch `paged`, paged engine gated by env `LLAMA_KV_PAGED`
-(default-off = stock). Models: `Qwen3-0.6B-Q8_0.gguf` and
-`Qwen3-32B-Q4_K_M.gguf` (llama.cpp), `Qwen3-32B` nvfp4a16 / W4A16 HF safetensors
-(vLLM 0.23.0). All dev drivers are dev-tree-only and not shipped.
-
-## Deliverable 1: stock GPU determinism across batch shapes (no paging)
-
-Question: is the patch-0007 GPU byte-identity "failure" (a near-tie greedy token
-flips on CUDA, e.g. 17971 vs 5671) caused by paging, or is it inherent stock
-CUDA non-determinism from running the same tokens in a different batch shape?
-
-Method: a new dev-only driver `llama-paged-batchshape` (paging explicitly OFF:
-`unsetenv("LLAMA_KV_PAGED")`). For a prompt `[P+S]` it greedy-decodes two ways,
-both stock contiguous KV:
-
-- (a) `full`  - prefill the whole `[P+S]` in ONE `llama_decode`.
-- (b) `split` - prefill `P` in one `llama_decode`, then `S` in a second.
-
-The two paths write byte-for-identical token ids; the only difference is the
-batch shape submitted to the kernels (full prefill vs P-then-S), which changes
-the float reduction order in the GEMMs and therefore the KV values by tiny
-amounts. 5 distinct prompts, suffix S=16.
-
-### Single next token (the literal T_full vs T_split)
-
-Both CPU and CUDA returned the SAME greedy next token for all 5 prompts
-(0/5 flips). BUT the top-2 logit gap measurably changes with the batch shape on
-CUDA, proving the float order does differ:
-
-```
-CUDA, S=8:  prompt 1  T_full=1896 (gap 0.07072)   T_split=1896 (gap 0.17986)
-CUDA, S=8:  prompt 4  T_full=49584 (gap 0.93304)  T_split=49584 (gap 0.85785)
-```
-
-The argmax simply did not flip on the immediate next token for these prompts -
-the gaps, while shifting, stayed wide enough.
-
-### Generated stream (what 0007 actually byte-asserts)
-
-0007 asserts byte-identity over a *generated* token stream, where the tiny
-prefill-shape KV perturbation accumulates and eventually crosses a near-tie.
-Generating G tokens greedily from `full` vs `split` and reporting first
-divergence:
-
-| gen length | CPU diverged | CUDA diverged |
-|-----------|--------------|---------------|
-| G=24 (0007 default) | 1/5 (prompt 0 @ step 5) | 2/5 (prompt 1 @ step 3, prompt 4 @ step 6) |
-| G=64 | 2/5 (steps 5, 42) | 3/5 (steps 3, 6, 30) |
-
-Example CUDA divergence, pure stock, zero paging:
-`prompt 1: DIVERGES at gen step 3: full=1260 split=576`.
-
-### Verdict (Deliverable 1): HYPOTHESIS HELD
-
-The 0007 GPU byte-identity failure is **stock batch-shape non-determinism, not a
-paged bug**. With paging entirely OFF, stock llama.cpp produces a different
-greedy token stream when the same prompt is processed in a full-prefill batch vs
-a split (prefix-then-suffix) batch - exactly the shape difference that 0007's
-prefix-share path introduces (full B-from-scratch vs prefix-cached + suffix-only).
-
-Refinement (reported honestly): it is **not strictly CUDA-only**. CPU exhibits
-the same divergence, just less often and later (1/5 vs 2/5 at G=24, and CPU's
-flips land at later generation steps). This is exactly why 0007's small, short
-CPU scenarios happened to pass 16/16 while the CUDA run flipped: CUDA's larger
-parallel reductions reorder more aggressively, so a near-tie crosses earlier and
-more frequently. The phenomenon is floating-point GEMM-batching non-determinism,
-inherent to both backends; paging is not the cause.
-
-## Deliverable 2: vLLM vs llama.cpp+paged on a shared-prefix fan-out
-
-Workload: K requests share a 1024-token system prefix, each with a unique
-32-token suffix, then generate 64 tokens. Both engines cache the shared prefix
-(vLLM automatic prefix caching ON by default; llama.cpp via the paged
-cross-request prefix cache, `LLAMA_KV_PAGED=1`).
-
-Quant is the realistic apples-to-oranges, reported honestly:
-- llama.cpp: Qwen3-32B **Q4_K_M** (GGUF), `-ngl 99`, CUDA dequant kernels.
-- vLLM: Qwen3-32B **nvfp4a16 (W4A16)**, served via the **Marlin FP4
-  weight-only** kernel because GB10 (sm_121) has **no native FP4 compute** -
-  i.e. vLLM is on a slower-than-ideal kernel path here. vLLM also ran
-  `enforce_eager=True` (no CUDA graphs / torch.compile; the env lacked a working
-  inductor/ninja toolchain), so the vLLM numbers are if anything **conservative**.
-
-### vLLM (automatic prefix caching), end-to-end
-
-APC hits confirmed in the engine log: **"Prefix cache hit rate: 97.0%"**,
-`prefix_cache_hits 33040/34848` (K=16) and `99344/102432` (K=32).
-
-| K | APC | prefill wall (G=1) | total wall (G=64) | throughput |
-|---|-----|--------------------|--------------------|-----------|
-| 16 | ON  | 0.749 s | 6.63 s | 2.41 req/s |
-| 16 | OFF | 20.19 s | 27.21 s | 0.59 req/s |
-| 32 | ON  | 1.13 s  | 7.56 s | 4.23 req/s |
-| 32 | OFF | 40.19 s | 48.71 s | 0.66 req/s |
-
-vLLM's APC cuts the fan-out prefill ~27x (K=16) to ~36x (K=32) vs APC-off; the
-huge ratio reflects how slow the FP4-emulation prefill is when forced to
-recompute all K prefixes.
-
-### llama.cpp + paged prefix cache (prefill phase)
-
-The paged shared-prefix bench (`llama-paged-prefix-bench`, `BENCH_GEN=0`,
-`PAGED_NGL=99`). Reuse confirmed: `kshare(seq1)=1024`, shared-block
-`ref_cnt = K` (all sequences hold the one prefix), 15360 / 31744 prefix tokens
-skipped.
-
-| K | mode | prefill tokens submitted | prefill wall | vs no-share |
-|---|------|--------------------------|--------------|-------------|
-| 16 | PAGED-SHARE | 1536  | 3.66 s  | 7.15x |
-| 16 | NO-SHARE    | 16896 | 26.17 s | 1.0x  |
-| 32 | PAGED-SHARE | 2048  | 6.04 s  | 10.3x |
-| 32 | NO-SHARE    | 33792 | 62.17 s | 1.0x  |
-
-The paged prefix cache delivers the expected **7.15x (K=16) / 10.3x (K=32)**
-prefill wall-time reduction - the headline cross-request prefix-skip win, on a
-real 32B model on GPU.
-
-### Head-to-head, both engines caching the shared prefix
-
-Prefill of the cached fan-out (vLLM G=1, ~prefill; llama.cpp G=0, pure prefill):
-
-| K | llama.cpp+paged prefill | vLLM APC prefill | vLLM faster by |
-|---|-------------------------|------------------|----------------|
-| 16 | 3.66 s | 0.749 s | ~4.9x |
-| 32 | 6.04 s | 1.13 s  | ~5.3x |
-
-### Verdict (Deliverable 2): competitive in kind, behind in absolute terms
-
-With both engines caching the shared prefix, **llama.cpp+paged is qualitatively
-competitive but absolutely behind vLLM on this GB10 box**:
-
-- **Same optimization, same order of magnitude.** llama.cpp's paged prefix cache
-  reproduces exactly the win vLLM's APC gives - skip the shared-prefix recompute
-  - and yields a 7-10x prefill reduction vs its own no-share baseline. On the
-  RAG/system-prompt fan-out the algorithmic gap is closed: llama.cpp no longer
-  pays K x prefix.
-
-- **vLLM still wins head-to-head by ~5x on the cached prefill** (0.75s vs 3.66s
-  at K=16; 1.13s vs 6.04s at K=32), and by more end-to-end because it does
-  **continuous batched decode** (all K sequences decoded in one fused step)
-  while the llama.cpp paged *dev driver* decodes each sequence serially. That
-  decode-batching gap is a property of the serving stack, not of the paged
-  prefix cache. Notably vLLM wins here while handicapped (eager mode, FP4
-  weight-only emulation with no native FP4 on GB10); a tuned vLLM would lead by
-  more.
-
-- **Honest caveats / blockers.** (1) Quant differs (Q4_K_M vs nvfp4a16). (2) The
-  comparison is prefill-vs-prefill plus vLLM end-to-end; a clean llama.cpp
-  end-to-end on this driver is blocked because its generation phase has a
-  stale-logits bug (`get_logits_ith` reads seq 0's prefill index after later
-  sequences' prefills overwrote the logits buffer -> segfault), and even fixed
-  its decode is serial, so it would not be apples-to-apples vs vLLM's batched
-  decode. The fair end-to-end llama.cpp number needs the grpc / llama-server
-  continuous-batching path, not this dev scaffold. (3) vLLM ran eager + FP4
-  emulation, making its numbers conservative.
-
-Bottom line: paged gives llama.cpp the cross-request prefix-skip that vLLM's APC
-provides, which is the categorical win and removes the K x prefix penalty on
-RAG/system-prompt fan-out. On absolute wall-time on this hardware vLLM retains a
-~5x prefill lead and a larger end-to-end lead from continuous batched decode and
-a more optimized serving stack.

backend/cpp/llama-cpp/patches/paged/SERVER_SWEEP.md

@@ -1,138 +0,0 @@
-# GB10 same-day head-to-head server sweep: llama-server (paged) vs vLLM
-
-Date: 2026-06-23. Hardware: GB10 / DGX Spark (sm_121, 128 GB LPDDR5x unified, ~273 GB/s
-weight-read floor). GPU otherwise idle (sibling vLLM had exited; LocalAI docker workers
-stopped for the run).
-
-This sweep **replaces** the stale carried "~75-80% of vLLM" figure (commit 07985ba4,
-pre-co-batching, single-point). It measures *real serving* steady-state aggregate decode
-throughput across the full concurrency curve, for three model classes, with one identical
-client driving both engines.
-
-## Method
-
-- **llama**: `llama-server` from the paged dev tree (`~/llama-paged-dev/build-cuda`, HEAD =
-  patch 0013 / commit 17d97cb), `LLAMA_KV_PAGED=1`, `-fa on -ngl 999 --parallel 128 -c 65536`.
-- **vLLM**: 0.23.0, `vllm serve --enforce-eager --enable-prefix-caching --max-num-seqs >=128
-  --max-model-len 4096` (APC on, eager per the GB10 no-CUDA-graphs edge).
-- **Client** (`sweep_client2.py`): N concurrent **non-streaming** `/v1/completions`, short
-  shared prompt, `max_tokens=min_tokens=256`, `ignore_eos=true`. Aggregate decode tok/s =
-  total generated tokens / wall. Non-streaming keeps the Python client off the critical path
-  (one JSON parse per request, not per token), so the **server** is the bottleneck. Validated:
-  vLLM pushed 4227 tok/s through the exact same client where llama topped out at 2087, so the
-  client is not the cap. Both engines use the identical client + prompt -> apples-to-apples.
-- npl (concurrency) sweep: 8 / 32 / 64 / 128.
-
-Quant parity:
-- Dense: llama **NVFP4-dense GGUF** (weight-only FP4, 16-bit compute) vs vLLM **NVFP4A16**
-  (weight FP4, 16-bit activation) -> matched precision class.
-- Small: llama **Q8_0** vs vLLM **bf16** (closest loadable form).
-- MoE: llama **mxfp4** GGUF. **vLLM could not serve this MoE on GB10 at all** (see below), so
-  there is no vLLM MoE column.
-
-## Results: aggregate decode tok/s (higher is better)
-
-### Dense 32B  (llama NVFP4-dense  vs  vLLM NVFP4A16)
-
-| npl | llama (NVFP4) | vLLM (NVFP4A16) | llama % of vLLM |
-|----:|--------------:|----------------:|----------------:|
-|   8 |          83.2 |            85.9 |          **96.9%** |
-|  32 |         228.9 |           301.3 |          76.0%  |
-|  64 |         367.1 |           507.8 |          72.3%  |
-| 128 |         520.6 |           604.0 |          86.2%  |
-
-Plateau: neither has plateaued at 128 (both still climbing, weight-read bound). llama is at
-**parity at batch-8** (97%), dips to ~72% mid-curve (npl 32-64), recovers to 86% at 128.
-
-### Small  Qwen3-0.6B  (llama Q8_0  vs  vLLM bf16)
-
-| npl | llama (Q8_0) | vLLM (bf16) | llama % of vLLM |
-|----:|-------------:|------------:|----------------:|
-|   8 |        911.3 |       923.0 |        **98.7%** |
-|  32 |       1701.6 |      2531.4 |        67.2%  |
-|  64 |       1911.7 |      3497.1 |        54.7%  |
-| 128 |       2087.6 |      4227.6 |        49.4%  |
-
-Plateau: **llama plateaus hard** at ~2.0-2.1k by npl 64-128 (+9% from 64->128). vLLM keeps
-scaling (3497 -> 4227). For a tiny runtime-bound model, vLLM's scheduler/batching amortizes
-better; llama-server's per-token host cost (sampling, detok, slot mgmt) caps it. This is the
-worst llama-vs-vLLM ratio in the sweep (down to 49%).
-
-### MoE  Qwen3-Coder-30B-A3B  (llama mxfp4; vLLM = NOT SERVABLE on GB10)
-
-| npl | llama (mxfp4) | vLLM |
-|----:|--------------:|-----:|
-|   8 |         290.0 |  n/a |
-|  32 |         582.5 |  n/a |
-|  64 |         931.8 |  n/a |
-| 128 |        1041.3 |  n/a |
-
-llama-server scales cleanly to **1041 tok/s** at npl 128 with **no npl-128 expert-activation
-cliff** (unlike the prior `llama-batched-bench` MoE numbers 253/505/830/620 that peaked at 64;
-short-prompt continuous batching in the server avoids it).
-
-**vLLM could not serve this MoE on GB10 (two independent failures):**
-1. **bf16** (`Qwen/Qwen3-Coder-30B-A3B-Instruct`, the only HF form on the box): loads the
-   56.9 GB of weights, then **hangs at the MoE warmup** (`Using MoEPrepareAndFinalize
-   NoDPEPModular` -> `Model loading took ...`), GPU 0% util, and **takes the whole box down
-   (hard reboot)**. Reproduced twice. With tight `--gpu-memory-utilization` it still hangs at
-   the same step before the API server ever comes up.
-2. **mxfp4 GGUF** (same weights llama uses): vLLM 0.23.0's GGUF loader **cannot map the fused
-   qwen3moe expert tensors** (`RuntimeError: Failed to map GGUF parameters (48):
-   ['model.layers.N.mlp.experts.gate_up_proj', ...]`). Engine init fails outright.
-
-So on GB10, llama.cpp is the *only* engine of the two that serves this 30B-A3B MoE at all -
-an availability win, independent of throughput.
-
-## Batch-8 anomaly triage (dense NVFP4) -- RESOLVED
-
-The prior mixed-load run reported llama batch-8 steady decode at **471 ms/step (~19% of vLLM
-aggregate, ~17 tok/s)**. This sweep does **not** reproduce it. Clean isolated batch-8 decode:
-
-- `llama-server` batch-8 dense paged = **83.2 tok/s** aggregate = ~96 ms/step = **96.9% of
-  vLLM's 85.9** (parity, both at the LPDDR5x weight-read floor).
-
-`llama-batched-bench` cross-check, dense NVFP4, `-npp 16 -ntg 128 -npl 1,8`, the three
-hypotheses isolated (S_TG = decode tok/s aggregate at batch 8):
-
-| config                | batch-8 S_TG t/s | ms/decode-step |
-|-----------------------|-----------------:|---------------:|
-| paged,  ctx 65536     |            90.32 |          88.6  |
-| stock,  ctx 65536     |            88.39 |          90.5  |
-| paged,  ctx 163840    |            89.33 |          89.6  |
-| stock,  ctx 163840    |            87.72 |          91.2  |
-
-Conclusion: clean batch-8 dense decode is **~88-90 tok/s (~89 ms/step) regardless of all three
-suspects**:
-- **Paged overhead?** No -- paged is within 2% of stock, and at ctx 65k paged is *faster*
-  (90.3 vs 88.4). The decode path is not paying a paged penalty at batch-8.
-- **The 163840-token ctx allocation?** No -- ctx 163840 == ctx 65536 within 1% (89.3 vs 90.3).
-  The large allocation does not slow steady-state decode.
-- **NVFP4 decode cost?** This *is* the cost -- ~89 ms/step is the GB10 weight-read floor for a
-  32B at batch-8 (it matches vLLM's 86 tok/s server and exceeds it at the kernel level: 90 vs
-  86). It is the hardware ceiling, not a bug.
-
-The 471 ms/step is ~5.3x slower than this clean floor and is explained by none of the three.
-It was a **mixed-load artifact**: the 8 decoders were time-sharing the GPU with a concurrent
-prefill (a large prompt / chunked prefill landing on the same steps). That decode-vs-prefill
-contention is exactly the stall **patch 0013 (`LLAMA_PREFILL_BUDGET`)** bounds. In steady-state
-isolated decode, batch-8 dense is at **parity with vLLM (97%)**, not 19%.
-
-## Aggregate map (replaces the carried 75-80%)
-
-llama-server (paged) as a fraction of vLLM, by regime:
-
-- **Low concurrency (batch-8): parity, 97-99%** on both measurable classes. Both engines sit on
-  the LPDDR5x weight-read floor; there is nothing to win.
-- **Dense 32B, mid-to-high concurrency: 72-86%.** Dips to ~72% at npl 32-64, recovers to 86% at
-  128. Both still climbing (weight-bound), neither plateaus by 128.
-- **Small 0.6B, mid-to-high concurrency: 49-67%.** llama plateaus ~2.0k; vLLM scales to 4.2k.
-  Runtime/scheduler-bound regime -- vLLM's batching wins; this is llama's weakest ratio.
-- **MoE 30B-A3B: llama-only.** vLLM cannot serve it on GB10 (bf16 reboots the box at MoE
-  warmup; GGUF expert tensors unmappable). llama serves it at 290 -> 1041 tok/s, scaling
-  cleanly with no npl-128 cliff.
-
-Net: the single "75-80%" number is replaced by a curve. It is *roughly* right only for the
-dense mid-band; it is too optimistic for the small model at high concurrency (49%) and moot for
-MoE (where llama is the only option). The headline is parity at low concurrency and a hardware
-(not engine) ceiling on dense decode.

backend/cpp/llama-cpp/prepare.sh

@@ -2,30 +2,12 @@
 
 ## Patches
 
-## Apply patches: the base `patches/` series, then the gated `patches/paged/`
+## Apply patches from the `patches` directory
-## series (default on; LLAMA_PAGED=off skips it). Only *.patch files are applied
-## (docs/dirs like patches/paged/ and *.md are skipped). The Makefile `llama.cpp`
-## target already `git apply`s these at checkout, so each apply is guarded by a
-## sentinel and skipped when already present - re-applying git-format patches with
-## `patch` fuzzily duplicates hunks (redefinition errors). This block only does
-## real work if prepare.sh is run against an unpatched checkout.
 if [ -d "patches" ]; then
-    for patch in patches/*.patch; do
+    for patch in $(ls patches); do
-        [ -e "$patch" ] || continue
         echo "Applying patch $patch"
-        patch -d llama.cpp/ -p1 -N -r - < "$patch" || true
+        patch -d llama.cpp/ -p1 < patches/$patch
-    done
+    done 
-    if [ "${LLAMA_PAGED:-on}" != "off" ] && [ -d "patches/paged" ]; then
-        if [ -f llama.cpp/src/paged-kv-manager.cpp ]; then
-            echo "paged-attention patch series already applied (sentinel present) - skipping re-apply"
-        else
-            for patch in patches/paged/*.patch; do
-                [ -e "$patch" ] || continue
-                echo "Applying paged patch $patch"
-                patch -d llama.cpp/ -p1 -N -r - < "$patch" || true
-            done
-        fi
-    fi
 fi
 
 set -e

backend/cpp/privacy-filter/Makefile

@@ -8,7 +8,7 @@
 # Local development: point at a working checkout instead of cloning, e.g.
 #   make PRIVACY_FILTER_SRC=$HOME/c/privacy-filter.cpp grpc-server
 
-PRIVACY_FILTER_VERSION?=646342f7a59c6b7d195185eac60bad762e572f1d
+PRIVACY_FILTER_VERSION?=98f52c5ef2250f207cc6b9a6aef05393a120cb7c
 PRIVACY_FILTER_REPO?=https://github.com/localai-org/privacy-filter.cpp
 PRIVACY_FILTER_SRC?=
 

backend/go/ced/.gitignore

@@ -0,0 +1,11 @@
+.cache/
+sources/
+build/
+package/
+ced-grpc
+# build artifacts staged in-tree by the Makefile (cp from sources/) or
+# symlinked for local dev; the real sources live in ced.cpp upstream.
+*.so
+*.so.*
+ced_capi.h
+compile_commands.json

backend/go/ced/Makefile

@@ -0,0 +1,77 @@
+# ced sound-classification backend Makefile.
+#
+# Upstream pin lives below as CED_VERSION?=<sha> so .github/bump_deps.sh can find
+# and update it (matches the parakeet-cpp / whisper.cpp convention).
+#
+# Local dev shortcut: symlink an out-of-tree ced.cpp shared build + header and
+# skip the clone/cmake steps entirely:
+#   ln -sf /path/to/ced.cpp/build-shared/libced.so .
+#   ln -sf /path/to/ced.cpp/include/ced_capi.h .
+#   go build -o ced-grpc .
+
+CED_VERSION?=c04ac14b7992d00584d9e812c9bb6268598a6ce7
+CED_REPO?=https://github.com/mudler/ced.cpp
+
+GOCMD?=go
+GO_TAGS?=
+JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
+
+BUILD_TYPE?=
+NATIVE?=false
+
+# Static-link ggml into libced.so (PIC) so the shared lib is self-contained:
+# dlopen needs no libggml*.so alongside it, only system libs the runtime image
+# already provides.
+CMAKE_ARGS?=-DCMAKE_BUILD_TYPE=Release -DCED_SHARED=ON -DCED_BUILD_CLI=OFF -DCED_BUILD_TESTS=OFF -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON
+
+ifeq ($(NATIVE),false)
+	CMAKE_ARGS+=-DGGML_NATIVE=OFF
+endif
+
+# ced.cpp gates its ggml backends behind CED_GGML_* options (set(... CACHE BOOL
+# "" FORCE)), so forward those instead of a bare -DGGML_CUDA=ON.
+ifeq ($(BUILD_TYPE),cublas)
+	CMAKE_ARGS+=-DCED_GGML_CUDA=ON -DGGML_CUDA_GRAPHS=ON
+else ifeq ($(BUILD_TYPE),openblas)
+	CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
+else ifeq ($(BUILD_TYPE),hipblas)
+	CMAKE_ARGS+=-DCED_GGML_HIP=ON
+else ifeq ($(BUILD_TYPE),vulkan)
+	CMAKE_ARGS+=-DCED_GGML_VULKAN=ON
+endif
+
+.PHONY: ced-grpc package build clean purge test all
+
+all: ced-grpc
+
+sources/ced.cpp:
+	mkdir -p sources/ced.cpp
+	cd sources/ced.cpp && \
+	git init -q && \
+	git remote add origin $(CED_REPO) && \
+	git fetch --depth 1 origin $(CED_VERSION) && \
+	git checkout FETCH_HEAD && \
+	git submodule update --init --recursive --depth 1 --single-branch
+
+libced.so: sources/ced.cpp
+	cmake -B sources/ced.cpp/build-shared -S sources/ced.cpp $(CMAKE_ARGS)
+	cmake --build sources/ced.cpp/build-shared --config Release -j$(JOBS)
+	cp -fv sources/ced.cpp/build-shared/libced.so* ./ 2>/dev/null || true
+	cp -fv sources/ced.cpp/include/ced_capi.h ./
+
+ced-grpc: libced.so main.go goced.go
+	CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o ced-grpc .
+
+package: ced-grpc
+	bash package.sh
+
+build: package
+
+test:
+	LD_LIBRARY_PATH=$(CURDIR):$$LD_LIBRARY_PATH $(GOCMD) test ./... -count=1
+
+clean: purge
+	rm -rf libced.so* ced_capi.h package ced-grpc
+
+purge:
+	rm -rf sources/ced.cpp

backend/go/ced/goced.go

@@ -0,0 +1,130 @@
+package main
+
+// Go side of the ced backend: purego bindings over ced_capi.h plus the gRPC
+// SoundDetection implementation.
+//
+// SKETCH: the pb.SoundDetection* types come from backend.proto (regenerate with
+// `make protogen-go`). The C side is single-threaded per ctx, so we guard the
+// engine with engineMu; LocalAI also serializes via base.SingleThread.
+import (
+	"context"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"sort"
+	"sync"
+	"unsafe"
+
+	"github.com/mudler/LocalAI/pkg/grpc/base"
+	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
+)
+
+// purego-bound entry points from libced.so. Names match ced_capi.h exactly.
+var (
+	CppAbiVersion       func() int32
+	CppLoad             func(ggufPath string) uintptr
+	CppFree             func(ctx uintptr)
+	CppLastError        func(ctx uintptr) string
+	CppNumClasses       func(ctx uintptr) int32
+	CppSampleRate       func(ctx uintptr) int32
+	CppClassifyPathJSON func(ctx uintptr, wavPath string, topK int32) uintptr
+	CppClassifyPcmJSON  func(ctx uintptr, pcm []float32, nSamples int32, sampleRate int32, topK int32) uintptr
+	CppFreeString       func(s uintptr)
+)
+
+// cstr copies a malloc'd C string (returned as uintptr) into a Go string and
+// frees the original via ced_capi_free_string. Empty/0 -> "".
+func cstr(p uintptr) string {
+	if p == 0 {
+		return ""
+	}
+	defer CppFreeString(p)
+	var b []byte
+	for i := 0; ; i++ {
+		ch := *(*byte)(unsafe.Pointer(p + uintptr(i))) //nolint:govet // #nosec G103 -- C-owned NUL-terminated string from libced (not Go-GC memory)
+		if ch == 0 {
+			break
+		}
+		b = append(b, ch)
+	}
+	return string(b)
+}
+
+// Ced is the gRPC backend. One loaded CED model per instance.
+type Ced struct {
+	base.Base
+	ctxPtr   uintptr
+	engineMu sync.Mutex
+}
+
+// Load resolves the GGUF and opens the C-API context.
+func (c *Ced) Load(opts *pb.ModelOptions) error {
+	if opts.ModelFile == "" {
+		return errors.New("ced: ModelFile is required")
+	}
+	ctx := CppLoad(opts.ModelFile)
+	if ctx == 0 {
+		return fmt.Errorf("ced: ced_capi_load failed for %q: %s", opts.ModelFile, CppLastError(0))
+	}
+	c.ctxPtr = ctx
+	return nil
+}
+
+// jsonTag mirrors the ced_capi JSON tag objects.
+type jsonTag struct {
+	Index int     `json:"index"`
+	Score float32 `json:"score"`
+	Label string  `json:"label"`
+}
+
+// SoundDetection classifies the clip at req.Src and returns scored AudioSet tags.
+func (c *Ced) SoundDetection(ctx context.Context, req *pb.SoundDetectionRequest) (*pb.SoundDetectionResponse, error) {
+	if c.ctxPtr == 0 {
+		return nil, errors.New("ced: model not loaded")
+	}
+	if req.GetSrc() == "" {
+		return nil, errors.New("ced: SoundDetectionRequest.src (audio path) is required")
+	}
+	topK := req.GetTopK()
+	if topK <= 0 {
+		topK = 10 // sensible default for a tagging response
+	}
+
+	c.engineMu.Lock()
+	out := cstr(CppClassifyPathJSON(c.ctxPtr, req.GetSrc(), topK))
+	lastErr := CppLastError(c.ctxPtr)
+	c.engineMu.Unlock()
+
+	if out == "" {
+		return nil, fmt.Errorf("ced: classification failed: %s", lastErr)
+	}
+	var tags []jsonTag
+	if err := json.Unmarshal([]byte(out), &tags); err != nil {
+		return nil, fmt.Errorf("ced: bad classifier JSON: %w", err)
+	}
+
+	thr := req.GetThreshold()
+	resp := &pb.SoundDetectionResponse{}
+	for _, t := range tags {
+		if t.Score < thr {
+			continue
+		}
+		resp.Detections = append(resp.Detections, &pb.SoundClass{
+			Label: t.Label, Score: t.Score, Index: int32(t.Index),
+		})
+	}
+	sort.Slice(resp.Detections, func(i, j int) bool {
+		return resp.Detections[i].Score > resp.Detections[j].Score
+	})
+	return resp, nil
+}
+
+func (c *Ced) Free() error {
+	c.engineMu.Lock()
+	defer c.engineMu.Unlock()
+	if c.ctxPtr != 0 {
+		CppFree(c.ctxPtr)
+		c.ctxPtr = 0
+	}
+	return nil
+}

backend/go/ced/main.go

@@ -0,0 +1,59 @@
+package main
+
+// ced sound-classification backend. Started internally by LocalAI: one gRPC
+// server per loaded model. Loads libced.so via purego and registers the flat
+// C-API declared in ced_capi.h. The library name can be overridden with
+// CED_LIBRARY (mirrors PARAKEET_LIBRARY / WHISPER_LIBRARY); the default looks
+// for the .so next to this binary.
+//
+// SKETCH: requires `make protogen-go` after the backend.proto SoundDetection
+// addition, and a built libced.so (see Makefile). See DESIGN.md.
+import (
+	"flag"
+	"fmt"
+	"os"
+
+	"github.com/ebitengine/purego"
+	grpc "github.com/mudler/LocalAI/pkg/grpc"
+)
+
+var addr = flag.String("addr", "localhost:50051", "the address to connect to")
+
+type libFunc struct {
+	ptr  any
+	name string
+}
+
+func main() {
+	libName := os.Getenv("CED_LIBRARY")
+	if libName == "" {
+		libName = "libced.so"
+	}
+	lib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
+	if err != nil {
+		panic(fmt.Errorf("ced: dlopen %q: %w", libName, err))
+	}
+
+	// Bound 1:1 to ced_capi.h. char*-returning functions are declared uintptr
+	// so we can free the same pointer with ced_capi_free_string after copying
+	// (purego's string return would copy and leak the original).
+	for _, lf := range []libFunc{
+		{&CppAbiVersion, "ced_capi_abi_version"},
+		{&CppLoad, "ced_capi_load"},
+		{&CppFree, "ced_capi_free"},
+		{&CppLastError, "ced_capi_last_error"},
+		{&CppNumClasses, "ced_capi_num_classes"},
+		{&CppSampleRate, "ced_capi_sample_rate"},
+		{&CppClassifyPathJSON, "ced_capi_classify_path_json"},
+		{&CppClassifyPcmJSON, "ced_capi_classify_pcm_json"},
+		{&CppFreeString, "ced_capi_free_string"},
+	} {
+		purego.RegisterLibFunc(lf.ptr, lib, lf.name)
+	}
+
+	fmt.Fprintf(os.Stderr, "[ced] ABI=%d\n", CppAbiVersion())
+	flag.Parse()
+	if err := grpc.StartServer(*addr, &Ced{}); err != nil {
+		panic(err)
+	}
+}

backend/go/ced/package.sh

@@ -0,0 +1,60 @@
+#!/bin/bash
+#
+# Bundle the ced-grpc binary, libced.so, the core runtime libs (libc/libstdc++/
+# libgomp + ld.so) and the GPU runtime for the active BUILD_TYPE so the package
+# is self-contained. Mirrors backend/go/parakeet-cpp/package.sh; run.sh routes
+# the (CGO_ENABLED=0) binary through lib/ld.so so the packaged libc is used.
+
+set -e
+
+CURDIR=$(dirname "$(realpath "$0")")
+REPO_ROOT="${CURDIR}/../../.."
+
+mkdir -p "$CURDIR/package/lib"
+
+cp -avf "$CURDIR/ced-grpc" "$CURDIR/package/"
+cp -avf "$CURDIR/run.sh" "$CURDIR/package/"
+
+cp -avf "$CURDIR"/libced.so* "$CURDIR/package/lib/" 2>/dev/null || {
+	echo "ERROR: libced.so not found in $CURDIR, run 'make' first" >&2
+	exit 1
+}
+
+if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
+    echo "Detected x86_64 architecture, copying x86_64 libraries..."
+    cp -arfLv /lib64/ld-linux-x86-64.so.2 "$CURDIR/package/lib/ld.so"
+    cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
+    cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
+elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
+    echo "Detected ARM64 architecture, copying ARM64 libraries..."
+    cp -arfLv /lib/ld-linux-aarch64.so.1 "$CURDIR/package/lib/ld.so"
+    cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
+    cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
+elif [ "$(uname -s)" = "Darwin" ]; then
+    echo "Detected Darwin"
+else
+    echo "Error: Could not detect architecture"
+    exit 1
+fi
+
+GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
+if [ -f "$GPU_LIB_SCRIPT" ]; then
+    echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
+    source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
+    package_gpu_libs
+fi
+
+echo "Packaging completed successfully"
+ls -liah "$CURDIR/package/" "$CURDIR/package/lib/"

backend/go/ced/run.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+set -e
+
+CURDIR=$(dirname "$(realpath "$0")")
+
+export LD_LIBRARY_PATH="$CURDIR/lib:$CURDIR:${LD_LIBRARY_PATH:-}"
+
+# If a self-contained ld.so was packaged, route through it so the packaged
+# libc / libstdc++ are used instead of the host's (matches the sibling backends).
+if [ -f "$CURDIR/lib/ld.so" ]; then
+	echo "Using lib/ld.so"
+	exec "$CURDIR/lib/ld.so" "$CURDIR/ced-grpc" "$@"
+fi
+
+exec "$CURDIR/ced-grpc" "$@"

backend/go/crispasr/Makefile

@@ -67,7 +67,7 @@ sources/CrispASR:
 	# it, so ${CMAKE_SOURCE_DIR} is THIS backend dir and the talk-llama sources
 	# aren't found. Rewrite to ${PROJECT_SOURCE_DIR} (the crispasr project root),
 	# which is correct both standalone and as a subproject. Idempotent.
-	sed -i 's#\$${CMAKE_SOURCE_DIR}/examples/talk-llama#\$${PROJECT_SOURCE_DIR}/examples/talk-llama#' sources/CrispASR/src/CMakeLists.txt
+	sed -i.bak 's#\$${CMAKE_SOURCE_DIR}/examples/talk-llama#\$${PROJECT_SOURCE_DIR}/examples/talk-llama#' sources/CrispASR/src/CMakeLists.txt && rm -f sources/CrispASR/src/CMakeLists.txt.bak
 
 # Detect OS
 UNAME_S := $(shell uname -s)

backend/go/crispasr/cpp/crispasr_shim.cpp

@@ -47,6 +47,74 @@ extern "C" void set_abort(int v) {
   g_abort.store(v, std::memory_order_relaxed);
 }
 
+// --- word-level timestamp accessors ---
+extern "C" {
+int crispasr_session_result_n_words(crispasr_session_result *r, int seg_i);
+const char *crispasr_session_result_word_text(crispasr_session_result *r,
+                                               int seg_i, int word_i);
+int64_t crispasr_session_result_word_t0(crispasr_session_result *r, int seg_i,
+                                         int word_i);
+int64_t crispasr_session_result_word_t1(crispasr_session_result *r, int seg_i,
+                                         int word_i);
+
+// Parakeet-specific word accessors
+int crispasr_parakeet_result_n_words(void *r);
+const char *crispasr_parakeet_result_word_text(void *r, int word_i);
+int64_t crispasr_parakeet_result_word_t0(void *r, int word_i);
+int64_t crispasr_parakeet_result_word_t1(void *r, int word_i);
+}
+
+void *get_result(void) { return g_result; }
+
+int get_word_count(int seg_i) {
+  if (!g_result)
+    return 0;
+  return crispasr_session_result_n_words(g_result, seg_i);
+}
+
+const char *get_word_text(int seg_i, int word_i) {
+  if (!g_result)
+    return "";
+  return crispasr_session_result_word_text(g_result, seg_i, word_i);
+}
+
+int64_t get_word_t0(int seg_i, int word_i) {
+  if (!g_result)
+    return 0;
+  return crispasr_session_result_word_t0(g_result, seg_i, word_i);
+}
+
+int64_t get_word_t1(int seg_i, int word_i) {
+  if (!g_result)
+    return 0;
+  return crispasr_session_result_word_t1(g_result, seg_i, word_i);
+}
+
+// Parakeet-specific word accessors
+int get_parakeet_word_count(void) {
+  if (!g_result)
+    return 0;
+  return crispasr_parakeet_result_n_words(g_result);
+}
+
+const char *get_parakeet_word_text(int word_i) {
+  if (!g_result)
+    return "";
+  return crispasr_parakeet_result_word_text(g_result, word_i);
+}
+
+int64_t get_parakeet_word_t0(int word_i) {
+  if (!g_result)
+    return 0;
+  return crispasr_parakeet_result_word_t0(g_result, word_i);
+}
+
+int64_t get_parakeet_word_t1(int word_i) {
+  if (!g_result)
+    return 0;
+  return crispasr_parakeet_result_word_t1(g_result, word_i);
+}
+
 static void ggml_log_cb(enum ggml_log_level level, const char *log,
                         void *data) {
   const char *level_str;

backend/go/crispasr/cpp/crispasr_shim.h

@@ -20,4 +20,18 @@ float *tts_synthesize(const char *text, int *out_n_samples); // 24kHz mono float
 void tts_free(float *pcm);
 int tts_set_voice(const char *name); // best-effort speaker selection; 0 ok
 int tts_set_voice_file(const char *path, const char *ref_text); // load voice pack (.gguf) or zero-shot clone (.wav + ref_text)
+
+// --- word-level timestamp accessors ---
+// Session-based (works for whisper-like backends)
+void *get_result(void);
+int get_word_count(int seg_i);
+const char *get_word_text(int seg_i, int word_i);
+int64_t get_word_t0(int seg_i, int word_i);
+int64_t get_word_t1(int seg_i, int word_i);
+
+// Parakeet-specific (global word list, no segment index)
+int get_parakeet_word_count(void);
+const char *get_parakeet_word_text(int word_i);
+int64_t get_parakeet_word_t0(int word_i);
+int64_t get_parakeet_word_t1(int word_i);
 }

backend/go/crispasr/gocrispasr.go

@@ -34,6 +34,18 @@ var (
 	CppTTSFree         func(ptr uintptr)
 	CppTTSSetVoice     func(name string) int
 	CppTTSSetVoiceFile func(path string, refText string) int
+
+	// Word-level timestamp accessors (session-based, per-segment)
+	CppGetWordCount func(segI int) int
+	CppGetWordText  func(segI int, wordI int) string
+	CppGetWordT0    func(segI int, wordI int) int64
+	CppGetWordT1    func(segI int, wordI int) int64
+
+	// Parakeet-specific word accessors (global, no segment index)
+	CppGetParakeetWordCount func() int
+	CppGetParakeetWordText  func(wordI int) string
+	CppGetParakeetWordT0    func(wordI int) int64
+	CppGetParakeetWordT1    func(wordI int) int64
 )
 
 type CrispASR struct {
@@ -212,6 +224,28 @@ func (w *CrispASR) VAD(req *pb.VADRequest) (pb.VADResponse, error) {
 	}, nil
 }
 
+// isValidWord reports whether a TranscriptWord contains recognisable speech
+// content. The parakeet-specific word accessors can return stale initialisation
+// data (model name, binary blobs) when a segment has no real speech. A word is
+// considered valid only when:
+//   - the text is non-empty after trimming,
+//   - it contains no U+FFFD replacement characters (from binary data scrubbing),
+//   - both timestamps are non-negative,
+//   - the word has positive duration (end > start).
+func isValidWord(w *pb.TranscriptWord) bool {
+	txt := strings.TrimSpace(w.Text)
+	if txt == "" {
+		return false
+	}
+	if strings.ContainsRune(txt, '\uFFFD') {
+		return false
+	}
+	if w.Start < 0 || w.End < 0 || w.End <= w.Start {
+		return false
+	}
+	return true
+}
+
 func (w *CrispASR) AudioTranscription(ctx context.Context, opts *pb.TranscriptRequest) (pb.TranscriptResult, error) {
 	if err := ctx.Err(); err != nil {
 		return pb.TranscriptResult{}, status.Error(codes.Canceled, "transcription cancelled")
@@ -290,15 +324,54 @@ func (w *CrispASR) AudioTranscription(ctx context.Context, opts *pb.TranscriptRe
 		// IDs, so Tokens is left empty.
 		txt := strings.ToValidUTF8(strings.Clone(CppGetSegmentText(i)), "<22>")
 
+		// Populate word-level timestamps. Try session-based functions first
+		// (per-segment); fall back to parakeet-specific functions (global word
+		// list with no segment index — only populated on the first segment to
+		// avoid duplication).
+		words := []*pb.TranscriptWord{}
+		wordCount := CppGetWordCount(i)
+		if wordCount == 0 && i == 0 {
+			wordCount = CppGetParakeetWordCount()
+			for j := 0; j < wordCount; j++ {
+				w := &pb.TranscriptWord{
+					Start: CppGetParakeetWordT0(j) * (10000000),
+					End:   CppGetParakeetWordT1(j) * (10000000),
+					Text:  strings.ToValidUTF8(strings.Clone(CppGetParakeetWordText(j)), "<22>"),
+				}
+				if isValidWord(w) {
+					words = append(words, w)
+				}
+			}
+		} else {
+			for j := 0; j < wordCount; j++ {
+				w := &pb.TranscriptWord{
+					Start: CppGetWordT0(i, j) * (10000000),
+					End:   CppGetWordT1(i, j) * (10000000),
+					Text:  strings.ToValidUTF8(strings.Clone(CppGetWordText(i, j)), "<22>"),
+				}
+				if isValidWord(w) {
+					words = append(words, w)
+				}
+			}
+		}
+
+		// Skip empty segments with no recognisable content (e.g. trailing
+		// silence segments that parakeet emits with stale init data).
+		trimmed := strings.TrimSpace(txt)
+		if trimmed == "" && len(words) == 0 {
+			continue
+		}
+
 		segment := &pb.TranscriptSegment{
 			Id:    int32(i),
 			Text:  txt,
 			Start: s, End: t,
+			Words: words,
 		}
 
 		segments = append(segments, segment)
 
-		text += " " + strings.TrimSpace(txt)
+		text += " " + trimmed
 	}
 
 	return pb.TranscriptResult{
@@ -390,13 +463,20 @@ func (w *CrispASR) AudioTranscriptionStream(ctx context.Context, opts *pb.Transc
 		s := CppGetSegmentStart(i) * 10000000
 		t := CppGetSegmentEnd(i) * 10000000
 		txt := strings.ToValidUTF8(strings.Clone(CppGetSegmentText(i)), "<22>")
+
+		// Skip empty segments (e.g. trailing silence that parakeet emits
+		// with stale init data).
+		trimmed := strings.TrimSpace(txt)
+		if trimmed == "" && s == t {
+			continue
+		}
+
 		segments = append(segments, &pb.TranscriptSegment{
 			Id:    int32(i),
 			Text:  txt,
 			Start: s, End: t,
 		})
 
-		trimmed := strings.TrimSpace(txt)
 		if trimmed == "" {
 			continue
 		}

backend/go/crispasr/main.go

@@ -44,6 +44,14 @@ func main() {
 		{&CppTTSFree, "tts_free"},
 		{&CppTTSSetVoice, "tts_set_voice"},
 		{&CppTTSSetVoiceFile, "tts_set_voice_file"},
+		{&CppGetWordCount, "get_word_count"},
+		{&CppGetWordText, "get_word_text"},
+		{&CppGetWordT0, "get_word_t0"},
+		{&CppGetWordT1, "get_word_t1"},
+		{&CppGetParakeetWordCount, "get_parakeet_word_count"},
+		{&CppGetParakeetWordText, "get_parakeet_word_text"},
+		{&CppGetParakeetWordT0, "get_parakeet_word_t0"},
+		{&CppGetParakeetWordT1, "get_parakeet_word_t1"},
 	}
 
 	for _, lf := range libFuncs {

backend/go/depth-anything-cpp/Makefile

@@ -8,11 +8,13 @@ JOBS?=$(shell nproc --ignore=1)
 
 # depth-anything.cpp. Pin to a specific commit for a stable build; a squash
 # merge upstream can orphan a branch, so the native version is pinned by SHA.
-# This SHA adds the nested two-file metric C-API (abi_version 4,
+# This SHA adds the Depth Anything V2 engine + C-API routing (depth-only,
-# da_capi_load_nested) required by the depth-anything-3-nested gallery model;
+# relative + metric) on top of the nested two-file metric C-API (abi_version 4,
-# tag it (e.g. v0.1.3) upstream to keep the SHA alive.
+# da_capi_load_nested) required by the depth-anything-3-nested gallery model.
+# It is kept alive by the upstream tag da2-support (survives a squash-merge);
+# repoint to the master merge commit once mudler/depth-anything.cpp PR #1 lands.
 DEPTHANYTHING_REPO?=https://github.com/mudler/depth-anything.cpp.git
-DEPTHANYTHING_VERSION?=cce5edc395fd1843806093d7ccc0c8b0d0b97b72
+DEPTHANYTHING_VERSION?=f4e17dea695dd12ae76bea98ba58030996b98118
 
 ifeq ($(NATIVE),false)
 	CMAKE_ARGS+=-DGGML_NATIVE=OFF

backend/go/face-detect/.gitignore

@@ -0,0 +1,18 @@
+# Fetched upstream sources
+sources/
+
+# CMake build directories
+build*/
+
+# build artifacts staged in-tree by the Makefile (cp from sources/) or
+# symlinked for local dev; the real sources live in face-detect.cpp upstream.
+*.so
+*.so.*
+facedetect_capi.h
+compile_commands.json
+
+# Compiled backend binary
+face-detect-grpc
+
+# Packaging output
+package/

backend/go/face-detect/Makefile

@@ -0,0 +1,97 @@
+# face-detect backend Makefile.
+#
+# Upstream pin lives below as FACEDETECT_VERSION?=be22d67... (.github/bump_deps.sh
+# can find and update it - matches the voice-detect / parakeet.cpp / whisper.cpp
+# convention).
+#
+# Local dev shortcut: if you already have an out-of-tree face-detect.cpp build,
+# symlink the .so + header into this directory and skip the clone/cmake steps:
+#
+#   ln -sf /path/to/face-detect.cpp/build-shared/libfacedetect.so .
+#   ln -sf /path/to/face-detect.cpp/include/facedetect_capi.h .
+#   go build -o face-detect-grpc .
+#
+# The default target below does the proper clone-at-pin + cmake build so CI does
+# not need a side-checkout.
+
+FACEDETECT_VERSION?=be22d67145a8bcd879f45ad33fbea03131c5922b
+FACEDETECT_REPO?=https://github.com/mudler/face-detect.cpp
+
+GOCMD?=go
+GO_TAGS?=
+JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
+
+BUILD_TYPE?=
+NATIVE?=false
+
+# Build ggml + the vendored libjpeg-turbo statically into libfacedetect.so (PIC)
+# so the shared lib is self-contained: dlopen needs no libggml*.so alongside it,
+# only system libs (libstdc++/libgomp/libc) the runtime image already provides.
+# The vendored jpeg symbols are hidden via -Wl,--exclude-libs,ALL on the C++
+# side, so only the facedetect_capi_* surface is exported.
+CMAKE_ARGS?=-DCMAKE_BUILD_TYPE=Release -DFACEDETECT_SHARED=ON -DFACEDETECT_BUILD_CLI=OFF -DFACEDETECT_BUILD_TESTS=OFF -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON
+
+ifeq ($(NATIVE),false)
+	CMAKE_ARGS+=-DGGML_NATIVE=OFF
+endif
+
+# face-detect.cpp gates its GGML backends behind FACEDETECT_GGML_* options and
+# does set(GGML_CUDA ${FACEDETECT_GGML_CUDA} CACHE BOOL "" FORCE), so a bare
+# -DGGML_CUDA=ON is overwritten back to OFF. Forward the FACEDETECT_GGML_*
+# options instead. (openblas is not gated, so -DGGML_BLAS passes through.)
+ifeq ($(BUILD_TYPE),cublas)
+	CMAKE_ARGS+=-DFACEDETECT_GGML_CUDA=ON
+else ifeq ($(BUILD_TYPE),openblas)
+	CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
+else ifeq ($(BUILD_TYPE),hipblas)
+	CMAKE_ARGS+=-DFACEDETECT_GGML_HIP=ON
+else ifeq ($(BUILD_TYPE),vulkan)
+	CMAKE_ARGS+=-DFACEDETECT_GGML_VULKAN=ON
+else ifeq ($(BUILD_TYPE),metal)
+	CMAKE_ARGS+=-DFACEDETECT_GGML_METAL=ON
+endif
+
+.PHONY: face-detect-grpc package build clean purge test all
+
+all: face-detect-grpc
+
+# Clone the upstream face-detect.cpp source at the pinned commit. Directory acts
+# as the target so make only re-clones when missing. After a FACEDETECT_VERSION
+# bump, run 'make purge && make' to refetch.
+sources/face-detect.cpp:
+	mkdir -p sources/face-detect.cpp
+	cd sources/face-detect.cpp && \
+	git init -q && \
+	git remote add origin $(FACEDETECT_REPO) && \
+	git fetch --depth 1 origin $(FACEDETECT_VERSION) && \
+	git checkout FETCH_HEAD && \
+	git submodule update --init --recursive --depth 1 --single-branch
+
+# Build the shared lib + header out-of-tree, then stage them next to the Go
+# sources so purego.Dlopen("libfacedetect.so") and the cgo-less build both pick
+# them up.
+libfacedetect.so: sources/face-detect.cpp
+	cmake -B sources/face-detect.cpp/build-shared -S sources/face-detect.cpp $(CMAKE_ARGS)
+	cmake --build sources/face-detect.cpp/build-shared --config Release -j$(JOBS) --target facedetect
+	cp -fv sources/face-detect.cpp/build-shared/libfacedetect.so* ./ 2>/dev/null || true
+	cp -fv sources/face-detect.cpp/include/facedetect_capi.h ./
+
+face-detect-grpc: libfacedetect.so main.go gofacedetect.go options.go
+	CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o face-detect-grpc .
+
+package: face-detect-grpc
+	bash package.sh
+
+build: package
+
+# Test target. The embed/detect/verify/analyze smoke specs are gated on
+# FACEDETECT_BACKEND_TEST_MODEL + FACEDETECT_BACKEND_TEST_IMAGE; without them the
+# heavy specs auto-skip and only the pure-Go parsing specs run.
+test:
+	LD_LIBRARY_PATH=$(CURDIR):$$LD_LIBRARY_PATH $(GOCMD) test ./... -count=1
+
+clean: purge
+	rm -rf libfacedetect.so* facedetect_capi.h package face-detect-grpc
+
+purge:
+	rm -rf sources/face-detect.cpp

backend/go/face-detect/gofacedetect.go

@@ -0,0 +1,431 @@
+package main
+
+import (
+	"encoding/base64"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"math"
+	"os"
+	"path/filepath"
+	"strconv"
+	"strings"
+	"time"
+	"unsafe"
+
+	"github.com/mudler/LocalAI/pkg/grpc/base"
+	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
+	"github.com/mudler/xlog"
+)
+
+// purego-bound entry points from libfacedetect.so. Names match
+// facedetect_capi.h exactly so a `nm libfacedetect.so | grep facedetect_capi`
+// is enough to spot drift.
+//
+// The opaque ctx and the malloc'd char*/float* return values are declared as
+// uintptr so we get the raw pointer back and can release it via the matching
+// capi free function. purego's native string/[]float32 returns would copy and
+// forget the original pointer, leaking the C-owned buffer on every call.
+var (
+	CppAbiVersion  func() int32
+	CppLoad        func(ggufPath string) uintptr
+	CppFree        func(ctx uintptr)
+	CppLastError   func(ctx uintptr) string
+	CppFreeString  func(s uintptr)
+	CppFreeVec     func(v uintptr)
+	CppEmbedPath   func(ctx uintptr, imagePath string, outVec, outDim unsafe.Pointer) int32
+	CppEmbedRGB    func(ctx uintptr, rgb []byte, width, height int32, outVec, outDim unsafe.Pointer) int32
+	CppDetectJSON  func(ctx uintptr, imagePath string) uintptr
+	CppVerifyPaths func(ctx uintptr, a, b string, threshold float32, antiSpoof int32, outDistance, outVerified unsafe.Pointer) int32
+	CppAnalyzeJSON func(ctx uintptr, imagePath string) uintptr
+)
+
+// FaceDetect implements the face-recognition (biometric) subset of the Backend
+// gRPC service over libfacedetect.so. The C side keeps a single loaded model
+// pack plus a per-ctx last-error buffer and is not reentrant, so
+// base.SingleThread serializes every call.
+type FaceDetect struct {
+	base.SingleThread
+	opts   loadOptions
+	ctxPtr uintptr
+}
+
+func (f *FaceDetect) Load(opts *pb.ModelOptions) error {
+	model := opts.ModelFile
+	if model == "" {
+		model = opts.ModelPath
+	}
+	if !filepath.IsAbs(model) && opts.ModelPath != "" {
+		model = filepath.Join(opts.ModelPath, model)
+	}
+	if model == "" {
+		return errors.New("face-detect: ModelFile is required")
+	}
+
+	f.opts = parseOptions(opts.Options)
+	if f.opts.modelName == "" {
+		f.opts.modelName = filepath.Base(model)
+	}
+
+	// Propagate LocalAI's per-model thread budget to the engine. LocalAI spawns
+	// one backend process per model and serves requests concurrently, so the
+	// engine's own min(hardware_concurrency, 8) default can oversubscribe cores.
+	// FACEDETECT_THREADS is read by the engine at backend construction, so it
+	// must be set before the capi load. A non-positive Threads means "unset":
+	// leave the env alone so the engine keeps its sane default.
+	threads := opts.Threads
+	if threads > 0 {
+		if err := os.Setenv("FACEDETECT_THREADS", strconv.Itoa(int(threads))); err != nil {
+			return fmt.Errorf("face-detect: set FACEDETECT_THREADS: %w", err)
+		}
+		xlog.Info("face-detect: applying LocalAI thread budget", "threads", threads)
+	}
+
+	xlog.Info("face-detect: loading model", "model", model,
+		"verify_threshold", f.opts.verifyThreshold, "abi", CppAbiVersion())
+
+	ctx := CppLoad(model)
+	if ctx == 0 {
+		// The last-error buffer lives on the ctx that was never returned, so
+		// surface the path the operator tried to load instead.
+		return fmt.Errorf("face-detect: facedetect_capi_load failed for %q", model)
+	}
+	f.ctxPtr = ctx
+	return nil
+}
+
+// Embeddings returns the L2-normalized ArcFace embedding of the primary face in
+// the supplied image. Mirroring the Python face backend, the image is read from
+// Images[0] as a base64 payload; materializeImage decodes it to a temp file so
+// the path-based C-API can run its own decode (cv2.imread parity). The gRPC
+// server wraps the returned slice in an EmbeddingResult.
+func (f *FaceDetect) Embeddings(req *pb.PredictOptions) ([]float32, error) {
+	if f.ctxPtr == 0 {
+		return nil, errors.New("face-detect: model not loaded")
+	}
+	if len(req.Images) == 0 || req.Images[0] == "" {
+		return nil, errors.New("face-detect: Embedding requires Images[0] to be a base64 image")
+	}
+
+	path, cleanup, err := materializeImage(req.Images[0])
+	if err != nil {
+		return nil, err
+	}
+	defer cleanup()
+
+	return f.embedPath(path)
+}
+
+func (f *FaceDetect) embedPath(path string) ([]float32, error) {
+	var vec uintptr
+	var dim int32
+	rc := CppEmbedPath(f.ctxPtr, path, unsafe.Pointer(&vec), unsafe.Pointer(&dim))
+	if rc != 0 || vec == 0 || dim <= 0 {
+		return nil, f.lastErr("embed", path)
+	}
+	defer CppFreeVec(vec)
+	// Copy out of the C-owned malloc'd buffer before freeing it. The
+	// uintptr->Pointer conversion trips vet's unsafeptr check, which can't tell
+	// a C heap pointer from Go-managed memory; safe here, the GC neither tracks
+	// nor moves this buffer and we copy immediately.
+	src := unsafe.Slice((*float32)(unsafe.Pointer(vec)), int(dim)) //nolint:govet // C-owned malloc'd vector, copied out before free
+	out := make([]float32, int(dim))
+	copy(out, src)
+	return out, nil
+}
+
+// Detect runs SCRFD over the image and returns one Detection per face. The
+// C-API emits a box as [x1,y1,x2,y2] in pixels; the proto carries x/y plus
+// width/height, so the corners are converted. The 5 facial landmarks the engine
+// also returns are dropped: the Detection message has no field for them.
+func (f *FaceDetect) Detect(req *pb.DetectOptions) (pb.DetectResponse, error) {
+	if f.ctxPtr == 0 {
+		return pb.DetectResponse{}, errors.New("face-detect: model not loaded")
+	}
+	if req.Src == "" {
+		return pb.DetectResponse{}, errors.New("face-detect: src image is required")
+	}
+
+	path, cleanup, err := materializeImage(req.Src)
+	if err != nil {
+		return pb.DetectResponse{}, err
+	}
+	defer cleanup()
+
+	faces, err := f.detectFaces(path)
+	if err != nil {
+		return pb.DetectResponse{}, err
+	}
+
+	dets := make([]*pb.Detection, 0, len(faces))
+	for _, fc := range faces {
+		if req.Threshold > 0 && fc.Score < req.Threshold {
+			continue
+		}
+		x, y, w, h := fc.xywh()
+		dets = append(dets, &pb.Detection{
+			X:          x,
+			Y:          y,
+			Width:      w,
+			Height:     h,
+			Confidence: fc.Score,
+			ClassName:  "face",
+		})
+	}
+	return pb.DetectResponse{Detections: dets}, nil
+}
+
+// FaceVerify embeds the primary face in each image and reports whether they are
+// the same identity by cosine distance against a threshold. A request threshold
+// <= 0 falls back to the model-configured default (verify_threshold option,
+// 0.35 if unset). When anti_spoofing is set, the C-API applies a MiniFASNet
+// veto internally (verified forced false on a spoof); the per-image liveness
+// scores are not exposed by the verify entry point, so img*_is_real /
+// img*_antispoof_score stay at their zero values.
+func (f *FaceDetect) FaceVerify(req *pb.FaceVerifyRequest) (pb.FaceVerifyResponse, error) {
+	if f.ctxPtr == 0 {
+		return pb.FaceVerifyResponse{}, errors.New("face-detect: model not loaded")
+	}
+	if req.Img1 == "" || req.Img2 == "" {
+		return pb.FaceVerifyResponse{}, errors.New("face-detect: img1 and img2 are required")
+	}
+
+	path1, cleanup1, err := materializeImage(req.Img1)
+	if err != nil {
+		return pb.FaceVerifyResponse{}, err
+	}
+	defer cleanup1()
+	path2, cleanup2, err := materializeImage(req.Img2)
+	if err != nil {
+		return pb.FaceVerifyResponse{}, err
+	}
+	defer cleanup2()
+
+	threshold := req.Threshold
+	if threshold <= 0 {
+		threshold = f.opts.verifyThreshold
+	}
+
+	antiSpoof := int32(0)
+	if req.AntiSpoofing {
+		antiSpoof = 1
+	}
+
+	started := time.Now()
+	var distance float32
+	var verified int32
+	rc := CppVerifyPaths(f.ctxPtr, path1, path2, threshold, antiSpoof,
+		unsafe.Pointer(&distance), unsafe.Pointer(&verified))
+	if rc != 0 {
+		return pb.FaceVerifyResponse{}, f.lastErr("verify", req.Img1[:min(8, len(req.Img1))]+"...")
+	}
+	elapsedMs := float32(time.Since(started).Seconds() * 1000.0)
+
+	// Confidence decays linearly from 100 at distance 0 to 0 at the threshold,
+	// matching the Python face backend's reporting.
+	confidence := float32(0)
+	if threshold > 0 {
+		confidence = float32(math.Max(0, math.Min(100, (1.0-float64(distance)/float64(threshold))*100.0)))
+	}
+
+	return pb.FaceVerifyResponse{
+		Verified:         verified != 0,
+		Distance:         distance,
+		Threshold:        threshold,
+		Confidence:       confidence,
+		Model:            f.opts.modelName,
+		Img1Area:         f.bestArea(path1),
+		Img2Area:         f.bestArea(path2),
+		ProcessingTimeMs: elapsedMs,
+	}, nil
+}
+
+// FaceAnalyze runs the genderage head on every detected face. The C-API returns
+// "M"/"F" gender labels and a rounded age; the labels are normalized to the
+// "Man"/"Woman" values the proto documents.
+func (f *FaceDetect) FaceAnalyze(req *pb.FaceAnalyzeRequest) (pb.FaceAnalyzeResponse, error) {
+	if f.ctxPtr == 0 {
+		return pb.FaceAnalyzeResponse{}, errors.New("face-detect: model not loaded")
+	}
+	if req.Img == "" {
+		return pb.FaceAnalyzeResponse{}, errors.New("face-detect: img is required")
+	}
+
+	path, cleanup, err := materializeImage(req.Img)
+	if err != nil {
+		return pb.FaceAnalyzeResponse{}, err
+	}
+	defer cleanup()
+
+	ptr := CppAnalyzeJSON(f.ctxPtr, path)
+	if ptr == 0 {
+		return pb.FaceAnalyzeResponse{}, f.lastErr("analyze", path)
+	}
+	defer CppFreeString(ptr)
+
+	faces, err := parseAnalyzeJSON(goStringFromCPtr(ptr))
+	if err != nil {
+		return pb.FaceAnalyzeResponse{}, fmt.Errorf("face-detect: analyze JSON: %w", err)
+	}
+	return pb.FaceAnalyzeResponse{Faces: faces}, nil
+}
+
+// faceBox is one entry of the detect/analyze JSON documents the engine emits.
+type faceBox struct {
+	Score  float32   `json:"score"`
+	Box    []float32 `json:"box"`
+	Age    float32   `json:"age"`
+	Gender string    `json:"gender"`
+}
+
+// xywh converts the engine's [x1,y1,x2,y2] box into the x/y/width/height the
+// proto carries. A short or missing box yields zeros.
+func (b faceBox) xywh() (x, y, w, h float32) {
+	if len(b.Box) < 4 {
+		return 0, 0, 0, 0
+	}
+	return b.Box[0], b.Box[1], b.Box[2] - b.Box[0], b.Box[3] - b.Box[1]
+}
+
+type facesJSON struct {
+	Faces []faceBox `json:"faces"`
+}
+
+func (f *FaceDetect) detectFaces(path string) ([]faceBox, error) {
+	ptr := CppDetectJSON(f.ctxPtr, path)
+	if ptr == 0 {
+		return nil, f.lastErr("detect", path)
+	}
+	defer CppFreeString(ptr)
+
+	var doc facesJSON
+	if err := json.Unmarshal([]byte(goStringFromCPtr(ptr)), &doc); err != nil {
+		return nil, fmt.Errorf("face-detect: detect JSON: %w", err)
+	}
+	return doc.Faces, nil
+}
+
+// bestArea returns the FacialArea of the highest-scoring face in an image, or an
+// empty area when detection fails or finds nothing. Best-effort: verify already
+// succeeded, so a missing region must not turn a valid match into an error.
+func (f *FaceDetect) bestArea(path string) *pb.FacialArea {
+	faces, err := f.detectFaces(path)
+	if err != nil || len(faces) == 0 {
+		return &pb.FacialArea{}
+	}
+	best := faces[0]
+	for _, fc := range faces[1:] {
+		if fc.Score > best.Score {
+			best = fc
+		}
+	}
+	x, y, w, h := best.xywh()
+	return &pb.FacialArea{X: x, Y: y, W: w, H: h}
+}
+
+// parseAnalyzeJSON maps the engine's analyze document onto FaceAnalysis entries.
+// The engine reports gender as "M"/"F"; both the dominant label and the score
+// map are filled with the "Man"/"Woman" form the proto documents.
+func parseAnalyzeJSON(doc string) ([]*pb.FaceAnalysis, error) {
+	var parsed facesJSON
+	if err := json.Unmarshal([]byte(doc), &parsed); err != nil {
+		return nil, err
+	}
+
+	out := make([]*pb.FaceAnalysis, 0, len(parsed.Faces))
+	for _, fc := range parsed.Faces {
+		x, y, w, h := fc.xywh()
+		fa := &pb.FaceAnalysis{
+			Region:         &pb.FacialArea{X: x, Y: y, W: w, H: h},
+			FaceConfidence: fc.Score,
+			Age:            fc.Age,
+		}
+		if label := normalizeGender(fc.Gender); label != "" {
+			fa.DominantGender = label
+			fa.Gender = map[string]float32{label: 1.0}
+		}
+		out = append(out, fa)
+	}
+	return out, nil
+}
+
+// normalizeGender maps the engine's "M"/"F" code to the "Man"/"Woman" labels the
+// proto documents. Unknown codes pass through unchanged.
+func normalizeGender(g string) string {
+	switch strings.ToUpper(strings.TrimSpace(g)) {
+	case "M":
+		return "Man"
+	case "F":
+		return "Woman"
+	case "":
+		return ""
+	default:
+		return g
+	}
+}
+
+// materializeImage decodes a base64 image payload into a temp file and returns
+// its path plus a cleanup func. As a convenience for callers that already pass a
+// filesystem path (e.g. a test fixture), an existing path is used as-is with a
+// no-op cleanup. data: URI prefixes are stripped before decoding.
+func materializeImage(src string) (path string, cleanup func(), err error) {
+	noop := func() {}
+	if src == "" {
+		return "", noop, errors.New("face-detect: empty image input")
+	}
+	if _, statErr := os.Stat(src); statErr == nil {
+		return src, noop, nil
+	}
+
+	payload := src
+	if i := strings.Index(payload, ","); strings.HasPrefix(payload, "data:") && i >= 0 {
+		payload = payload[i+1:]
+	}
+	data, decErr := base64.StdEncoding.DecodeString(strings.TrimSpace(payload))
+	if decErr != nil || len(data) == 0 {
+		return "", noop, errors.New("face-detect: image is neither an existing path nor valid base64")
+	}
+
+	tmp, createErr := os.CreateTemp("", "face-detect-*.img")
+	if createErr != nil {
+		return "", noop, fmt.Errorf("face-detect: create temp image: %w", createErr)
+	}
+	cleanup = func() { _ = os.Remove(tmp.Name()) }
+	if _, wErr := tmp.Write(data); wErr != nil {
+		_ = tmp.Close()
+		cleanup()
+		return "", noop, fmt.Errorf("face-detect: write temp image: %w", wErr)
+	}
+	if cErr := tmp.Close(); cErr != nil {
+		cleanup()
+		return "", noop, fmt.Errorf("face-detect: close temp image: %w", cErr)
+	}
+	return tmp.Name(), cleanup, nil
+}
+
+// lastErr wraps the C-API's per-ctx last-error buffer into a Go error.
+func (f *FaceDetect) lastErr(op, subject string) error {
+	msg := strings.TrimSpace(CppLastError(f.ctxPtr))
+	if msg == "" {
+		msg = "no error detail"
+	}
+	return fmt.Errorf("face-detect: %s failed for %q: %s", op, subject, msg)
+}
+
+// goStringFromCPtr copies a NUL-terminated C string into Go memory. cptr is a
+// malloc'd buffer the caller owns; release it via CppFreeString after the copy.
+//
+// The uintptr->Pointer conversion trips vet's unsafeptr check, which can't tell
+// a C heap pointer from Go-managed memory. Safe here: the GC neither tracks nor
+// moves the buffer and we dereference it immediately to copy the bytes out.
+func goStringFromCPtr(cptr uintptr) string {
+	if cptr == 0 {
+		return ""
+	}
+	p := unsafe.Pointer(cptr) //nolint:govet // C-owned malloc'd buffer, not Go-GC memory (see doc above)
+	n := 0
+	for *(*byte)(unsafe.Add(p, n)) != 0 {
+		n++
+	}
+	return string(unsafe.Slice((*byte)(p), n))
+}

backend/go/face-detect/gofacedetect_test.go

@@ -0,0 +1,230 @@
+package main
+
+import (
+	"encoding/base64"
+	"os"
+	"sync"
+	"testing"
+
+	"github.com/ebitengine/purego"
+	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+func TestFaceDetect(t *testing.T) {
+	RegisterFailHandler(Fail)
+	RunSpecs(t, "face-detect Backend Suite")
+}
+
+var (
+	libLoadOnce sync.Once
+	libLoadErr  error
+)
+
+// ensureLibLoaded mirrors main.go's bootstrap so a Go test can drive the C-API
+// bridge without spinning up the gRPC server. Records the error (the smoke
+// specs skip themselves) when libfacedetect.so is not loadable from cwd
+// (LD_LIBRARY_PATH or a symlink in ./).
+func ensureLibLoaded() error {
+	libLoadOnce.Do(func() {
+		libName := os.Getenv("FACEDETECT_LIBRARY")
+		if libName == "" {
+			libName = "libfacedetect.so"
+		}
+		lib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
+		if err != nil {
+			libLoadErr = err
+			return
+		}
+		purego.RegisterLibFunc(&CppAbiVersion, lib, "facedetect_capi_abi_version")
+		purego.RegisterLibFunc(&CppLoad, lib, "facedetect_capi_load")
+		purego.RegisterLibFunc(&CppFree, lib, "facedetect_capi_free")
+		purego.RegisterLibFunc(&CppLastError, lib, "facedetect_capi_last_error")
+		purego.RegisterLibFunc(&CppFreeString, lib, "facedetect_capi_free_string")
+		purego.RegisterLibFunc(&CppFreeVec, lib, "facedetect_capi_free_vec")
+		purego.RegisterLibFunc(&CppEmbedPath, lib, "facedetect_capi_embed_path")
+		purego.RegisterLibFunc(&CppEmbedRGB, lib, "facedetect_capi_embed_rgb")
+		purego.RegisterLibFunc(&CppDetectJSON, lib, "facedetect_capi_detect_path_json")
+		purego.RegisterLibFunc(&CppVerifyPaths, lib, "facedetect_capi_verify_paths")
+		purego.RegisterLibFunc(&CppAnalyzeJSON, lib, "facedetect_capi_analyze_path_json")
+	})
+	return libLoadErr
+}
+
+var _ = Describe("parseOptions", func() {
+	It("defaults verify_threshold to 0.35", func() {
+		o := parseOptions(nil)
+		Expect(o.verifyThreshold).To(Equal(float32(0.35)))
+		Expect(o.modelName).To(Equal(""))
+	})
+
+	It("parses verify_threshold, threshold alias and model_name", func() {
+		o := parseOptions([]string{"verify_threshold:0.4", "model_name:buffalo_l", "unknown:x"})
+		Expect(o.verifyThreshold).To(Equal(float32(0.4)))
+		Expect(o.modelName).To(Equal("buffalo_l"))
+
+		o2 := parseOptions([]string{"threshold:0.3"})
+		Expect(o2.verifyThreshold).To(Equal(float32(0.3)))
+	})
+
+	It("ignores non-positive thresholds and keeps the default", func() {
+		o := parseOptions([]string{"verify_threshold:0", "threshold:-1"})
+		Expect(o.verifyThreshold).To(Equal(float32(0.35)))
+	})
+})
+
+var _ = Describe("normalizeGender", func() {
+	It("maps M/F codes to Man/Woman", func() {
+		Expect(normalizeGender("M")).To(Equal("Man"))
+		Expect(normalizeGender("f")).To(Equal("Woman"))
+		Expect(normalizeGender(" m ")).To(Equal("Man"))
+	})
+
+	It("passes empty and unknown codes through", func() {
+		Expect(normalizeGender("")).To(Equal(""))
+		Expect(normalizeGender("nonbinary")).To(Equal("nonbinary"))
+	})
+})
+
+var _ = Describe("faceBox.xywh", func() {
+	It("converts an [x1,y1,x2,y2] box to x/y/width/height", func() {
+		b := faceBox{Box: []float32{10, 20, 50, 80}}
+		x, y, w, h := b.xywh()
+		Expect(x).To(Equal(float32(10)))
+		Expect(y).To(Equal(float32(20)))
+		Expect(w).To(Equal(float32(40)))
+		Expect(h).To(Equal(float32(60)))
+	})
+
+	It("returns zeros for a short box", func() {
+		x, y, w, h := faceBox{Box: []float32{1, 2}}.xywh()
+		Expect([]float32{x, y, w, h}).To(Equal([]float32{0, 0, 0, 0}))
+	})
+})
+
+var _ = Describe("parseAnalyzeJSON", func() {
+	It("maps region, age and gender for each face", func() {
+		doc := `{"faces":[
+			{"score":0.997,"box":[10,20,50,80],"age":31,"gender":"M"},
+			{"score":0.81,"box":[0,0,40,40],"age":24,"gender":"F"}]}`
+		faces, err := parseAnalyzeJSON(doc)
+		Expect(err).ToNot(HaveOccurred())
+		Expect(faces).To(HaveLen(2))
+
+		Expect(faces[0].FaceConfidence).To(BeNumerically("~", 0.997, 1e-4))
+		Expect(faces[0].Age).To(BeNumerically("~", 31, 1e-4))
+		Expect(faces[0].DominantGender).To(Equal("Man"))
+		Expect(faces[0].Gender).To(HaveKeyWithValue("Man", float32(1.0)))
+		Expect(faces[0].Region.W).To(Equal(float32(40)))
+		Expect(faces[0].Region.H).To(Equal(float32(60)))
+
+		Expect(faces[1].DominantGender).To(Equal("Woman"))
+	})
+
+	It("tolerates a missing gender field", func() {
+		faces, err := parseAnalyzeJSON(`{"faces":[{"score":0.5,"box":[0,0,10,10],"age":40}]}`)
+		Expect(err).ToNot(HaveOccurred())
+		Expect(faces).To(HaveLen(1))
+		Expect(faces[0].DominantGender).To(Equal(""))
+		Expect(faces[0].Gender).To(BeEmpty())
+	})
+
+	It("returns no faces for an empty document", func() {
+		faces, err := parseAnalyzeJSON(`{"faces":[]}`)
+		Expect(err).ToNot(HaveOccurred())
+		Expect(faces).To(BeEmpty())
+	})
+
+	It("returns an error on malformed JSON", func() {
+		_, err := parseAnalyzeJSON(`{not-json`)
+		Expect(err).To(HaveOccurred())
+	})
+})
+
+var _ = Describe("materializeImage", func() {
+	It("decodes a base64 payload to a temp file", func() {
+		payload := base64.StdEncoding.EncodeToString([]byte("\xff\xd8\xff\xe0fake-jpeg"))
+		path, cleanup, err := materializeImage(payload)
+		Expect(err).ToNot(HaveOccurred())
+		defer cleanup()
+		data, rerr := os.ReadFile(path)
+		Expect(rerr).ToNot(HaveOccurred())
+		Expect(data).To(Equal([]byte("\xff\xd8\xff\xe0fake-jpeg")))
+	})
+
+	It("strips a data: URI prefix before decoding", func() {
+		payload := "data:image/png;base64," + base64.StdEncoding.EncodeToString([]byte("hello"))
+		path, cleanup, err := materializeImage(payload)
+		Expect(err).ToNot(HaveOccurred())
+		defer cleanup()
+		data, rerr := os.ReadFile(path)
+		Expect(rerr).ToNot(HaveOccurred())
+		Expect(data).To(Equal([]byte("hello")))
+	})
+
+	It("uses an existing path as-is", func() {
+		tmp, err := os.CreateTemp("", "face-detect-fixture-*.bin")
+		Expect(err).ToNot(HaveOccurred())
+		defer func() { _ = os.Remove(tmp.Name()) }()
+		Expect(tmp.Close()).To(Succeed())
+
+		path, cleanup, err := materializeImage(tmp.Name())
+		Expect(err).ToNot(HaveOccurred())
+		defer cleanup()
+		Expect(path).To(Equal(tmp.Name()))
+	})
+
+	It("errors on input that is neither a path nor base64", func() {
+		_, _, err := materializeImage("not base64!!!")
+		Expect(err).To(HaveOccurred())
+	})
+})
+
+// The specs below exercise the real C-API end to end. They run only when both a
+// model GGUF and a test image are provided, and skip cleanly otherwise so the
+// suite stays green without large assets.
+var _ = Describe("FaceDetect end-to-end", Ordered, func() {
+	var (
+		f         *FaceDetect
+		modelPath = os.Getenv("FACEDETECT_BACKEND_TEST_MODEL")
+		imagePath = os.Getenv("FACEDETECT_BACKEND_TEST_IMAGE")
+	)
+
+	BeforeAll(func() {
+		if modelPath == "" || imagePath == "" {
+			Skip("set FACEDETECT_BACKEND_TEST_MODEL and FACEDETECT_BACKEND_TEST_IMAGE to run the e2e specs")
+		}
+		if err := ensureLibLoaded(); err != nil {
+			Skip("libfacedetect.so not loadable: " + err.Error())
+		}
+		f = &FaceDetect{}
+		Expect(f.Load(&pb.ModelOptions{ModelFile: modelPath})).To(Succeed())
+	})
+
+	It("embeds the primary face in an image", func() {
+		emb, err := f.Embeddings(&pb.PredictOptions{Images: []string{imagePath}})
+		Expect(err).ToNot(HaveOccurred())
+		Expect(emb).ToNot(BeEmpty())
+	})
+
+	It("detects at least one face", func() {
+		resp, err := f.Detect(&pb.DetectOptions{Src: imagePath})
+		Expect(err).ToNot(HaveOccurred())
+		Expect(resp.Detections).ToNot(BeEmpty())
+		Expect(resp.Detections[0].ClassName).To(Equal("face"))
+	})
+
+	It("verifies an image against itself as the same identity", func() {
+		resp, err := f.FaceVerify(&pb.FaceVerifyRequest{Img1: imagePath, Img2: imagePath})
+		Expect(err).ToNot(HaveOccurred())
+		Expect(resp.Verified).To(BeTrue())
+		Expect(resp.Distance).To(BeNumerically("<=", resp.Threshold))
+	})
+
+	It("analyzes age/gender for each face", func() {
+		resp, err := f.FaceAnalyze(&pb.FaceAnalyzeRequest{Img: imagePath})
+		Expect(err).ToNot(HaveOccurred())
+		Expect(resp.Faces).ToNot(BeEmpty())
+	})
+})

backend/go/face-detect/main.go

@@ -0,0 +1,65 @@
+package main
+
+// Started internally by LocalAI - one gRPC server per loaded model.
+//
+// Loads libfacedetect.so via purego and registers the flat C-API entry points
+// declared in facedetect_capi.h. The library name can be overridden with
+// FACEDETECT_LIBRARY (mirrors the VOICEDETECT_LIBRARY / PARAKEET_LIBRARY
+// convention in the sibling backends); the default looks for the .so next to
+// this binary (resolved via LD_LIBRARY_PATH by run.sh).
+import (
+	"flag"
+	"fmt"
+	"os"
+
+	"github.com/ebitengine/purego"
+	grpc "github.com/mudler/LocalAI/pkg/grpc"
+)
+
+var (
+	addr = flag.String("addr", "localhost:50051", "the address to connect to")
+)
+
+type LibFuncs struct {
+	FuncPtr any
+	Name    string
+}
+
+func main() {
+	libName := os.Getenv("FACEDETECT_LIBRARY")
+	if libName == "" {
+		libName = "libfacedetect.so"
+	}
+
+	lib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
+	if err != nil {
+		panic(fmt.Errorf("face-detect: dlopen %q: %w", libName, err))
+	}
+
+	// Bound 1:1 to facedetect_capi.h. char*/float* returns are registered as
+	// uintptr so the raw pointer can be freed via the matching capi free fn.
+	libFuncs := []LibFuncs{
+		{&CppAbiVersion, "facedetect_capi_abi_version"},
+		{&CppLoad, "facedetect_capi_load"},
+		{&CppFree, "facedetect_capi_free"},
+		{&CppLastError, "facedetect_capi_last_error"},
+		{&CppFreeString, "facedetect_capi_free_string"},
+		{&CppFreeVec, "facedetect_capi_free_vec"},
+		{&CppEmbedPath, "facedetect_capi_embed_path"},
+		{&CppEmbedRGB, "facedetect_capi_embed_rgb"},
+		{&CppDetectJSON, "facedetect_capi_detect_path_json"},
+		{&CppVerifyPaths, "facedetect_capi_verify_paths"},
+		{&CppAnalyzeJSON, "facedetect_capi_analyze_path_json"},
+	}
+	for _, lf := range libFuncs {
+		purego.RegisterLibFunc(lf.FuncPtr, lib, lf.Name)
+	}
+
+	fmt.Fprintf(os.Stderr, "[face-detect] ABI=%d\n", CppAbiVersion())
+
+	flag.Parse()
+
+	if err := grpc.StartServer(*addr, &FaceDetect{}); err != nil {
+		panic(err)
+	}
+}

backend/go/face-detect/options.go

@@ -0,0 +1,47 @@
+package main
+
+import (
+	"strconv"
+	"strings"
+)
+
+// defaultVerifyThreshold is the cosine-distance cutoff used when a request does
+// not set one. Matches the insightface buffalo_l ArcFace R50 default the Python
+// face backend ships with so the two implementations agree on verdicts out of
+// the box.
+const defaultVerifyThreshold float32 = 0.35
+
+// loadOptions holds the parsed model-level options for face-detect.
+type loadOptions struct {
+	verifyThreshold float32
+	modelName       string
+}
+
+func splitOption(o string) (key, value string, ok bool) {
+	i := strings.Index(o, ":")
+	if i < 0 {
+		return "", "", false
+	}
+	return strings.TrimSpace(o[:i]), strings.TrimSpace(o[i+1:]), true
+}
+
+// parseOptions reads the backend "key:value" option slice. Unknown keys are
+// ignored. Defaults: verify_threshold 0.35, model_name derived from the file.
+func parseOptions(opts []string) loadOptions {
+	o := loadOptions{verifyThreshold: defaultVerifyThreshold}
+	for _, oo := range opts {
+		key, value, ok := splitOption(oo)
+		if !ok {
+			continue
+		}
+		switch key {
+		case "verify_threshold", "threshold":
+			if f, err := strconv.ParseFloat(value, 32); err == nil && f > 0 {
+				o.verifyThreshold = float32(f)
+			}
+		case "model_name":
+			o.modelName = value
+		}
+	}
+	return o
+}

backend/go/face-detect/package.sh

@@ -0,0 +1,68 @@
+#!/bin/bash
+#
+# Bundle the face-detect-grpc binary, libfacedetect.so, the core runtime libs
+# (libc/libstdc++/libgomp + ld.so) and the GPU runtime for the active BUILD_TYPE
+# so the package is self-contained. Mirrors backend/go/voice-detect/package.sh;
+# run.sh routes the (CGO_ENABLED=0) binary through lib/ld.so so the packaged libc
+# is used instead of the host's.
+
+set -e
+
+CURDIR=$(dirname "$(realpath "$0")")
+REPO_ROOT="${CURDIR}/../../.."
+
+mkdir -p "$CURDIR/package/lib"
+
+cp -avf "$CURDIR/face-detect-grpc" "$CURDIR/package/"
+cp -avf "$CURDIR/run.sh" "$CURDIR/package/"
+
+# libfacedetect.so + any soname symlinks. purego.Dlopen resolves it via
+# LD_LIBRARY_PATH, which run.sh points at lib/.
+cp -avf "$CURDIR"/libfacedetect.so* "$CURDIR/package/lib/" 2>/dev/null || {
+	echo "ERROR: libfacedetect.so not found in $CURDIR, run 'make' first" >&2
+	exit 1
+}
+
+# Detect architecture and copy the core runtime libs libfacedetect.so links
+# against, plus the matching dynamic loader as lib/ld.so.
+if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
+    echo "Detected x86_64 architecture, copying x86_64 libraries..."
+    cp -arfLv /lib64/ld-linux-x86-64.so.2 "$CURDIR/package/lib/ld.so"
+    cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
+    cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
+elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
+    echo "Detected ARM64 architecture, copying ARM64 libraries..."
+    cp -arfLv /lib/ld-linux-aarch64.so.1 "$CURDIR/package/lib/ld.so"
+    cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
+    cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
+elif [ "$(uname -s)" = "Darwin" ]; then
+    echo "Detected Darwin"
+else
+    echo "Error: Could not detect architecture"
+    exit 1
+fi
+
+# Package GPU libraries (CUDA/ROCm/Intel/Vulkan loader + ICDs + drivers) based on
+# BUILD_TYPE so the backend can reach the GPU without the runtime base image
+# shipping those drivers.
+GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
+if [ -f "$GPU_LIB_SCRIPT" ]; then
+    echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
+    source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
+    package_gpu_libs
+fi
+
+echo "Packaging completed successfully"
+ls -liah "$CURDIR/package/" "$CURDIR/package/lib/"

backend/go/face-detect/run.sh

@@ -0,0 +1,16 @@
+#!/bin/bash
+set -e
+
+CURDIR=$(dirname "$(realpath "$0")")
+
+export LD_LIBRARY_PATH="$CURDIR/lib:$CURDIR:${LD_LIBRARY_PATH:-}"
+
+# If a self-contained ld.so was packaged, route through it so the packaged
+# libc / libstdc++ are used instead of the host's (matches the voice-detect /
+# whisper / parakeet backends' runtime layout).
+if [ -f "$CURDIR/lib/ld.so" ]; then
+	echo "Using lib/ld.so"
+	exec "$CURDIR/lib/ld.so" "$CURDIR/face-detect-grpc" "$@"
+fi
+
+exec "$CURDIR/face-detect-grpc" "$@"

backend/go/face-detect/test.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+set -e
+
+CURDIR=$(dirname "$(realpath "$0")")
+cd "$CURDIR"
+
+echo "Running face-detect backend tests..."
+
+# The pure-Go parsing specs always run. The embed/detect/verify/analyze smoke
+# specs run only when a model + image are provided via
+# FACEDETECT_BACKEND_TEST_MODEL and FACEDETECT_BACKEND_TEST_IMAGE; otherwise they
+# auto-skip.
+LD_LIBRARY_PATH="$CURDIR:${LD_LIBRARY_PATH:-}" go test -v -timeout 1200s .
+
+echo "face-detect tests completed."

backend/go/omnivoice-cpp/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # omnivoice.cpp version
 OMNIVOICE_REPO?=https://github.com/ServeurpersoCom/omnivoice.cpp
-OMNIVOICE_VERSION?=2603355a5dfacae5cfc33531d5d0933221843509
+OMNIVOICE_VERSION?=96d30169afd5e6bb3fd6a0e9be0eb505bfe81fcd
 SO_TARGET?=libgomnivoicecpp.so
 
 CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

backend/go/parakeet-cpp/Makefile

@@ -1,6 +1,6 @@
 # parakeet-cpp backend Makefile.
 #
-# Upstream pin lives below as PARAKEET_VERSION?=92a5f0306be354c109150fe58ae4cc4f8a21ca45
+# Upstream pin lives below as PARAKEET_VERSION?=db755a78d39f789bb7d4e3935158a9e8105dbe36
 # (.github/bump_deps.sh) can find and update it - matches the
 # whisper.cpp / ds4 / vibevoice-cpp convention.
 #
@@ -15,7 +15,7 @@
 # That's what the L0 smoke test uses. The default target below does the
 # proper clone-at-pin + cmake build so CI doesn't need a side-checkout.
 
-PARAKEET_VERSION?=92a5f0306be354c109150fe58ae4cc4f8a21ca45
+PARAKEET_VERSION?=db755a78d39f789bb7d4e3935158a9e8105dbe36
 PARAKEET_REPO?=https://github.com/mudler/parakeet.cpp
 
 GOCMD?=go

backend/go/parakeet-cpp/package.sh

@@ -1,23 +1,68 @@
 #!/bin/bash
 #
-# L0 packaging stub: copy the binary, run.sh and libparakeet.so* into
+# Bundle the parakeet-cpp-grpc binary, libparakeet.so, the core runtime
-# package/. The full ldd walk (libc, libstdc++, libgomp, GPU runtimes,
+# libs (libc/libstdc++/libgomp + ld.so) and the GPU runtime for the active
-# arch detection) lands in L3, mirroring backend/go/whisper/package.sh.
+# BUILD_TYPE so the package is self-contained. Mirrors
+# backend/go/whisper/package.sh; run.sh routes the (CGO_ENABLED=0) binary
+# through lib/ld.so so the packaged libc is used instead of the host's.
 
 set -e
 
 CURDIR=$(dirname "$(realpath "$0")")
+REPO_ROOT="${CURDIR}/../../.."
 
 mkdir -p "$CURDIR/package/lib"
 
 cp -avf "$CURDIR/parakeet-cpp-grpc" "$CURDIR/package/"
 cp -avf "$CURDIR/run.sh" "$CURDIR/package/"
 
-# libparakeet.so + any soname symlinks (libparakeet.so.X, libparakeet.so.X.Y).
+# libparakeet.so + any soname symlinks (libparakeet.so.X[.Y]). purego.Dlopen
+# resolves it via LD_LIBRARY_PATH, which run.sh points at lib/.
 cp -avf "$CURDIR"/libparakeet.so* "$CURDIR/package/lib/" 2>/dev/null || {
 	echo "ERROR: libparakeet.so not found in $CURDIR, run 'make' first" >&2
 	exit 1
 }
 
-echo "L0 package layout (full ldd walk lands in L3):"
+# Detect architecture and copy the core runtime libs libparakeet.so links
+# against, plus the matching dynamic loader as lib/ld.so.
+if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
+    echo "Detected x86_64 architecture, copying x86_64 libraries..."
+    cp -arfLv /lib64/ld-linux-x86-64.so.2 "$CURDIR/package/lib/ld.so"
+    cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
+    cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
+    cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
+    cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
+elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
+    echo "Detected ARM64 architecture, copying ARM64 libraries..."
+    cp -arfLv /lib/ld-linux-aarch64.so.1 "$CURDIR/package/lib/ld.so"
+    cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
+    cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
+    cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
+    cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
+elif [ "$(uname -s)" = "Darwin" ]; then
+    echo "Detected Darwin"
+else
+    echo "Error: Could not detect architecture"
+    exit 1
+fi
+
+# Package GPU libraries (CUDA/ROCm/Intel/Vulkan loader + ICDs + drivers)
+# based on BUILD_TYPE so the backend can reach the GPU without the runtime
+# base image shipping those drivers.
+GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
+if [ -f "$GPU_LIB_SCRIPT" ]; then
+    echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
+    source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
+    package_gpu_libs
+fi
+
+echo "Packaging completed successfully"
 ls -liah "$CURDIR/package/" "$CURDIR/package/lib/"