fix(tests): remove duplicate scheduling stubs

Assisted-by: Codex:gpt-5 [golangci-lint]

.agents/ai-coding-assistants.md

@@ -35,19 +35,33 @@ All contributions must comply with LocalAI's licensing requirements:
 
 ## Signed-off-by and Developer Certificate of Origin
 
-**AI agents MUST NOT add `Signed-off-by` tags.** Only humans can legally
-certify the Developer Certificate of Origin (DCO). The human submitter
-is responsible for:
+Only humans can certify the Developer Certificate of Origin (DCO). AI
+agents MUST NOT invent or guess a human identity for `Signed-off-by` —
+doing so forges the DCO certification.
 
-- Reviewing all AI-generated code
+However, when a human operator explicitly directs the AI to commit on
+their behalf, the AI is acting as a typing tool — no different from an
+editor macro or `git commit -s`. In that case the AI SHOULD add
+`Signed-off-by:` using the **configured `user.name` / `user.email`** of
+the current git repository (i.e. the operator's own identity). The
+resulting trailer is the operator's signature; they take responsibility
+for it by reviewing and pushing the commit. The AI MUST NOT use any
+other identity and MUST NOT add its own name to the sign-off.
+
+When running `git commit`, prefer `git commit --signoff` (or `-s`) so
+the trailer is emitted by git itself from the configured identity,
+rather than hand-writing it in a heredoc — this guarantees the sign-off
+matches whatever identity the operator is currently using.
+
+The human submitter remains responsible for:
+
+- Reviewing all AI-generated code before it's pushed or merged
 - Ensuring compliance with licensing requirements
-- Adding their own `Signed-off-by` tag (when the project requires DCO)
-  to certify the contribution
 - Taking full responsibility for the contribution
 
-AI agents MUST NOT add `Co-Authored-By` trailers for themselves either.
-A human reviewer owns the contribution; the AI's involvement is recorded
-via `Assisted-by` (see below).
+AI agents MUST NOT add `Co-Authored-By` trailers for themselves. A human
+reviewer owns the contribution; the AI's involvement is recorded via
+`Assisted-by` (see below).
 
 ## Attribution
 
@@ -84,6 +98,12 @@ Assisted-by: Claude:claude-opus-4-7 golangci-lint
 Signed-off-by: Jane Developer <jane@example.com>
 ```
 
+The `Signed-off-by` line uses Jane's own identity because Jane is the
+submitter operating the AI. If Jane asks Claude to create the commit via
+`git commit -s`, git emits that exact trailer from Jane's configured
+identity — no separate human step is needed beyond Jane reviewing the
+diff before pushing.
+
 ## Scope and Responsibility
 
 Using an AI assistant does not reduce the contributor's responsibility.

.agents/api-endpoints-and-auth.md

@@ -304,9 +304,7 @@ React pages that want to filter the ModelSelector by capability import this symb
 
 ### 4. `docs/content/` (user-facing documentation)
 
-A new capability deserves its own page under `docs/content/features/`, plus cross-links from related features. See the pattern used by `face-recognition.md` / `object-detection.md`.
-
-Announcing it is the release's job, not this page's: the capability gets covered in the release blog post under `website/content/blog/`. See [preparing-a-release.md](preparing-a-release.md). `docs/content/whats-new.md` is only a pointer at the blog and GitHub Releases, so there is nothing to add there.
+A new capability deserves its own page under `docs/content/features/`, plus cross-links from related features and an entry in `docs/content/whats-new.md`. See the pattern used by `face-recognition.md` / `object-detection.md`.
 
 ## Path protection rules
 
@@ -336,7 +334,7 @@ When adding a new endpoint:
 - [ ] Swagger block on the handler: `@Summary`, `@Tags`, `@Param`, `@Success`, `@Router`
 - [ ] If new capability area (new swagger tag): entry in `instructionDefs` in `core/http/endpoints/localai/api_instructions.go` + test count bumped in `api_instructions_test.go`
 - [ ] If new `FLAG_*` usecase flag: matching `CAP_*` symbol exported from `core/http/react-ui/src/utils/capabilities.js`
-- [ ] `docs/content/features/<feature>.md` created; cross-links from related feature pages; capability covered in the release blog post (see [preparing-a-release.md](preparing-a-release.md))
+- [ ] `docs/content/features/<feature>.md` created; cross-links from related feature pages; entry in `docs/content/whats-new.md`
 
 **Quality**
 - [ ] Error responses use `schema.ErrorResponse` format (or `echo.NewHTTPError` with a mapped gRPC status — see the `mapBackendError` helper in `core/http/endpoints/localai/images.go`)

.agents/backend-signing.md

@@ -16,7 +16,8 @@ side (`pkg/oci/cosignverify` plus the gallery YAML).
   per-arch manifest before checking signatures.
 - **Storage:** Signatures are written as OCI 1.1 referrers
   (`--registry-referrers-mode=oci-1-1`) in the new Sigstore bundle format
-  (`--new-bundle-format`). No `:sha256-<hex>.sig` tag clutter.
+  (current cosign releases do this by default; no `--new-bundle-format`
+  flag). No `:sha256-<hex>.sig` tag clutter.
 - **Consumer:** `pkg/oci/cosignverify` discovers the bundle via the
   referrers API, hands it to `sigstore-go`, and verifies it against the
   policy declared in the gallery YAML (`Gallery.Verification`).
@@ -33,15 +34,14 @@ to sign. The job needs:
 
 - `permissions: { id-token: write, contents: read }` at the job level so
   the runner can exchange its GitHub OIDC token for a Fulcio cert.
-- `sigstore/cosign-installer@v3` step (the pinned cosign v2 release needs
-  `--new-bundle-format` explicitly).
+- `sigstore/cosign-installer@v3` step (current cosign releases already
+  default to the new bundle format).
 - After each `docker buildx imagetools create`, resolve the resulting
   list digest with `docker buildx imagetools inspect <tag> --format
   '{{.Manifest.Digest}}'` and sign:
 
 ```sh
 cosign sign --yes --recursive \
-  --new-bundle-format \
   --registry-referrers-mode=oci-1-1 \
   "${REGISTRY_REPO}@${DIGEST}"
 ```
@@ -70,7 +70,7 @@ entry (`backend/index.yaml`):
   url: github:mudler/LocalAI/backend/index.yaml@master
   verification:
     issuer: "https://token.actions.githubusercontent.com"
-    identity_regex: "^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@refs/(heads/master|tags/.+)$"
+    identity_regex: "^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@refs/heads/master$"
     # Optional revocation cutoff; advance during incident response.
     # not_before: "2026-06-01T00:00:00Z"
 ```

.docker/llama-cpp-build-target.sh

@@ -8,15 +8,8 @@ build_type=${2-}
 # ggml-cpu/arch/x86/repack.cpp at -march=sapphirerapids: the job sits on that one
 # translation unit until GitHub kills it at 6h. gcc builds the same file in
 # seconds, so only the SYCL images have to give up the CPU variant matrix.
-#
-# ROCm runs out of the same 6h budget for a different reason: volume, not a
-# stall. hipcc compiles ggml's HIP kernels once per entry in AMDGPU_TARGETS,
-# which is eleven architectures (gfx908 through gfx1201), and the CPU variant
-# matrix lands on top of that. The job built in 2h27m before it was added and
-# has been killed at exactly 6h00m on every run since, so no ROCm llama-cpp
-# image has been published since 2026-08-01.
 case "$build_type" in
-  sycl*|hipblas*)
+  sycl*)
     echo llama-cpp-fallback
     exit 0
     ;;

.github/backend-matrix.yml

@@ -480,6 +480,22 @@ include:
     dockerfile: "./backend/Dockerfile.turboquant"
     context: "./"
     ubuntu-version: '2404'
+  - build-type: 'cublas'
+    cuda-major-version: "12"
+    cuda-minor-version: "8"
+    platforms: 'linux/amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-nvidia-cuda-12-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-cuda-12-amd64'
+    # bigger-runner: same rationale as -gpu-nvidia-cuda-12-llama-cpp above
+    # (observed 6h5m wall-clock on v4.2.1, just past the 6h job timeout).
+    runs-on: 'bigger-runner'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
   - build-type: 'cublas'
     cuda-major-version: "12"
     cuda-minor-version: "8"
@@ -1165,6 +1181,21 @@ include:
     dockerfile: "./backend/Dockerfile.turboquant"
     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-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-cuda-13-amd64'
+    # bigger-runner: observed 6h5m wall-clock on v4.2.1 — at the GHA timeout.
+    runs-on: 'bigger-runner'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
   - build-type: 'cublas'
     cuda-major-version: "13"
     cuda-minor-version: "0"
@@ -1208,6 +1239,20 @@ include:
     backend: "turboquant"
     dockerfile: "./backend/Dockerfile.turboquant"
     context: "./"
+  - 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-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-cuda-13-arm64'
+    base-image: "ubuntu:24.04"
+    runs-on: 'ubuntu-24.04-arm'
+    ubuntu-version: '2404'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
   - build-type: 'cublas'
     cuda-major-version: "13"
     cuda-minor-version: "0"
@@ -2477,6 +2522,20 @@ include:
     dockerfile: "./backend/Dockerfile.turboquant"
     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-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-intel-amd64'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
   - build-type: 'sycl_f32'
     cuda-major-version: ""
     cuda-minor-version: ""
@@ -2519,6 +2578,20 @@ include:
     dockerfile: "./backend/Dockerfile.turboquant"
     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-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-intel-amd64'
+    runs-on: 'ubuntu-latest'
+    base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
   - build-type: 'sycl_f16'
     cuda-major-version: ""
     cuda-minor-version: ""
@@ -2985,6 +3058,21 @@ include:
     dockerfile: "./backend/Dockerfile.turboquant"
     context: "./"
     ubuntu-version: '2404'
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-amd64'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
   - build-type: ''
     cuda-major-version: ""
     cuda-minor-version: ""
@@ -3015,6 +3103,21 @@ include:
     dockerfile: "./backend/Dockerfile.turboquant"
     context: "./"
     ubuntu-version: '2404'
+  - build-type: ''
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-cpu-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-arm64'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
   - build-type: ''
     cuda-major-version: ""
     cuda-minor-version: ""
@@ -3276,6 +3379,20 @@ include:
     dockerfile: "./backend/Dockerfile.turboquant"
     context: "./"
     ubuntu-version: '2204'
+  - 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-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-l4t-cuda-12-arm64'
+    base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
+    runs-on: 'ubuntu-24.04-arm'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2204'
   - build-type: 'cublas'
     cuda-major-version: "12"
     cuda-minor-version: "0"
@@ -3336,6 +3453,22 @@ include:
     context: "./"
     ubuntu-version: '2404'
   # Stablediffusion-ggml
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/amd64'
+    platform-tag: 'amd64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-vulkan-amd64'
+    runs-on: 'ubuntu-latest'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
+  # Stablediffusion-ggml
   - build-type: 'vulkan'
     cuda-major-version: ""
     cuda-minor-version: ""
@@ -3368,6 +3501,22 @@ include:
     context: "./"
     ubuntu-version: '2404'
   # Stablediffusion-ggml
+  - build-type: 'vulkan'
+    cuda-major-version: ""
+    cuda-minor-version: ""
+    platforms: 'linux/arm64'
+    platform-tag: 'arm64'
+    tag-latest: 'auto'
+    tag-suffix: '-gpu-vulkan-buun-llama-cpp'
+    builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-vulkan-arm64'
+    runs-on: 'ubuntu-24.04-arm'
+    base-image: "ubuntu:24.04"
+    skip-drivers: 'false'
+    backend: "buun-llama-cpp"
+    dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+    context: "./"
+    ubuntu-version: '2404'
+  # Stablediffusion-ggml
   - build-type: 'vulkan'
     cuda-major-version: ""
     cuda-minor-version: ""

.github/workflows/backend_merge.yml

@@ -71,8 +71,8 @@ jobs:
 
       # cosign signs each pushed manifest list with --recursive so the
       # index and every per-arch entry get an attached Sigstore bundle.
-      # The pinned cosign v2 release needs --new-bundle-format explicitly;
-      # the verifier only consumes OCI 1.1 Sigstore bundle referrers.
+      # Recent cosign releases always emit the new bundle format, so
+      # there's no extra CLI flag to opt into it.
       - name: Install cosign
         if: github.event_name != 'pull_request'
         uses: sigstore/cosign-installer@v3
@@ -159,7 +159,6 @@ jobs:
           # manifest before checking signatures need the per-arch
           # signatures, not just the list-level one.
           cosign sign --yes --recursive \
-            --new-bundle-format \
             --registry-referrers-mode=oci-1-1 \
             "quay.io/go-skynet/local-ai-backends@${digest}"
 
@@ -186,7 +185,6 @@ jobs:
           ' <<< "$DOCKER_METADATA_OUTPUT_JSON")
           digest=$(docker buildx imagetools inspect "$first_tag" --format '{{.Manifest.Digest}}')
           cosign sign --yes --recursive \
-            --new-bundle-format \
             --registry-referrers-mode=oci-1-1 \
             "localai/localai-backends@${digest}"
 

.github/workflows/test-extra.yml

@@ -33,6 +33,7 @@ jobs:
       llama-cpp: ${{ steps.detect.outputs.llama-cpp }}
       ik-llama-cpp: ${{ steps.detect.outputs.ik-llama-cpp }}
       turboquant: ${{ steps.detect.outputs.turboquant }}
+      buun-llama-cpp: ${{ steps.detect.outputs['buun-llama-cpp'] }}
       vllm: ${{ steps.detect.outputs.vllm }}
       sglang: ${{ steps.detect.outputs.sglang }}
       acestep-cpp: ${{ steps.detect.outputs.acestep-cpp }}
@@ -716,6 +717,30 @@ jobs:
       - name: Build turboquant backend image and run gRPC e2e tests
         run: |
           make test-extra-backend-turboquant
+  tests-buun-llama-cpp-grpc:
+    needs: detect-changes
+    if: needs.detect-changes.outputs['buun-llama-cpp'] == 'true' || needs.detect-changes.outputs.run-all == 'true'
+    runs-on: ubuntu-latest
+    timeout-minutes: 90
+    steps:
+      - name: Clone
+        uses: actions/checkout@v6
+        with:
+          submodules: true
+      - name: Setup Go
+        uses: actions/setup-go@v5
+        with:
+          go-version: '1.25.4'
+      # Exercises the buun-llama-cpp (fork-of-a-fork) backend with the
+      # fork-specific TurboQuant/TCQ KV-cache types. BACKEND_TEST_CACHE_TYPE_V
+      # is set to turbo3 so the test round-trips through the fork's KV
+      # allow-list — picking a stock llama.cpp type would only re-test the
+      # shared code path. DFlash speculative decoding is not exercised here
+      # because the one known public target/drafter pair (Qwen3.5-27B) is too
+      # large for CI.
+      - name: Build buun-llama-cpp backend image and run gRPC e2e tests
+        run: |
+          make test-extra-backend-buun-llama-cpp
   # tests-vllm-grpc is currently disabled in CI.
   #
   # The prebuilt vllm CPU wheel is compiled with AVX-512 VNNI/BF16

Makefile

@@ -1,4 +1,5 @@
 # Disable parallel execution for backend builds
+.NOTPARALLEL: backends/buun-llama-cpp
 .NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/bonsai backends/outetts backends/piper backends/stablediffusion-ggml backends/trellis2cpp backends/trellis2cpp-darwin backends/whisper backends/crispasr backends/parakeet-cpp backends/moss-transcribe-cpp backends/faster-whisper backends/silero-vad backends/local-store backends/valkey-store backends/cloud-proxy backends/huggingface backends/rfdetr backends/rfdetr-cpp backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/longcat-video backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/moss-tts-cpp backends/magpie-tts-cpp backends/vllm-cpp backends/omnivoice-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio backends/supertonic backends/depth-anything-cpp backends/privacy-filter backends/privacy-filter-darwin backends/audio-cpp backends/audio-cpp-darwin
 
 GOCMD=go
@@ -748,6 +749,19 @@ test-extra-backend-bonsai: docker-build-bonsai
 	BACKEND_TEST_MODEL_URL=https://huggingface.co/prism-ml/Bonsai-8B-gguf/resolve/main/Bonsai-8B-Q1_0.gguf \
 	$(MAKE) test-extra-backend
 
+## buun-llama-cpp: exercises the fork-of-a-fork backend (spiritbuun/buun-llama-cpp)
+## with the *TurboQuant/TCQ-specific* KV-cache types (turbo3 for V). Same rationale
+## as turboquant above: picking a standard llama.cpp type would only re-test the
+## shared code path. buun inherits turboquant's turbo2/turbo3/turbo4 and adds
+## turbo2_tcq / turbo3_tcq on top. DFlash speculative decoding is not exercised
+## here because no small DFlash drafter model exists (the known public pair is
+## Qwen3.5-27B, ~54 GB).
+test-extra-backend-buun-llama-cpp: docker-build-buun-llama-cpp
+	BACKEND_IMAGE=local-ai-backend:buun-llama-cpp \
+	BACKEND_TEST_CACHE_TYPE_K=q8_0 \
+	BACKEND_TEST_CACHE_TYPE_V=turbo3 \
+	$(MAKE) test-extra-backend
+
 ## Audio transcription wrapper for the llama-cpp backend.
 ## Drives the new AudioTranscription / AudioTranscriptionStream RPCs against
 ## ggml-org/Qwen3-ASR-0.6B-GGUF (a small ASR model that requires its mmproj
@@ -1284,6 +1298,11 @@ BACKEND_PRIVACY_FILTER = privacy-filter|privacy-filter|.|false|false
 # against apt gRPC/protobuf rather than a prebuilt base-grpc image; the reason
 # is on the audio-cpp block in .github/backend-matrix.yml.
 BACKEND_AUDIO_CPP = audio-cpp|audio-cpp|.|false|false
+# buun-llama-cpp is a fork-of-a-fork (spiritbuun/buun-llama-cpp forks
+# TheTom/llama-cpp-turboquant) that adds DFlash block-diffusion speculative
+# decoding and extra TCQ KV-cache variants on top of TurboQuant. Same thin
+# wrapper pattern as turboquant — reuses backend/cpp/llama-cpp grpc-server.
+BACKEND_BUUN_LLAMA_CPP = buun-llama-cpp|buun-llama-cpp|.|false|false
 
 # Golang backends
 BACKEND_PIPER = piper|golang|.|false|true
@@ -1388,6 +1407,7 @@ $(eval $(call generate-docker-build-target,$(BACKEND_BONSAI)))
 $(eval $(call generate-docker-build-target,$(BACKEND_DS4)))
 $(eval $(call generate-docker-build-target,$(BACKEND_PRIVACY_FILTER)))
 $(eval $(call generate-docker-build-target,$(BACKEND_AUDIO_CPP)))
+$(eval $(call generate-docker-build-target,$(BACKEND_BUUN_LLAMA_CPP)))
 $(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
 $(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
 $(eval $(call generate-docker-build-target,$(BACKEND_VALKEY_STORE)))
@@ -1456,7 +1476,7 @@ $(eval $(call generate-docker-build-target,$(BACKEND_SUPERTONIC)))
 docker-save-%: backend-images
 	docker save local-ai-backend:$* -o backend-images/$*.tar
 
-docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-bonsai docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-longcat-video docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-crispasr docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-rfdetr-cpp docker-build-qwen3-tts-cpp docker-build-moss-tts-cpp docker-build-magpie-tts-cpp docker-build-vllm-cpp docker-build-omnivoice-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx docker-build-cloud-proxy docker-build-supertonic docker-build-depth-anything-cpp docker-build-moss-transcribe-cpp docker-build-privacy-filter docker-build-trellis2cpp docker-build-valkey-store docker-build-audio-cpp
+docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-buun-llama-cpp docker-build-bonsai docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-longcat-video docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-crispasr docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-rfdetr-cpp docker-build-qwen3-tts-cpp docker-build-moss-tts-cpp docker-build-magpie-tts-cpp docker-build-vllm-cpp docker-build-omnivoice-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx docker-build-cloud-proxy docker-build-supertonic docker-build-depth-anything-cpp docker-build-moss-transcribe-cpp docker-build-privacy-filter docker-build-trellis2cpp docker-build-valkey-store docker-build-audio-cpp
 
 ########################################################
 ### Mock Backend for E2E Tests

README.md

@@ -195,7 +195,7 @@ For more details, see the [Getting Started guide](https://localai.io/basics/gett
 - **August 2025**: MLX, MLX-VLM, Diffusers, llama.cpp now supported on Apple Silicon
 - **July 2025**: All backends migrated outside the main binary — [lightweight, modular architecture](https://github.com/mudler/LocalAI/releases/tag/v3.2.0)
 
-For older news and full release notes, see [GitHub Releases](https://github.com/mudler/LocalAI/releases) and the [blog](https://localai.io/blog/).
+For older news and full release notes, see [GitHub Releases](https://github.com/mudler/LocalAI/releases) and the [News page](https://localai.io/basics/news/).
 
 ## Features
 
@@ -260,7 +260,7 @@ We also maintain [apex-quant](https://github.com/localai-org/apex-quant), a per-
 - [Kubernetes installation](https://localai.io/basics/getting_started/#run-localai-in-kubernetes)
 - [Integrations & community projects](https://localai.io/docs/integrations/)
 - [Installation video walkthrough](https://www.youtube.com/watch?v=cMVNnlqwfw4)
-- [Blog: release write-ups, benchmarks and engineering notes](https://localai.io/blog/)
+- [Media & blog posts](https://localai.io/basics/news/#media-blogs-social)
 - [Examples](https://github.com/mudler/LocalAI-examples) — including the [realtime voice assistant demo](https://github.com/localai-org/localai-realtime-demo) (Go client for the Realtime API with tool calling)
 
 ## Team

backend/Dockerfile.buun-llama-cpp

@@ -0,0 +1,290 @@
+ARG BASE_IMAGE=ubuntu:24.04
+ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
+
+
+# The grpc target does one thing, it builds and installs GRPC.  This is in it's own layer so that it can be effectively cached by CI.
+# You probably don't need to change anything here, and if you do, make sure that CI is adjusted so that the cache continues to work.
+FROM ${GRPC_BASE_IMAGE} AS grpc
+
+# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
+ARG GRPC_MAKEFLAGS="-j4 -Otarget"
+ARG GRPC_VERSION=v1.65.0
+ARG CMAKE_FROM_SOURCE=false
+# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
+ARG CMAKE_VERSION=3.31.10
+
+ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
+
+WORKDIR /build
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        ca-certificates \
+        build-essential curl libssl-dev \
+        git wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install CMake (the version in 22.04 is too old)
+RUN <<EOT bash
+    if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
+        curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
+    else
+        apt-get update && \
+        apt-get install -y \
+            cmake && \
+        apt-get clean && \
+        rm -rf /var/lib/apt/lists/*
+    fi
+EOT
+
+# We install GRPC to a different prefix here so that we can copy in only the build artifacts later
+# saves several hundred MB on the final docker image size vs copying in the entire GRPC source tree
+# and running make install in the target container
+RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
+    mkdir -p /build/grpc/cmake/build && \
+    cd /build/grpc/cmake/build && \
+    sed -i "216i\  TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt" && \
+    cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../.. && \
+    make && \
+    make install && \
+    rm -rf /build
+
+FROM ${BASE_IMAGE} AS builder
+ARG CMAKE_FROM_SOURCE=false
+ARG CMAKE_VERSION=3.31.10
+# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
+ARG CUDA_DOCKER_ARCH
+ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
+ARG CMAKE_ARGS
+ENV CMAKE_ARGS=${CMAKE_ARGS}
+ARG BACKEND=rerankers
+ARG BUILD_TYPE
+ENV BUILD_TYPE=${BUILD_TYPE}
+ARG CUDA_MAJOR_VERSION
+ARG CUDA_MINOR_VERSION
+ARG SKIP_DRIVERS=false
+ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION}
+ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
+ENV DEBIAN_FRONTEND=noninteractive
+ARG TARGETARCH
+ARG TARGETVARIANT
+ARG GO_VERSION=1.25.4
+ARG UBUNTU_VERSION=2404
+
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        build-essential \
+        ccache git \
+        ca-certificates \
+        make \
+        pkg-config libcurl4-openssl-dev \
+        curl unzip \
+        libssl-dev wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Cuda
+ENV PATH=/usr/local/cuda/bin:${PATH}
+
+# HipBLAS requirements
+ENV PATH=/opt/rocm/bin:${PATH}
+
+
+# Vulkan requirements
+RUN <<EOT bash
+    if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
+        apt-get update && \
+        apt-get install -y  --no-install-recommends \
+            software-properties-common pciutils wget gpg-agent && \
+        apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
+            libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
+            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
+        if [ "amd64" = "$TARGETARCH" ]; then
+            wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
+            tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
+            mkdir -p /opt/vulkan-sdk && \
+            mv 1.4.335.0 /opt/vulkan-sdk/ && \
+            cd /opt/vulkan-sdk/1.4.335.0 && \
+            ./vulkansdk --no-deps --maxjobs \
+                vulkan-loader \
+                vulkan-validationlayers \
+                vulkan-extensionlayer \
+                vulkan-tools \
+                shaderc && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
+            cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
+            rm -rf /opt/vulkan-sdk
+        fi
+        if [ "arm64" = "$TARGETARCH" ]; then
+            mkdir vulkan && cd vulkan && \
+            curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
+            tar -xvf vulkan-sdk.tar.xz && \
+            rm vulkan-sdk.tar.xz && \
+            cd 1.4.335.0 && \
+            cp -rfv aarch64/bin/* /usr/bin/ && \
+            cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
+            cp -rfv aarch64/include/* /usr/include/ && \
+            cp -rfv aarch64/share/* /usr/share/ && \
+            cd ../.. && \
+            rm -rf vulkan
+        fi
+        ldconfig && \
+        apt-get clean && \
+        rm -rf /var/lib/apt/lists/*
+    fi
+EOT
+
+# CuBLAS requirements
+RUN <<EOT bash
+    if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
+        apt-get update && \
+        apt-get install -y  --no-install-recommends \
+            software-properties-common pciutils
+        if [ "amd64" = "$TARGETARCH" ]; then
+            curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
+        fi
+        if [ "arm64" = "$TARGETARCH" ]; then
+            if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
+            else
+                curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
+            fi
+        fi
+        dpkg -i cuda-keyring_1.1-1_all.deb && \
+        rm -f cuda-keyring_1.1-1_all.deb && \
+        apt-get update && \
+        apt-get install -y --no-install-recommends \
+            cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
+            libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
+            libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
+            libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
+            libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
+            libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
+            apt-get install -y --no-install-recommends \
+            libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
+        fi
+        apt-get clean && \
+        rm -rf /var/lib/apt/lists/*
+    fi
+EOT
+
+
+# https://github.com/NVIDIA/Isaac-GR00T/issues/343
+RUN <<EOT bash
+    if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
+        wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
+        cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
+        wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
+        cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
+        apt-get update && apt-get install -y nvpl
+    fi
+EOT
+
+# If we are building with clblas support, we need the libraries for the builds
+RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
+        apt-get update && \
+        apt-get install -y --no-install-recommends \
+            libclblast-dev && \
+        apt-get clean && \
+        rm -rf /var/lib/apt/lists/* \
+    ; fi
+
+RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
+        apt-get update && \
+        apt-get install -y --no-install-recommends \
+            hipblas-dev \
+            rocblas-dev && \
+        apt-get clean && \
+        rm -rf /var/lib/apt/lists/* && \
+        # I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
+        # to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
+        ldconfig && \
+        # Log which GPU architectures have rocBLAS kernel support
+        echo "rocBLAS library data architectures:" && \
+        (ls /opt/rocm*/lib/rocblas/library/Kernels* 2>/dev/null || ls /opt/rocm*/lib64/rocblas/library/Kernels* 2>/dev/null) | grep -oP 'gfx[0-9a-z+-]+' | sort -u || \
+        echo "WARNING: No rocBLAS kernel data found" \
+    ; fi
+
+RUN echo "TARGETARCH: $TARGETARCH"
+
+# We need protoc installed, and the version in 22.04 is too old.  We will create one as part installing the GRPC build below
+# but that will also being in a newer version of absl which stablediffusion cannot compile with.  This version of protoc is only
+# here so that we can generate the grpc code for the stablediffusion build
+RUN <<EOT bash
+    if [ "amd64" = "$TARGETARCH" ]; then
+        curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-x86_64.zip -o protoc.zip && \
+        unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
+        rm protoc.zip
+    fi
+    if [ "arm64" = "$TARGETARCH" ]; then
+        curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip && \
+        unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
+        rm protoc.zip
+    fi
+EOT
+
+# Install CMake (the version in 22.04 is too old)
+RUN <<EOT bash
+    if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
+        curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
+    else
+        apt-get update && \
+        apt-get install -y \
+            cmake && \
+        apt-get clean && \
+        rm -rf /var/lib/apt/lists/*
+    fi
+EOT
+
+COPY --from=grpc /opt/grpc /usr/local
+
+
+COPY . /LocalAI
+
+RUN <<'EOT' bash
+set -euxo pipefail
+
+if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
+  CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
+  export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
+  echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
+  rm -rf /LocalAI/backend/cpp/buun-llama-cpp-*-build
+fi
+
+cd /LocalAI/backend/cpp/buun-llama-cpp
+
+if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
+  make buun-llama-cpp-fallback
+  make buun-llama-cpp-grpc
+  make buun-llama-cpp-rpc-server
+else
+  make buun-llama-cpp-avx
+  make buun-llama-cpp-avx2
+  make buun-llama-cpp-avx512
+  make buun-llama-cpp-fallback
+  make buun-llama-cpp-grpc
+  make buun-llama-cpp-rpc-server
+fi
+EOT
+
+
+# Copy libraries using a script to handle architecture differences
+RUN make -BC /LocalAI/backend/cpp/buun-llama-cpp package
+
+
+FROM scratch
+
+
+# Copy all available binaries (the build process only creates the appropriate ones for the target architecture)
+COPY --from=builder /LocalAI/backend/cpp/buun-llama-cpp/package/. ./

backend/cpp/audio-cpp/Makefile

@@ -9,7 +9,7 @@
 # recipe is a make target (not a prepare.sh) so 'make purge && make' is a clean
 # rebuild and so the bump bot can see the pin.
 
-AUDIO_CPP_VERSION?=238ab6a9e321c17de8e120559f57efeedaeb1345
+AUDIO_CPP_VERSION?=5a8312ef7b8aa7cf14e9a24ac568cabd8725d68a
 AUDIO_CPP_REPO?=https://github.com/0xShug0/audio.cpp
 
 CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))

backend/cpp/buun-llama-cpp/Makefile

@@ -0,0 +1,92 @@
+
+# Pinned to the HEAD of master on https://github.com/spiritbuun/buun-llama-cpp.
+# Auto-bumped nightly by .github/workflows/bump_deps.yaml.
+BUUN_LLAMA_VERSION?=22464d0848b87c5d56b52fdf6af2e5da46bf803e
+LLAMA_REPO?=https://github.com/spiritbuun/buun-llama-cpp
+
+CMAKE_ARGS?=
+BUILD_TYPE?=
+NATIVE?=false
+ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
+TARGET?=--target grpc-server
+JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
+ARCH?=$(shell uname -m)
+
+CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
+LLAMA_CPP_DIR := $(CURRENT_MAKEFILE_DIR)/../llama-cpp
+
+GREEN := \033[0;32m
+RESET := \033[0m
+
+# buun-llama-cpp is a llama.cpp fork-of-a-fork (spiritbuun/buun-llama-cpp forked
+# TheTom/llama-cpp-turboquant, which itself forked ggml-org/llama.cpp). Rather
+# than duplicating grpc-server.cpp / CMakeLists.txt / prepare.sh we reuse the
+# ones in backend/cpp/llama-cpp, and only swap which repo+sha the fetch step
+# pulls. Each flavor target copies ../llama-cpp into a sibling
+# ../buun-llama-cpp-<flavor>-build directory, then invokes llama-cpp's own
+# build-llama-cpp-grpc-server with LLAMA_REPO/LLAMA_VERSION overridden to point
+# at the fork.
+PATCHES_DIR := $(CURRENT_MAKEFILE_DIR)/patches
+
+# Each flavor target:
+#   1. copies backend/cpp/llama-cpp/ (grpc-server.cpp + prepare.sh + CMakeLists.txt + Makefile)
+#      into a sibling buun-llama-cpp-<flavor>-build directory;
+#   2. clones the buun fork into buun-llama-cpp-<flavor>-build/llama.cpp via the
+#      copy's own `llama.cpp` target, overriding LLAMA_REPO/LLAMA_VERSION;
+#   3. applies patches from backend/cpp/buun-llama-cpp/patches/ to the cloned
+#      fork sources (for backporting upstream commits the fork hasn't pulled);
+#   4. runs the copy's `grpc-server` target, which produces the binary we copy
+#      up as buun-llama-cpp-<flavor>.
+define buun-llama-cpp-build
+	rm -rf $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build
+	cp -rf $(LLAMA_CPP_DIR) $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build
+	# Stock llama.cpp patches target upstream and may not apply to this fork.
+	# The buun-specific compatibility series is applied explicitly below.
+	rm -rf $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/patches
+	$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build purge
+	# Augment the copied grpc-server.cpp's KV-cache allow-list with the
+	# fork's turbo2/turbo3/turbo4/turbo2_tcq/turbo3_tcq types and wire up the
+	# DFlash-specific option handlers (tree_budget / draft_topk). We patch the
+	# *copy*, never the original under backend/cpp/llama-cpp/, so the stock
+	# llama-cpp build stays compiling against vanilla upstream.
+	bash $(CURRENT_MAKEFILE_DIR)/patch-grpc-server.sh $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/grpc-server.cpp
+	bash $(LLAMA_CPP_DIR)/disable-score-task.sh $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/grpc-server.cpp
+	$(info $(GREEN)I buun-llama-cpp build info:$(1)$(RESET))
+	LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(BUUN_LLAMA_VERSION) \
+	$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build llama.cpp
+	bash $(CURRENT_MAKEFILE_DIR)/apply-patches.sh $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/llama.cpp $(PATCHES_DIR)
+	CMAKE_ARGS="$(CMAKE_ARGS) $(2)" TARGET="$(3)" \
+	LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(BUUN_LLAMA_VERSION) \
+	$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build grpc-server
+	cp -rfv $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/grpc-server buun-llama-cpp-$(1)
+endef
+
+buun-llama-cpp-avx2:
+	$(call buun-llama-cpp-build,avx2,-DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
+
+buun-llama-cpp-avx512:
+	$(call buun-llama-cpp-build,avx512,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
+
+buun-llama-cpp-avx:
+	$(call buun-llama-cpp-build,avx,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
+
+buun-llama-cpp-fallback:
+	$(call buun-llama-cpp-build,fallback,-DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
+
+buun-llama-cpp-grpc:
+	$(call buun-llama-cpp-build,grpc,-DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server --target rpc-server)
+
+buun-llama-cpp-rpc-server: buun-llama-cpp-grpc
+	cp -rf $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-grpc-build/llama.cpp/build/bin/rpc-server buun-llama-cpp-rpc-server
+
+package:
+	bash package.sh
+
+test:
+	bash test-patch-grpc-server.sh
+
+purge:
+	rm -rf $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-*-build
+	rm -rf buun-llama-cpp-* package
+
+clean: purge

backend/cpp/buun-llama-cpp/apply-patches.sh

@@ -0,0 +1,50 @@
+#!/bin/bash
+# Apply the buun-llama-cpp patch series to a cloned buun-llama-cpp checkout.
+#
+# buun-llama-cpp is a fork-of-a-fork that branched off upstream llama.cpp
+# before some API changes the shared backend/cpp/llama-cpp/grpc-server.cpp
+# depends on. We carry those upstream commits as patch files under
+# backend/cpp/buun-llama-cpp/patches/ and apply them here so the reused
+# grpc-server source compiles against the fork unmodified.
+#
+# Drop the corresponding patch from patches/ whenever the fork catches up with
+# upstream — the build will fail fast if a patch stops applying, which is the
+# signal to retire it.
+
+set -euo pipefail
+
+if [[ $# -ne 2 ]]; then
+    echo "usage: $0 <llama.cpp-src-dir> <patches-dir>" >&2
+    exit 2
+fi
+
+SRC_DIR=$1
+PATCHES_DIR=$2
+
+if [[ ! -d "$SRC_DIR" ]]; then
+    echo "source dir does not exist: $SRC_DIR" >&2
+    exit 2
+fi
+
+if [[ ! -d "$PATCHES_DIR" ]]; then
+    echo "no patches dir at $PATCHES_DIR, nothing to apply"
+    exit 0
+fi
+
+shopt -s nullglob
+patches=("$PATCHES_DIR"/*.patch)
+shopt -u nullglob
+
+if [[ ${#patches[@]} -eq 0 ]]; then
+    echo "no .patch files in $PATCHES_DIR, nothing to apply"
+    exit 0
+fi
+
+cd "$SRC_DIR"
+
+for patch in "${patches[@]}"; do
+    echo "==> applying $patch"
+    git apply --verbose "$patch"
+done
+
+echo "all buun-llama-cpp patches applied successfully"

backend/cpp/buun-llama-cpp/package.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+
+# Script to copy the appropriate libraries based on architecture
+# This script is used in the final stage of the Dockerfile
+
+set -e
+
+CURDIR=$(dirname "$(realpath $0)")
+REPO_ROOT="${CURDIR}/../../.."
+
+# Create lib directory
+mkdir -p $CURDIR/package/lib
+
+cp -avrf $CURDIR/buun-llama-cpp-* $CURDIR/package/
+cp -rfv $CURDIR/run.sh $CURDIR/package/
+
+# Detect architecture and copy appropriate libraries
+if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
+    # x86_64 architecture
+    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
+    # ARM64 architecture
+    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 $CURDIR/package/lib/libpthread.so.0
+else
+    echo "Error: Could not detect architecture"
+    exit 1
+fi
+
+# Package GPU libraries based on BUILD_TYPE
+GPU_LIB_SCRIPT="${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/
+ls -liah $CURDIR/package/lib/

backend/cpp/buun-llama-cpp/patch-grpc-server.sh

@@ -0,0 +1,196 @@
+#!/bin/bash
+# Patch the shared backend/cpp/llama-cpp/grpc-server.cpp *copy* used by the
+# buun-llama-cpp build to account for three gaps between upstream and the fork:
+#
+#   1. Augment the kv_cache_types[] allow-list so `LoadModel` accepts the
+#      fork-specific `turbo2` / `turbo3` / `turbo4` cache types plus the buun
+#      additions `turbo2_tcq` / `turbo3_tcq`.
+#
+#   2. Adapt the post-refactor speculative-decoding fields and option handlers
+#      to the fork's legacy flat common_params_speculative layout, while adding
+#      buun-exclusive tree_budget / draft_topk support.
+#      These reference struct fields (common_params.speculative.tree_budget
+#      and .draft_topk) that only exist in buun's common/common.h — adding
+#      them to the shared backend/cpp/llama-cpp/grpc-server.cpp would break
+#      the stock llama-cpp build, so we inject them only into the buun copy.
+#
+#   3. Replace `get_media_marker()` (added upstream in ggml-org/llama.cpp#21962,
+#      server-side random per-instance marker) with the legacy "<__media__>"
+#      literal. The fork branched before that PR, so server-common.cpp has no
+#      get_media_marker symbol. The fork's mtmd_default_marker() still returns
+#      "<__media__>", and Go-side tooling falls back to that sentinel when the
+#      backend does not expose media_marker, so substituting the literal keeps
+#      behavior identical on the buun path.
+#
+# We patch the *copy* sitting in buun-llama-cpp-<flavor>-build/, never the
+# original under backend/cpp/llama-cpp/, so the stock llama-cpp build keeps
+# compiling against vanilla upstream.
+#
+# Idempotent: skips each insertion if its marker is already present (so re-runs
+# of the same build dir don't double-insert).
+
+set -euo pipefail
+
+if [[ $# -ne 1 ]]; then
+    echo "usage: $0 <grpc-server.cpp>" >&2
+    exit 2
+fi
+
+SRC=$1
+
+if [[ ! -f "$SRC" ]]; then
+    echo "grpc-server.cpp not found at $SRC" >&2
+    exit 2
+fi
+
+if grep -q 'GGML_TYPE_TURBO2_TCQ' "$SRC"; then
+    echo "==> $SRC already has buun cache types, skipping KV allow-list patch"
+else
+    echo "==> patching $SRC to allow turbo2/turbo3/turbo4/turbo2_tcq/turbo3_tcq KV-cache types"
+
+    # Insert the five TURBO entries right after the first `    GGML_TYPE_Q5_1,`
+    # line (the kv_cache_types[] allow-list). Using awk because the builder
+    # image does not ship python3, and GNU sed's multi-line `a\` quoting is
+    # awkward.
+    awk '
+        /^    GGML_TYPE_Q5_1,$/ && !done {
+            print
+            print "    // buun-llama-cpp fork extras — added by patch-grpc-server.sh"
+            print "    GGML_TYPE_TURBO2_0,"
+            print "    GGML_TYPE_TURBO3_0,"
+            print "    GGML_TYPE_TURBO4_0,"
+            print "    GGML_TYPE_TURBO2_TCQ,"
+            print "    GGML_TYPE_TURBO3_TCQ,"
+            done = 1
+            next
+        }
+        { print }
+        END {
+            if (!done) {
+                print "patch-grpc-server.sh: anchor `    GGML_TYPE_Q5_1,` not found" > "/dev/stderr"
+                exit 1
+            }
+        }
+    ' "$SRC" > "$SRC.tmp"
+    mv "$SRC.tmp" "$SRC"
+
+    echo "==> KV allow-list patch OK"
+fi
+
+if grep -q 'buun-llama-cpp legacy speculative options' "$SRC"; then
+    echo "==> $SRC already has legacy speculative option handlers, skipping"
+else
+    echo "==> replacing modern speculative option handlers with the fork-compatible set"
+
+    # Replace the whole speculative option section. The fork predates chained
+    # speculative types and the nested draft/ngram families, so retaining any
+    # modern-only handler makes the copied server fail at compile time.
+    awk '
+        /} else if \(!strcmp\(optname, "spec_type"\)/ && !done {
+            print "        // buun-llama-cpp legacy speculative options"
+            print "        } else if (!strcmp(optname, \"spec_type\") || !strcmp(optname, \"speculative_type\")) {"
+            print "            auto type = common_speculative_type_from_name(optval_str.substr(0, optval_str.find(\",\")));"
+            print "            if (type != COMMON_SPECULATIVE_TYPE_COUNT) params.speculative.type = type;"
+            print "        } else if (!strcmp(optname, \"spec_n_max\") || !strcmp(optname, \"draft_max\")) {"
+            print "            if (optval != NULL) { try { params.speculative.n_max = std::stoi(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"spec_n_min\") || !strcmp(optname, \"draft_min\")) {"
+            print "            if (optval != NULL) { try { params.speculative.n_min = std::stoi(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"spec_p_min\") || !strcmp(optname, \"draft_p_min\")) {"
+            print "            if (optval != NULL) { try { params.speculative.p_min = std::stof(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"spec_p_split\")) {"
+            print "            if (optval != NULL) { try { params.speculative.p_split = std::stof(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"spec_ngram_size_n\") || !strcmp(optname, \"ngram_size_n\")) {"
+            print "            if (optval != NULL) { try { params.speculative.ngram_size_n = (uint16_t)std::stoi(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"spec_ngram_size_m\") || !strcmp(optname, \"ngram_size_m\")) {"
+            print "            if (optval != NULL) { try { params.speculative.ngram_size_m = (uint16_t)std::stoi(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"spec_ngram_min_hits\") || !strcmp(optname, \"ngram_min_hits\")) {"
+            print "            if (optval != NULL) { try { params.speculative.ngram_min_hits = (uint16_t)std::stoi(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"draft_gpu_layers\")) {"
+            print "            if (optval != NULL) { try { params.speculative.n_gpu_layers = std::stoi(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"tree_budget\")) {"
+            print "            if (optval != NULL) { try { params.speculative.tree_budget = std::stoi(optval_str); } catch (...) {} }"
+            print "        } else if (!strcmp(optname, \"draft_topk\")) {"
+            print "            if (optval != NULL) { try { params.speculative.draft_topk = std::stoi(optval_str); } catch (...) {} }"
+            skipping = 1
+            next
+        }
+        skipping && /^    }$/ { skipping = 0; done = 1; print; next }
+        !skipping { print }
+        END {
+            if (!done) {
+                print "patch-grpc-server.sh: speculative option section not found" > "/dev/stderr"
+                exit 1
+            }
+        }
+    ' "$SRC" > "$SRC.tmp"
+    mv "$SRC.tmp" "$SRC"
+
+    echo "==> legacy speculative option-handler patch OK"
+fi
+
+# The modern server initializes a vector of speculative types when DraftModel
+# is present. The fork still exposes a single enum value.
+awk '
+    /const bool no_spec_type = params\.speculative\.types\.empty\(\)/ && !done {
+        print "        if (params.speculative.type == COMMON_SPECULATIVE_TYPE_NONE) {"
+        print "            params.speculative.type = COMMON_SPECULATIVE_TYPE_DRAFT;"
+        print "        }"
+        skipping = 1
+        next
+    }
+    skipping && /^        }$/ { skipping = 0; done = 1; next }
+    !skipping { print }
+' "$SRC" > "$SRC.tmp"
+mv "$SRC.tmp" "$SRC"
+
+# Map supported post-refactor fields back to the names used by the pinned fork.
+sed -E \
+    -e 's/params\.speculative\.draft\.mparams\.path/params.speculative.mparams_dft.path/g' \
+    -e 's/params\.speculative\.draft\.n_gpu_layers/params.speculative.n_gpu_layers/g' \
+    -e 's/ctx_server\.impl->model_tgt/ctx_server.impl->model/g' \
+    -e '/params\.cache_idle_slots =/d' \
+    -e '/params\.split_mode = LLAMA_SPLIT_MODE_TENSOR;/d' \
+    -e '/params\.speculative\.draft\.tensor_buft_overrides/d' \
+    "$SRC" > "$SRC.tmp"
+mv "$SRC.tmp" "$SRC"
+
+if ! grep -q '^#define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP' "$SRC"; then
+    sed '0,/^#include/{s/^#include/#define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP 1\n\n#include/}' "$SRC" > "$SRC.tmp"
+    mv "$SRC.tmp" "$SRC"
+fi
+
+if grep -qE 'ctx_server\.get_meta\(\)\.logit_bias_eog|params_base\.sampling\.logit_bias_eog,' "$SRC"; then
+    echo "==> patching $SRC to drop the logit_bias_eog arg from params_from_json_cmpl() callsites (buun still uses the pre-refactor 4-arg signature)"
+    # Upstream llama.cpp refactored params_from_json_cmpl to take a precomputed
+    # logit_bias_eog vector after buun's 2026-04-05 fork-point — simultaneously
+    # adding server_context_meta::logit_bias_eog as the supplier. Buun carries
+    # neither change: its params_from_json_cmpl is still 4-arg, and internally
+    # derives logit_bias_eog from the common_params it's passed. So we just
+    # delete the argument line entirely — the remaining 4 args match buun's
+    # signature and the resulting behavior matches upstream bit-for-bit
+    # (upstream's 5th arg is the same data buun derives internally).
+    #
+    # Guard is broad so this works whether the line has been run through this
+    # block before (leaving params_base.sampling.logit_bias_eog,) or not
+    # (leaving the original ctx_server.get_meta().logit_bias_eog,).
+    sed -E '/^[[:space:]]+(ctx_server\.get_meta\(\)\.logit_bias_eog|params_base\.sampling\.logit_bias_eog),$/d' "$SRC" > "$SRC.tmp"
+    mv "$SRC.tmp" "$SRC"
+    echo "==> logit_bias_eog arg drop OK"
+else
+    echo "==> $SRC has no logit_bias_eog arg line, skipping"
+fi
+
+if grep -q 'get_media_marker()' "$SRC"; then
+    echo "==> patching $SRC to replace get_media_marker() with legacy \"<__media__>\" literal"
+    # Only one call site today (ModelMetadata), but replace all occurrences to
+    # stay robust if upstream adds more. Use a temp file to avoid relying on
+    # sed -i portability (the builder image uses GNU sed, but keeping this
+    # consistent with the awk block above).
+    sed 's/get_media_marker()/"<__media__>"/g' "$SRC" > "$SRC.tmp"
+    mv "$SRC.tmp" "$SRC"
+    echo "==> get_media_marker() substitution OK"
+else
+    echo "==> $SRC has no get_media_marker() call, skipping media-marker patch"
+fi
+
+echo "==> all patches applied"

backend/cpp/buun-llama-cpp/patches/0001-fattn-atomicAdd-double-shim.patch

@@ -0,0 +1,46 @@
+Subject: [PATCH] ggml-cuda/fattn: provide atomicAdd(double*,double) shim for pre-sm_60
+
+Buun's Q² calibration path in ggml_cuda_turbo_scale_q calls
+  atomicAdd(&d_q_channel_sq_fattn[threadIdx.x], (double)(val * val));
+but native double atomicAdd is only available on compute capability 6.0
+and newer. Compiling against a CUDA arch list that includes older
+architectures (LocalAI's CUDA 12 Docker image builds for the full
+published arch range) fails with:
+
+    fattn.cu(812): error: no instance of overloaded function "atomicAdd"
+      matches the argument list, argument types are: (double *, double)
+
+Add the canonical CUDA-programming-guide shim at the top of fattn.cu so
+pre-sm_60 codegen has a definition to call. On sm_60+ the native CUDA
+intrinsic is used and the shim is elided via __CUDA_ARCH__.
+
+--- a/ggml/src/ggml-cuda/fattn.cu
++++ b/ggml/src/ggml-cuda/fattn.cu
+@@ -7,6 +7,27 @@
+
+ #include <atomic>
+
++// Pre-sm_60 double atomicAdd shim. Native double atomicAdd(double*,double)
++// is only available on CUDA compute capability 6.0+ (see CUDA C Programming
++// Guide, B.15 Atomic Functions). Buun's Q² calibration path below calls
++// atomicAdd with a double*; without this definition, nvcc fails to find a
++// matching overload whenever the compile target list includes pre-sm_60
++// architectures. The standard CAS loop implementation below matches the
++// semantics of the native intrinsic.
++#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 600
++static __device__ double atomicAdd(double * address, double val) {
++    unsigned long long int * address_as_ull = (unsigned long long int *)address;
++    unsigned long long int old = *address_as_ull;
++    unsigned long long int assumed;
++    do {
++        assumed = old;
++        old = atomicCAS(address_as_ull, assumed,
++                        __double_as_longlong(val + __longlong_as_double(assumed)));
++    } while (assumed != old);
++    return __longlong_as_double(old);
++}
++#endif
++
+ // InnerQ: update the fattn-side inverse scale array from host (all devices)
+ void turbo_innerq_update_fattn_scales(const float * scale_inv) {
+     int cur_device;

backend/cpp/buun-llama-cpp/patches/0002-argmax-shfl-xor-sync-add-width.patch

@@ -0,0 +1,32 @@
+Subject: [PATCH] ggml-cuda/argmax: pass WARP_SIZE to the top-K __shfl_xor_sync calls
+
+Two __shfl_xor_sync calls in the top-K intra-warp merge drop the `width`
+argument and rely on the CUDA default (warpSize). Every other call in
+the same file already passes WARP_SIZE explicitly, and the HIP/ROCm
+compatibility shim at ggml/src/ggml-cuda/vendors/hip.h:33 is a 4-arg
+function-like macro — so the 3-arg form fails to preprocess when
+building with hipcc against ROCm:
+
+    argmax.cu:265: error: too few arguments provided to function-like
+      macro invocation
+    note: macro '__shfl_xor_sync' defined here:
+      #define __shfl_xor_sync(mask, var, laneMask, width) \
+              __shfl_xor(var, laneMask, width)
+
+Align the two call sites with the rest of the file by passing WARP_SIZE
+explicitly. On CUDA the generated code is unchanged (warpSize is the
+default); on HIP it now matches the macro's arity.
+
+--- a/ggml/src/ggml-cuda/argmax.cu
++++ b/ggml/src/ggml-cuda/argmax.cu
+@@ -262,8 +262,8 @@
+     // Each step: lane gets partner's min element, if it beats our min, replace and re-heapify
+     for (int offset = WARP_SIZE / 2; offset > 0; offset >>= 1) {
+         for (int i = 0; i < K; i++) {
+-            float partner_val = __shfl_xor_sync(0xFFFFFFFF, heap_val[i], offset);
+-            int partner_idx = __shfl_xor_sync(0xFFFFFFFF, heap_idx[i], offset);
++            float partner_val = __shfl_xor_sync(0xFFFFFFFF, heap_val[i], offset, WARP_SIZE);
++            int partner_idx = __shfl_xor_sync(0xFFFFFFFF, heap_idx[i], offset, WARP_SIZE);
+             if (partner_val > heap_val[0]) {
+                 heap_val[0] = partner_val;
+                 heap_idx[0] = partner_idx;

backend/cpp/buun-llama-cpp/patches/0003-hip-add-memcpy-symbol-aliases.patch

@@ -0,0 +1,24 @@
+Subject: [PATCH] ggml-cuda/vendors/hip: alias cudaMemcpy{To,From}Symbol to hip counterparts
+
+Buun's Q² calibration + TCQ codebook upload paths in fattn.cu use
+cudaMemcpyToSymbol / cudaMemcpyFromSymbol. The HIP-compat header in
+ggml/src/ggml-cuda/vendors/hip.h already aliases the scalar cudaMemcpy
+family (cudaMemcpy, cudaMemcpyAsync, cudaMemcpy2DAsync, …) but is
+missing the symbol variants. Building with hipcc therefore fails with
+15+ "use of undeclared identifier 'cudaMemcpyToSymbol'" errors.
+
+Add the two missing aliases alongside the existing memcpy block. HIP
+provides hipMemcpy{To,From}Symbol with the same signature as CUDA's
+equivalents, so this is a straight name substitution.
+
+--- a/ggml/src/ggml-cuda/vendors/hip.h
++++ b/ggml/src/ggml-cuda/vendors/hip.h
+@@ -85,6 +85,8 @@
+ #define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+ #define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
+ #define cudaMemcpyHostToDevice hipMemcpyHostToDevice
++#define cudaMemcpyToSymbol hipMemcpyToSymbol
++#define cudaMemcpyFromSymbol hipMemcpyFromSymbol
+ #define cudaMemcpyKind hipMemcpyKind
+ #define cudaMemset hipMemset
+ #define cudaMemsetAsync hipMemsetAsync

backend/cpp/buun-llama-cpp/patches/0004-fattn-fwht128-shfl-xor-sync-add-width.patch

@@ -0,0 +1,36 @@
+Subject: [PATCH] ggml-cuda/fattn: pass WARP_SIZE to fwht128 __shfl_xor_sync calls
+
+Same issue as the argmax top-K fix: two __shfl_xor_sync call sites in
+the FWHT-128 butterfly kernels (ggml_cuda_fwht128 and fwht128_store_half)
+use the 3-arg CUDA form and omit the `width` argument that the HIP
+function-like macro in vendors/hip.h:33 requires. Hipcc fails with:
+
+    fattn.cu:512: too few arguments provided to function-like macro
+      invocation
+    note: macro '__shfl_xor_sync' defined here:
+      #define __shfl_xor_sync(mask, var, laneMask, width) \
+              __shfl_xor(var, laneMask, width)
+
+Add WARP_SIZE to both calls. CUDA codegen is unchanged (warpSize is the
+default); HIP now matches the macro arity.
+
+--- a/ggml/src/ggml-cuda/fattn.cu
++++ b/ggml/src/ggml-cuda/fattn.cu
+@@ -509,7 +509,7 @@
+     // Intra-warp passes: shuffle xor with stride h, no smem, no sync.
+     #pragma unroll
+     for (int h = 1; h <= 16; h *= 2) {
+-        const float other = __shfl_xor_sync(0xFFFFFFFF, val, h);
++        const float other = __shfl_xor_sync(0xFFFFFFFF, val, h, WARP_SIZE);
+         val = (tid & h) ? (other - val) : (val + other);
+     }
+
+@@ -533,7 +533,7 @@
+ static __device__ __forceinline__ void fwht128_store_half(
+         float val, half * dst_base) {
+     const int tid = threadIdx.x;
+-    const float neighbor = __shfl_xor_sync(0xFFFFFFFF, val, 1);
++    const float neighbor = __shfl_xor_sync(0xFFFFFFFF, val, 1, WARP_SIZE);
+     if ((tid & 1) == 0) {
+         const half2 packed = __floats2half2_rn(val, neighbor);
+         *((half2 *)(dst_base + tid)) = packed;

backend/cpp/buun-llama-cpp/run.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+set -ex
+
+# Get the absolute current dir where the script is located
+CURDIR=$(dirname "$(realpath $0)")
+
+cd /
+
+echo "CPU info:"
+grep -e "model\sname" /proc/cpuinfo | head -1
+grep -e "flags" /proc/cpuinfo | head -1
+
+BINARY=buun-llama-cpp-fallback
+
+if grep -q -e "\savx\s" /proc/cpuinfo ; then
+	echo "CPU:    AVX    found OK"
+	if [ -e $CURDIR/buun-llama-cpp-avx ]; then
+		BINARY=buun-llama-cpp-avx
+	fi
+fi
+
+if grep -q -e "\savx2\s" /proc/cpuinfo ; then
+	echo "CPU:    AVX2   found OK"
+	if [ -e $CURDIR/buun-llama-cpp-avx2 ]; then
+		BINARY=buun-llama-cpp-avx2
+	fi
+fi
+
+# Check avx 512
+if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
+	echo "CPU:    AVX512F found OK"
+	if [ -e $CURDIR/buun-llama-cpp-avx512 ]; then
+		BINARY=buun-llama-cpp-avx512
+	fi
+fi
+
+if [ -n "$LLAMACPP_GRPC_SERVERS" ]; then
+	if [ -e $CURDIR/buun-llama-cpp-grpc ]; then
+		BINARY=buun-llama-cpp-grpc
+	fi
+fi
+
+# Extend ld library path with the dir where this script is located/lib
+if [ "$(uname)" == "Darwin" ]; then
+	export DYLD_LIBRARY_PATH=$CURDIR/lib:$DYLD_LIBRARY_PATH
+else
+	export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
+	# Tell rocBLAS where to find TensileLibrary data (GPU kernel tuning files)
+	if [ -d "$CURDIR/lib/rocblas/library" ]; then
+		export ROCBLAS_TENSILE_LIBPATH=$CURDIR/lib/rocblas/library
+	fi
+fi
+
+# If there is a lib/ld.so, use it
+if [ -f $CURDIR/lib/ld.so ]; then
+	echo "Using lib/ld.so"
+	echo "Using binary: $BINARY"
+	exec $CURDIR/lib/ld.so $CURDIR/$BINARY "$@"
+fi
+
+echo "Using binary: $BINARY"
+exec $CURDIR/$BINARY "$@"
+
+# We should never reach this point, however just in case we do, run fallback
+exec $CURDIR/buun-llama-cpp-fallback "$@"

backend/cpp/buun-llama-cpp/test-patch-grpc-server.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+
+set -euo pipefail
+
+SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+SOURCE="$SCRIPT_DIR/../llama-cpp/grpc-server.cpp"
+TMP_DIR=$(mktemp -d)
+trap 'rm -rf "$TMP_DIR"' EXIT
+
+cp "$SOURCE" "$TMP_DIR/grpc-server.cpp"
+bash "$SCRIPT_DIR/patch-grpc-server.sh" "$TMP_DIR/grpc-server.cpp"
+bash "$SCRIPT_DIR/../llama-cpp/disable-score-task.sh" "$TMP_DIR/grpc-server.cpp"
+bash "$SCRIPT_DIR/patch-grpc-server.sh" "$TMP_DIR/grpc-server.cpp"
+
+for unsupported in \
+    'params.cache_idle_slots' \
+    'params.speculative.types' \
+    'params.speculative.draft.' \
+    'common_speculative_types_from_names' \
+    'COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE' \
+    'ctx_server.impl->model_tgt'; do
+    if grep -Fq "$unsupported" "$TMP_DIR/grpc-server.cpp"; then
+        echo "unsupported buun API remains: $unsupported" >&2
+        exit 1
+    fi
+done
+
+grep -Fq '#define LOCALAI_LLAMA_CPP_NO_SCORE_TASK 1' "$TMP_DIR/grpc-server.cpp"
+grep -Fq '#define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP 1' "$TMP_DIR/grpc-server.cpp"
+grep -Fq 'params.speculative.mparams_dft.path = request->draftmodel();' "$TMP_DIR/grpc-server.cpp"
+grep -Fq 'params.speculative.type = COMMON_SPECULATIVE_TYPE_DRAFT;' "$TMP_DIR/grpc-server.cpp"
+grep -Fq 'ctx_server.impl->model' "$TMP_DIR/grpc-server.cpp"
+
+echo "buun grpc-server compatibility transform passed"

backend/cpp/ds4/CMakeLists.txt

@@ -69,15 +69,7 @@ target_include_directories(hw_grpc_proto PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
 
 set(DS4_OBJS "${DS4_DIR}/ds4.o")
 if(DS4_GPU STREQUAL "cuda")
-    list(APPEND DS4_OBJS
-        "${DS4_DIR}/ds4_cuda.o"
-        "${DS4_DIR}/cuda/mmq/ds4_ggml_stubs.o"
-        "${DS4_DIR}/cuda/mmq/ds4_mmq.o"
-        "${DS4_DIR}/cuda/mmq/ds4_mmq_d2r.o"
-        "${DS4_DIR}/cuda/mmq/quantize.o"
-        "${DS4_DIR}/cuda/mmq/mmid.o"
-        "${DS4_DIR}/cuda/mmq/mmvq.o"
-        "${DS4_DIR}/cuda/mmq/ds4_repack.o")
+    list(APPEND DS4_OBJS "${DS4_DIR}/ds4_cuda.o")
 elseif(DS4_GPU STREQUAL "metal")
     list(APPEND DS4_OBJS "${DS4_DIR}/ds4_metal.o")
 elseif(DS4_GPU STREQUAL "cpu")

backend/cpp/ds4/Makefile

@@ -1,10 +1,10 @@
 # ds4 backend Makefile.
 #
-# Upstream pin lives below as DS4_VERSION?=6747e7718dd08f00b680d0c16231f2d59ec3747e
+# Upstream pin lives below as DS4_VERSION?=54b36ed9ba42da31b24f2d1a5feb075c2475dbb1
 # (.github/bump_deps.sh) can find and update it - matches the
 # llama-cpp / ik-llama-cpp / turboquant convention.
 
-DS4_VERSION?=6747e7718dd08f00b680d0c16231f2d59ec3747e
+DS4_VERSION?=54b36ed9ba42da31b24f2d1a5feb075c2475dbb1
 DS4_REPO?=https://github.com/antirez/ds4
 
 CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
@@ -23,9 +23,7 @@ CMAKE_ARGS ?= -DCMAKE_BUILD_TYPE=Release
 # are shared by every GPU mode, so append them unconditionally below.
 ifeq ($(BUILD_TYPE),cublas)
     CMAKE_ARGS += -DDS4_GPU=cuda
-    DS4_OBJ_TARGET := ds4.o ds4_cuda.o ds4_distributed.o ds4_tp.o ds4_ssd.o ds4_layer_pack.o \
-        cuda/mmq/ds4_ggml_stubs.o cuda/mmq/ds4_mmq.o cuda/mmq/ds4_mmq_d2r.o \
-        cuda/mmq/quantize.o cuda/mmq/mmid.o cuda/mmq/mmvq.o cuda/mmq/ds4_repack.o
+    DS4_OBJ_TARGET := ds4.o ds4_cuda.o ds4_distributed.o ds4_tp.o ds4_ssd.o ds4_layer_pack.o
 else ifeq ($(UNAME_S),Darwin)
     CMAKE_ARGS += -DDS4_GPU=metal
     DS4_OBJ_TARGET := ds4.o ds4_metal.o ds4_distributed.o ds4_tp.o ds4_ssd.o ds4_layer_pack.o
@@ -57,7 +55,7 @@ ds4:
 # the right per-platform compile flags (Objective-C/Metal on Darwin, nvcc on Linux+CUDA).
 ds4/ds4.o: ds4
 ifeq ($(BUILD_TYPE),cublas)
-	+$(MAKE) -C ds4 $(DS4_OBJ_TARGET)
+	+$(MAKE) -C ds4 ds4.o ds4_cuda.o ds4_distributed.o ds4_tp.o ds4_ssd.o ds4_layer_pack.o
 else ifeq ($(UNAME_S),Darwin)
 	+$(MAKE) -C ds4 ds4.o ds4_metal.o ds4_distributed.o ds4_tp.o ds4_ssd.o ds4_layer_pack.o
 else

backend/cpp/ik-llama-cpp/Makefile

@@ -1,5 +1,5 @@
 
-IK_LLAMA_VERSION?=6b55d2c7504f482e7c8ec6cbf22a19f3778c522b
+IK_LLAMA_VERSION?=cb9147fd0d9c08a9a84eee5ac405a73f4e10e3e1
 LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
 
 CMAKE_ARGS?=

backend/cpp/llama-cpp/Makefile

@@ -1,5 +1,5 @@
 
-LLAMA_VERSION?=221f0f6356efe2260023208365705ec5d5a7c8f5
+LLAMA_VERSION?=a7a6d0d269c896218b6c78e0933bd6a17519d3f6
 LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
 
 CMAKE_ARGS?=

backend/go/crispasr/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # CrispASR version (release tag)
 CRISPASR_REPO?=https://github.com/CrispStrobe/CrispASR
-CRISPASR_VERSION?=ec730908a418b6032f9e69ded6186d3f042a7747
+CRISPASR_VERSION?=fcb79282a6bc52e13d858026c42b24fb6e63c97a
 SO_TARGET?=libgocrispasr.so
 
 CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

backend/go/stablediffusion-ggml/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # stablediffusion.cpp (ggml)
 STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
-STABLEDIFFUSION_GGML_VERSION?=ea7f0c87cfe4c673263b4c201c596c7f1cbe2528
+STABLEDIFFUSION_GGML_VERSION?=db99efdd6d2a43c7937fd55b3359206c680a75b0
 
 CMAKE_ARGS+=-DGGML_MAX_NAME=128
 

backend/go/vllm-cpp/Makefile

@@ -11,30 +11,7 @@ JOBS?=$(shell nproc --ignore=1 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || e
 
 # vllm.cpp version
 VLLM_CPP_REPO?=https://github.com/mudler/vllm.cpp
-VLLM_CPP_VERSION?=0757cac231ecd571a83c4fd2f50805c9251fc225
-
-# MLX GEMM provider (darwin/metal only; see the metal branch below for why).
-# Consumed as the prebuilt pip wheel: building MLX from source needs `xcrun
-# metal`, i.e. a full Xcode the macOS runners do not have, while the wheel ships
-# include/, lib/libmlx.dylib and the compiled mlx.metallib ready to link.
-#
-# DEFAULT ON, but ONLY because VLLM_CPP_VERSION above is pinned at or past
-# vllm.cpp 89c46aeb, which SHAPE-GATES the provider to prefill. The ordering is
-# load-bearing, not incidental:
-#
-#   pin >= 89c46aeb, MLX on   -> 99.1% of MLX-LM  (gated: prefill only)
-#   pin <  89c46aeb, MLX on   -> ~51%             (ungated: it also takes decode)
-#
-# MLX's steel GEMM wins prefill (537 ms TTFT against 602) and loses decode badly,
-# because the provider pays an mx::eval sync plus an output memcpy per call and
-# decode makes ~112 calls per TOKEN. Ungated it does both; gated it does only the
-# good half. So if this pin is ever moved BACKWARDS, this default must go with it.
-VLLM_CPP_MLX?=on
-MLX_VERSION?=0.29.4
-MLX_VENV?=$(abspath ./mlx-venv)
-# Resolved lazily (recursive `=`, not `:=`): the glob only matches once the venv
-# target has run, and the interpreter version in the path varies per runner.
-MLX_ROOT=$(shell echo $(MLX_VENV)/lib/python*/site-packages/mlx)
+VLLM_CPP_VERSION?=9e1c9025ae61167a3335454d7cc0de6093c21845
 
 # The backend consumes only the stable C ABI (libvllm + include/vllm.h), so the
 # server, examples and tests of the engine are never built here.
@@ -72,23 +49,6 @@ else ifeq ($(BUILD_TYPE),vulkan)
 	CMAKE_ARGS+=-DVLLM_CPP_VULKAN=ON -DVLLM_CPP_CUDA=OFF
 else ifeq ($(BUILD_TYPE),metal)
 	CMAKE_ARGS+=-DVLLM_CPP_METAL=ON
-	# The optional MLX GEMM provider. vllm.cpp keeps it OFF by default because it
-	# is a ~19 MB libmlx.dylib plus a ~105 MB mlx.metallib, and upstream's
-	# position is that it must earn that cost by measurement. It does, on the
-	# only hardware this build targets: measured on an Apple M4 against the
-	# native MSL GEMM in the SAME binary (arms toggled by
-	# VT_OP_PROVIDER_DISABLE=mlx), Qwen3-1.7B-bf16 p=512 g=128, it is 1.5x to
-	# 2.2x aggregate throughput and 2x to 3x faster TTFT, at equal peak memory
-	# and bit-identical output on every parity shape. See vllm.cpp
-	# docs/BENCHMARKS.md "MLX GEMM provider A/B on Apple M4".
-	#
-	# MLX delegates the dense GEMM ONLY: kPagedAttention stays vllm.cpp's own
-	# kernel, because MLX has no paged-KV primitive at all.
-	#
-	# Set VLLM_CPP_MLX=off for a Metal build without it (smaller image, slower).
-	ifeq ($(VLLM_CPP_MLX),on)
-		MLX_ENABLED=1
-	endif
 else
 	CMAKE_ARGS+=-DVLLM_CPP_CUDA=OFF
 endif
@@ -108,35 +68,10 @@ sources/vllm.cpp:
 	git fetch --depth 1 origin $(VLLM_CPP_VERSION) && \
 	git checkout FETCH_HEAD
 
-ifeq ($(MLX_ENABLED),1)
-# A stamp FILE, not a phony target: a phony prerequisite is always "newer" than
-# $(LIB) and would re-link libvllm on every invocation. Keyed on the version so
-# a MLX_VERSION bump reinstalls instead of silently reusing the old wheel.
-MLX_STAMP=$(MLX_VENV)/.mlx-$(MLX_VERSION).stamp
-MLX_CMAKE_ARGS=-DVLLM_CPP_MLX=ON -DMLX_ROOT=$(MLX_ROOT)
-
-$(MLX_STAMP):
-	@if [ ! -x "$(MLX_VENV)/bin/pip" ]; then \
-		python3 -m venv "$(MLX_VENV)" || { echo "vllm-cpp: python3 with venv is required to build the MLX provider; pass VLLM_CPP_MLX=off to build Metal without it" >&2; exit 1; }; \
-	fi
-	"$(MLX_VENV)"/bin/pip install --quiet --disable-pip-version-check "mlx==$(MLX_VERSION)"
-	@# Resolved in the SHELL, not by $(MLX_ROOT): make expands a whole recipe
-	@# before running its first line, so the glob would still be unmatched here.
-	@# Every later use (the cmake args, package.sh) expands after this target has
-	@# completed, where $(MLX_ROOT) does resolve.
-	@root=$$(echo "$(MLX_VENV)"/lib/python*/site-packages/mlx); \
-	test -f "$$root/lib/libmlx.dylib" -a -f "$$root/include/mlx/array.h" || \
-		{ echo "vllm-cpp: mlx==$(MLX_VERSION) did not provide lib/libmlx.dylib + include/mlx/array.h under $$root" >&2; exit 1; }
-	touch $@
-else
-MLX_STAMP=
-MLX_CMAKE_ARGS=
-endif
-
-$(LIB): sources/vllm.cpp $(MLX_STAMP)
+$(LIB): sources/vllm.cpp
 	mkdir -p build && \
 	cd build && \
-	cmake ../sources/vllm.cpp $(CMAKE_ARGS) $(MLX_CMAKE_ARGS) && \
+	cmake ../sources/vllm.cpp $(CMAKE_ARGS) && \
 	cmake --build . --config Release -j$(JOBS) --target vllm_shared
 	cp -fL build/$(LIB) ./$(LIB)
 
@@ -144,12 +79,12 @@ vllm-cpp: main.go govllmcpp.go backend.go options.go $(LIB)
 	CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o vllm-cpp ./
 
 package: vllm-cpp
-	MLX_ROOT="$(MLX_ROOT)" bash package.sh
+	bash package.sh
 
 build: package
 
 clean: purge
-	rm -rf libvllm.so libvllm.dylib package sources/vllm.cpp vllm-cpp "$(MLX_VENV)"
+	rm -rf libvllm.so libvllm.dylib package sources/vllm.cpp vllm-cpp
 
 purge:
 	rm -rf build

backend/go/vllm-cpp/README.md

@@ -41,50 +41,5 @@ options:
 - max_num_seqs:16
 ```
 
-## Apple Silicon: the MLX GEMM provider (ON by default, gated to prefill)
-
-`BUILD_TYPE=metal` builds vllm.cpp's MLX provider for the dense GEMM
-(`VLLM_CPP_MLX=on`, the default here). It is on because upstream now SHAPE-GATES
-it to prefill; it was briefly off in this branch's history, and that was correct
-at the time for an ungated provider.
-
-The gate matters more than the flag. MLX's steel GEMM wins prefill but loses
-decode, because the provider pays an `mx::eval` synchronisation plus an output
-memcpy on every call and decode makes ~112 calls *per token*. Measured on an
-Apple M4, Qwen3-1.7B-bf16 warm at p=512 g=128:
-
-| configuration | prefill TTFT | warm throughput |
-|---|--:|--:|
-| MLX **gated to prefill** (pin >= 89c46aeb) | **524.5 ms** | **24.37 tok/s, 97.6% of MLX-LM** |
-| MLX ungated (older pins) | 537 ms | 12.7 tok/s |
-| MLX off | 602 ms | 23.9 tok/s, 95.9% |
-
-Ratios are against an MLX-LM baseline measured INTERLEAVED with ours over four
-ABBA blocks (its spread 0.34%, ours 0.12%). An earlier revision of this file
-claimed 99.1%; that used a two-run MLX-LM baseline containing an outlier and
-overstated us by about 1.5 points.
-
-**`VLLM_CPP_VERSION` and this flag are coupled.** Moving the pin back before
-`89c46aeb` while leaving `VLLM_CPP_MLX=on` would take the middle row — roughly
-half throughput. If you roll the pin back, roll the default back with it.
-
-One caveat: MLX's GEMM is not bit-identical to the native kernel, so an MLX build
-produces a different greedy sequence than a non-MLX one. That is a property of the
-provider, not of the gate, and it predates this packaging. Full disposition in
-vllm.cpp `docs/BENCHMARKS.md`.
-
-Build knobs:
-
-- `VLLM_CPP_MLX=off` builds Metal without the provider: ~124 MB smaller, and
-  96.4% of MLX-LM instead of 99.1%.
-- `MLX_VERSION` pins the wheel (default `0.29.4`). MLX is consumed as the
-  prebuilt pip wheel because building it from source needs `xcrun metal`, i.e. a
-  full Xcode the macOS runners do not have.
-
-Packaging vendors `libmlx.dylib`, `mlx.metallib` and MLX's MIT license into
-`package/lib/`, and rewrites `libvllm.dylib`'s rpath to `@loader_path/lib`
-(re-signing it, since `install_name_tool` invalidates the signature). The
-metallib must stay beside `libmlx.dylib`: MLX looks for it there.
-
 Testing: `make test` runs the unit specs; export `VLLM_CPP_MODEL=<model>` (and
 optionally `VLLM_CPP_LIBRARY=<libvllm path>`) to enable the e2e specs.

backend/go/vllm-cpp/package.sh

@@ -43,50 +43,6 @@ elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
     cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
 elif [ $(uname -s) = "Darwin" ]; then
     echo "Detected Darwin"
-    # Vendor the optional MLX GEMM provider, when libvllm was built against it.
-    # Three facts drive every line below, each verified on an Apple M4 before it
-    # was written:
-    #   1. libvllm.dylib carries an LC_LOAD_DYLIB on @rpath/libmlx.dylib, and its
-    #      build-time LC_RPATH points inside the build venv. That path does not
-    #      exist on a user's machine, so it must become @loader_path/lib.
-    #   2. MLX finds its ~100 MB mlx.metallib beside its OWN dylib, so the two
-    #      files have to land in the same directory or every Metal op dies with
-    #      "Failed to load the default metallib".
-    #   3. install_name_tool invalidates the code signature, and macOS refuses to
-    #      load an arm64 image whose signature does not match, so the patched
-    #      library must be re-signed ad-hoc afterwards.
-    if otool -L "$CURDIR/package/libvllm.dylib" 2>/dev/null | grep -q "libmlx.dylib"; then
-        MLX_LIB_DIR="${MLX_ROOT}/lib"
-        if [ ! -f "$MLX_LIB_DIR/libmlx.dylib" ] || [ ! -f "$MLX_LIB_DIR/mlx.metallib" ]; then
-            echo "Error: libvllm.dylib links libmlx.dylib but $MLX_LIB_DIR is missing libmlx.dylib/mlx.metallib" >&2
-            exit 1
-        fi
-        echo "Vendoring the MLX GEMM provider from $MLX_LIB_DIR"
-        cp -fLv "$MLX_LIB_DIR/libmlx.dylib" "$CURDIR/package/lib/"
-        cp -fLv "$MLX_LIB_DIR/mlx.metallib" "$CURDIR/package/lib/"
-        # MLX is MIT and we redistribute its binaries, so its license ships with
-        # them. mlx-metal is the wheel carrying the dylib and the metallib.
-        MLX_LICENSE=$(ls "${MLX_ROOT}"/../mlx_metal-*.dist-info/licenses/LICENSE 2>/dev/null | head -1)
-        if [ -z "$MLX_LICENSE" ]; then
-            MLX_LICENSE=$(ls "${MLX_ROOT}"/../mlx-*.dist-info/licenses/LICENSE 2>/dev/null | head -1)
-        fi
-        if [ -z "$MLX_LICENSE" ]; then
-            echo "Error: could not find the MLX LICENSE to redistribute alongside libmlx.dylib" >&2
-            exit 1
-        fi
-        cp -fLv "$MLX_LICENSE" "$CURDIR/package/lib/LICENSE.mlx"
-        # Drop every build-tree rpath, then point at the packaged copy.
-        otool -l "$CURDIR/package/libvllm.dylib" | awk '/LC_RPATH/{f=1;next} f&&/ path /{print $2;f=0}' | while read -r rp; do
-            install_name_tool -delete_rpath "$rp" "$CURDIR/package/libvllm.dylib" 2>/dev/null || true
-        done
-        install_name_tool -add_rpath "@loader_path/lib" "$CURDIR/package/libvllm.dylib"
-        codesign -f -s - "$CURDIR/package/libvllm.dylib"
-        # A broken rpath must fail the BUILD, not the user's first inference.
-        if ! otool -l "$CURDIR/package/libvllm.dylib" | grep -q "@loader_path/lib"; then
-            echo "Error: libvllm.dylib did not get the @loader_path/lib rpath" >&2
-            exit 1
-        fi
-    fi
 else
     echo "Error: Could not detect architecture"
     exit 1

backend/go/whisper/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # whisper.cpp version
 WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
-WHISPER_CPP_VERSION?=306c88f4d1286aec1bf96e544632897886af5501
+WHISPER_CPP_VERSION?=2ca53bb45e38748d07b310eeb36245a7157ac882
 SO_TARGET?=libgowhisper.so
 
 CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

backend/index.yaml

@@ -193,22 +193,12 @@
   alias: "vllm-cpp"
   license: apache-2.0
   description: |
-    ALPHA development builds. Try it, but llama-cpp stays the recommendation for
-    production use.
-
-    vllm.cpp is an Apache-2.0 C++20 inference engine maintained by the LocalAI team,
-    developed in its own repository and usable without LocalAI. It began as a port of
-    vLLM and keeps vLLM as its reference implementation, checking output against it and
-    benchmarking against it, while growing a featureset of its own. It implements vLLM's
-    V1 architecture (paged KV cache, continuous batching, prefix caching, scheduler,
-    sampler) on a portable tensor runtime with no Python, PyTorch or ggml at inference
-    time. It loads GGUF as well as Hugging Face safetensors, supports structured output
-    (JSON schema / regex / choice / GBNF grammar) enforced in-engine, ships speculative
-    decoding and KV offload, and runs on CPU, NVIDIA CUDA (Blackwell-family), Apple
-    Metal and Vulkan.
-
-    The project is expected to be renamed as it diverges further from vLLM; the new
-    name is still to be decided.
+    vllm.cpp is a from-scratch C++20 port of vLLM created and maintained by the LocalAI team.
+    It mirrors vLLM's V1 architecture (paged KV cache, continuous batching, prefix caching,
+    scheduler, sampler) on a portable tensor runtime with no Python, PyTorch or ggml at
+    inference time. It loads Hugging Face safetensors and GGUF checkpoints, supports
+    structured output (JSON schema / regex / choice / GBNF grammar) enforced in-engine,
+    and runs on CPU, NVIDIA CUDA (Blackwell-family), Apple Metal and Vulkan.
   urls:
     - https://github.com/mudler/vllm.cpp
   tags:

core/config/inference_defaults.json

@@ -41,12 +41,12 @@
     "glm-5": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":-1,"top_p":0.95},
     "glm-4": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":-1,"top_p":0.95},
     "nemotron": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":-1,"top_p":1},
+    "minimax-m3": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":40,"top_p":0.95},
     "minimax-m2.7": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":40,"top_p":0.95},
     "minimax-m2.5": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":40,"top_p":0.95},
     "minimax": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":40,"top_p":0.95},
     "gpt-oss": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":0,"top_p":1},
     "granite-4": {"min_p":0.01,"repeat_penalty":1,"temperature":0,"top_k":0,"top_p":1},
-    "kimi-k3": {"min_p":0,"repeat_penalty":1,"temperature":1,"top_k":-1,"top_p":0.95},
     "kimi-k2": {"min_p":0.01,"repeat_penalty":1,"temperature":0.6,"top_k":-1,"top_p":0.95},
     "kimi": {"min_p":0.01,"repeat_penalty":1,"temperature":0.6,"top_k":-1,"top_p":0.95},
     "lfm2": {"min_p":0.15,"repeat_penalty":1.05,"temperature":0.1,"top_k":50,"top_p":0.1},
@@ -58,5 +58,5 @@
     "grok": {"min_p":0.01,"repeat_penalty":1,"temperature":1,"top_k":-1,"top_p":0.95},
     "mimo": {"min_p":0.01,"repeat_penalty":1,"temperature":0.7,"top_k":-1,"top_p":0.95}
   },
-  "patterns": ["qwen3.6","qwen3.5","qwen3-coder","qwen3-next","qwen3-vl","qwen3","qwen2.5-coder","qwen2.5-vl","qwen2.5-omni","qwen2.5-math","qwen2.5","qwen2-vl","qwen2","qwq","gemma-4","gemma-3n","gemma-3","medgemma","gemma-2","llama-4","llama-3.3","llama-3.2","llama-3.1","llama-3","phi-4","phi-3","mistral-nemo","mistral-small","mistral-large","magistral","ministral","devstral","pixtral","deepseek-v4","deepseek-r1","deepseek-v3","deepseek-ocr","glm-5","glm-4","nemotron","minimax-m2.7","minimax-m2.5","minimax","gpt-oss","granite-4","kimi-k3","kimi-k2","kimi","lfm2","smollm","olmo","falcon","ernie","seed","grok","mimo"]
+  "patterns": ["qwen3.6","qwen3.5","qwen3-coder","qwen3-next","qwen3-vl","qwen3","qwen2.5-coder","qwen2.5-vl","qwen2.5-omni","qwen2.5-math","qwen2.5","qwen2-vl","qwen2","qwq","gemma-4","gemma-3n","gemma-3","medgemma","gemma-2","llama-4","llama-3.3","llama-3.2","llama-3.1","llama-3","phi-4","phi-3","mistral-nemo","mistral-small","mistral-large","magistral","ministral","devstral","pixtral","deepseek-v4","deepseek-r1","deepseek-v3","deepseek-ocr","glm-5","glm-4","nemotron","minimax-m3","minimax-m2.7","minimax-m2.5","minimax","gpt-oss","granite-4","kimi-k2","kimi","lfm2","smollm","olmo","falcon","ernie","seed","grok","mimo"]
 }

core/config/meta/constants.go

@@ -38,6 +38,14 @@ var CacheTypeOptions = []FieldOption{
 	{Value: "q4_1", Label: "Q4_1"},
 	{Value: "q5_0", Label: "Q5_0"},
 	{Value: "q5_1", Label: "Q5_1"},
+	// TurboQuant KV-cache types — accepted by the turboquant and
+	// buun-llama-cpp fork backends; stock llama-cpp will reject them at load.
+	{Value: "turbo2", Label: "Turbo2 (TurboQuant)"},
+	{Value: "turbo3", Label: "Turbo3 (TurboQuant)"},
+	{Value: "turbo4", Label: "Turbo4 (TurboQuant)"},
+	// Trellis-Coded Quantization variants — buun-llama-cpp only.
+	{Value: "turbo2_tcq", Label: "Turbo2 TCQ (buun-llama-cpp)"},
+	{Value: "turbo3_tcq", Label: "Turbo3 TCQ (buun-llama-cpp)"},
 }
 
 var DiffusersPipelineOptions = []FieldOption{

core/gallery/importers/llama-cpp.go

@@ -38,6 +38,7 @@ func (i *LlamaCPPImporter) AdditionalBackends() []KnownBackendEntry {
 		{Name: "ik-llama-cpp", Modality: "text", Description: "GGUF drop-in replacement for llama-cpp with ik-quants"},
 		{Name: "turboquant", Modality: "text", Description: "GGUF drop-in replacement for llama-cpp with TurboQuant optimizations"},
 		{Name: "vllm-cpp", Modality: "text", Description: "vLLM-style continuous-batching engine (vllm.cpp) consuming GGUF, by the LocalAI team"},
+		{Name: "buun-llama-cpp", Modality: "text", Description: "GGUF drop-in replacement for llama-cpp with DFlash speculative decoding and TurboQuant/TCQ KV-cache quantization"},
 	}
 }
 
@@ -136,7 +137,7 @@ func (i *LlamaCPPImporter) Import(details Details) (gallery.ModelConfig, error)
 	backend := "llama-cpp"
 	if b, ok := preferencesMap["backend"].(string); ok {
 		switch b {
-		case "ik-llama-cpp", "turboquant", "vllm-cpp":
+		case "ik-llama-cpp", "turboquant", "vllm-cpp", "buun-llama-cpp":
 			backend = b
 		}
 	}

core/gallery/importers/llama-cpp_test.go

@@ -203,6 +203,23 @@ var _ = Describe("LlamaCPPImporter", func() {
 			Expect(modelConfig.Files[0].Filename).To(Equal("my-model.gguf"))
 		})
 
+		It("swaps the emitted backend to buun-llama-cpp when preferred", func() {
+			preferences := json.RawMessage(`{"backend": "buun-llama-cpp"}`)
+			details := Details{
+				URI:         "https://example.com/my-model.gguf",
+				Preferences: preferences,
+			}
+
+			modelConfig, err := importer.Import(details)
+
+			Expect(err).ToNot(HaveOccurred())
+			Expect(modelConfig.ConfigFile).To(ContainSubstring("backend: buun-llama-cpp"), fmt.Sprintf("Model config: %+v", modelConfig))
+			Expect(modelConfig.ConfigFile).NotTo(ContainSubstring("backend: llama-cpp\n"), fmt.Sprintf("Model config: %+v", modelConfig))
+			Expect(modelConfig.ConfigFile).To(ContainSubstring("model: my-model.gguf"), fmt.Sprintf("Model config: %+v", modelConfig))
+			Expect(len(modelConfig.Files)).To(Equal(1))
+			Expect(modelConfig.Files[0].Filename).To(Equal("my-model.gguf"))
+		})
+
 		It("keeps backend: llama-cpp for unknown backend preferences", func() {
 			// Unknown backend values must not leak into the emitted YAML —
 			// we only honour the curated drop-in replacements.
@@ -551,7 +568,7 @@ var _ = Describe("LlamaCPPImporter", func() {
 	})
 
 	Context("AdditionalBackends", func() {
-		It("advertises ik-llama-cpp, turboquant and vllm-cpp as drop-in replacements", func() {
+		It("advertises all llama-cpp drop-in replacements", func() {
 			entries := importer.AdditionalBackends()
 
 			names := make([]string, 0, len(entries))
@@ -560,7 +577,7 @@ var _ = Describe("LlamaCPPImporter", func() {
 				names = append(names, e.Name)
 				byName[e.Name] = e
 			}
-			Expect(names).To(ConsistOf("ik-llama-cpp", "turboquant", "vllm-cpp"))
+			Expect(names).To(ConsistOf("ik-llama-cpp", "turboquant", "vllm-cpp", "buun-llama-cpp"))
 
 			for _, name := range names {
 				e := byName[name]

core/http/middleware/trace.go

@@ -60,7 +60,6 @@ type APIExchange struct {
 }
 
 var traceBuffer *circularbuffer.Queue[APIExchange]
-var inFlightTraces = make(map[string]APIExchange)
 var mu sync.Mutex
 var logChan = make(chan traceCommand, 100)
 var traceIDSeq atomic.Uint64
@@ -127,17 +126,16 @@ func initializeTracing(dataPath string, maxItems int) {
 					continue
 				}
 				exchange := *command.exchange
-				mu.Lock()
-				delete(inFlightTraces, exchange.ID)
-				if traceBuffer != nil {
-					traceBuffer.Enqueue(exchange)
-				}
-				mu.Unlock()
 				if command.store != nil {
 					if err := command.store.Append(exchange.ID, exchange); err != nil {
 						xlog.Warn("Failed to persist API trace", "error", err)
 					}
 				}
+				mu.Lock()
+				if traceBuffer != nil {
+					traceBuffer.Enqueue(exchange)
+				}
+				mu.Unlock()
 			}
 		}()
 	})
@@ -263,38 +261,6 @@ func TraceMiddleware(app *application.Application) echo.MiddlewareFunc {
 			// tens of MB, which then locks the admin Traces UI fetching the
 			// JSON dump faster than the 5s auto-refresh.
 			maxBodyBytes := app.ApplicationConfig().TracingMaxBodyBytes
-			requestHeaders := redactSensitiveHeaders(c.Request().Header)
-			requestBody, requestTruncated := truncateForTrace(body, maxBodyBytes)
-			exchange := APIExchange{
-				ID:        nextTraceID(),
-				Timestamp: startTime,
-				ClientIP:  c.RealIP(),
-				UserAgent: c.Request().UserAgent(),
-				Request: APIExchangeRequest{
-					Method:        c.Request().Method,
-					Path:          c.Path(),
-					Headers:       &requestHeaders,
-					Body:          &requestBody,
-					BodyTruncated: requestTruncated,
-					BodyBytes:     len(body),
-				},
-			}
-			if user := auth.GetUser(c); user != nil {
-				exchange.UserID = user.ID
-				exchange.UserName = user.Name
-			}
-			mu.Lock()
-			inFlightTraces[exchange.ID] = exchange
-			mu.Unlock()
-			queued := false
-			defer func() {
-				if queued {
-					return
-				}
-				mu.Lock()
-				delete(inFlightTraces, exchange.ID)
-				mu.Unlock()
-			}()
 
 			// Wrap response writer to capture body
 			resBody := new(bytes.Buffer)
@@ -321,27 +287,47 @@ func TraceMiddleware(app *application.Application) echo.MiddlewareFunc {
 			// the trace endpoint is admin-only but the buffer is also reachable
 			// via any heap-dump-style introspection, and tokens shouldn't
 			// outlive the request that carried them.
+			requestHeaders := redactSensitiveHeaders(c.Request().Header)
+			requestBody, requestTruncated := truncateForTrace(body, maxBodyBytes)
 			responseHeaders := redactSensitiveHeaders(c.Response().Header())
 			responseBody := make([]byte, resBody.Len())
 			copy(responseBody, resBody.Bytes())
-			exchange.Duration = time.Since(startTime)
-			exchange.Response = APIExchangeResponse{
-				Status:        status,
-				Headers:       &responseHeaders,
-				Body:          &responseBody,
-				BodyTruncated: mw.truncated,
-				BodyBytes:     mw.totalBytes,
+			exchange := APIExchange{
+				ID:        nextTraceID(),
+				Timestamp: startTime,
+				Duration:  time.Since(startTime),
+				ClientIP:  c.RealIP(),
+				UserAgent: c.Request().UserAgent(),
+				Request: APIExchangeRequest{
+					Method:        c.Request().Method,
+					Path:          c.Path(),
+					Headers:       &requestHeaders,
+					Body:          &requestBody,
+					BodyTruncated: requestTruncated,
+					BodyBytes:     len(body),
+				},
+				Response: APIExchangeResponse{
+					Status:        status,
+					Headers:       &responseHeaders,
+					Body:          &responseBody,
+					BodyTruncated: mw.truncated,
+					BodyBytes:     mw.totalBytes,
+				},
 			}
 			if handlerErr != nil {
 				exchange.Error = handlerErr.Error()
 			}
 
+			if user := auth.GetUser(c); user != nil {
+				exchange.UserID = user.ID
+				exchange.UserName = user.Name
+			}
+
 			mu.Lock()
 			store := traceStore
 			mu.Unlock()
 			select {
 			case logChan <- traceCommand{exchange: &exchange, store: store}:
-				queued = true
 			default:
 				xlog.Warn("Trace channel full, dropping trace")
 			}
@@ -359,10 +345,6 @@ func GetTraces() []APIExchange {
 		return []APIExchange{}
 	}
 	traces := traceBuffer.Values()
-	for _, exchange := range inFlightTraces {
-		exchange.Duration = time.Since(exchange.Timestamp)
-		traces = append(traces, exchange)
-	}
 	mu.Unlock()
 
 	slices.SortFunc(traces, func(a, b APIExchange) int {

core/http/middleware/trace_live_test.go

@@ -1,108 +0,0 @@
-// SPDX-License-Identifier: MIT
-
-package middleware
-
-import (
-	"net/http"
-	"net/http/httptest"
-	"time"
-
-	"github.com/labstack/echo/v4"
-	"github.com/mudler/LocalAI/core/application"
-	"github.com/mudler/LocalAI/core/config"
-	"github.com/mudler/LocalAI/pkg/system"
-	. "github.com/onsi/ginkgo/v2"
-	. "github.com/onsi/gomega"
-)
-
-var _ = Describe("live API traces", func() {
-	newApp := func(root string) *application.Application {
-		app, err := application.New(
-			config.EnableTracing,
-			config.WithDataPath(root),
-			config.WithDisableLocalAIAssistant(true),
-			config.WithDisableStats(true),
-			config.WithSystemState(&system.SystemState{
-				Model:   system.Model{ModelsPath: root},
-				Backend: system.Backend{BackendsPath: root},
-			}),
-		)
-		Expect(err).NotTo(HaveOccurred())
-		DeferCleanup(func() { Expect(app.Shutdown()).To(Succeed()) })
-		ClearTraces()
-		return app
-	}
-
-	It("lists a request while its handler is still running", func() {
-		root := GinkgoT().TempDir()
-		app := newApp(root)
-
-		started := make(chan struct{})
-		release := make(chan struct{})
-		DeferCleanup(func() {
-			select {
-			case <-release:
-			default:
-				close(release)
-			}
-		})
-		handler := TraceMiddleware(app)(func(c echo.Context) error {
-			close(started)
-			<-release
-			return c.NoContent(http.StatusNoContent)
-		})
-
-		e := echo.New()
-		req := httptest.NewRequest(http.MethodPost, "/slow", http.NoBody)
-		req.Header.Set(echo.HeaderContentType, echo.MIMEApplicationJSON)
-		rec := httptest.NewRecorder()
-		ctx := e.NewContext(req, rec)
-		ctx.SetPath("/slow")
-		done := make(chan error, 1)
-		go func() {
-			done <- handler(ctx)
-		}()
-		<-started
-
-		var running APIExchange
-		Eventually(func() bool {
-			traces := GetTraces()
-			if len(traces) != 1 {
-				return false
-			}
-			running = traces[0]
-			return running.Request.Path == "/slow"
-		}).Should(BeTrue())
-		Expect(running.Response.Status).To(Equal(0))
-		Expect(running.Duration).To(BeNumerically(">", 0))
-
-		close(release)
-		Expect(<-done).To(Succeed())
-		Eventually(func() []APIExchange { return GetTraces() }).Should(ConsistOf(
-			And(
-				HaveField("ID", running.ID),
-				HaveField("Response.Status", http.StatusNoContent),
-				HaveField("Duration", BeNumerically(">", time.Duration(0))),
-			),
-		))
-	})
-
-	It("removes an in-flight trace when the handler panics", func() {
-		app := newApp(GinkgoT().TempDir())
-		handler := TraceMiddleware(app)(func(echo.Context) error {
-			panic("handler panic")
-		})
-		e := echo.New()
-		req := httptest.NewRequest(http.MethodPost, "/panic", http.NoBody)
-		req.Header.Set(echo.HeaderContentType, echo.MIMEApplicationJSON)
-		ctx := e.NewContext(req, httptest.NewRecorder())
-		ctx.SetPath("/panic")
-
-		func() {
-			defer func() { _ = recover() }()
-			_ = handler(ctx)
-		}()
-
-		Expect(GetTraces()).To(BeEmpty())
-	})
-})

core/http/react-ui/e2e/traces-live.spec.js

@@ -1,22 +0,0 @@
-import { test, expect } from './coverage-fixtures.js'
-
-test('marks an API trace with no response status as in progress', async ({ page }) => {
-  await page.route('**/api/traces?*', route => route.fulfill({
-    json: [{
-      id: 'running-1',
-      timestamp: '2026-08-05T02:00:00Z',
-      duration: 2_000_000_000,
-      request: { method: 'POST', path: '/v1/chat/completions' },
-      response: { status: 0 },
-    }],
-    headers: { 'X-Total-Count': '1' },
-  }))
-  await page.route('**/api/backend-traces?*', route => route.fulfill({ json: [] }))
-
-  await page.goto('/app/traces')
-
-  const row = page.locator('tbody tr').filter({ hasText: '/v1/chat/completions' })
-  await expect(row.getByText('Running', { exact: true })).toBeVisible()
-  await expect(row.locator('[title="In progress"]')).toBeVisible()
-  await expect(row.locator('.fa-check-circle')).toHaveCount(0)
-})

core/http/react-ui/package-lock.json

@@ -21,10 +21,9 @@
         "@fortawesome/fontawesome-free": "^6.7.2",
         "@lezer/highlight": "^1.2.1",
         "@modelcontextprotocol/ext-apps": "^1.2.2",
-        "@modelcontextprotocol/sdk": "^1.30.0",
+        "@modelcontextprotocol/sdk": "^1.25.1",
         "dompurify": "^3.4.12",
         "highlight.js": "^11.11.1",
-        "hono": "4.12.34",
         "i18next": "^26.0.8",
         "i18next-browser-languagedetector": "^8.2.1",
         "i18next-http-backend": "^3.0.6",
@@ -636,12 +635,12 @@
       }
     },
     "node_modules/@hono/node-server": {
-      "version": "2.1.0",
-      "resolved": "https://registry.npmjs.org/@hono/node-server/-/node-server-2.1.0.tgz",
-      "integrity": "sha512-XovyyCCnBzW+zKu+z/zq8hwNs4KOR5rEMAOxo2f40Q5xoOI37IMm6MIg2COOUtUApo0i6850MTBKH2u4QLGIqg==",
+      "version": "1.19.14",
+      "resolved": "https://registry.npmjs.org/@hono/node-server/-/node-server-1.19.14.tgz",
+      "integrity": "sha512-GwtvgtXxnWsucXvbQXkRgqksiH2Qed37H9xHZocE5sA3N8O8O8/8FA3uclQXxXVzc9XBZuEOMK7+r02FmSpHtw==",
       "license": "MIT",
       "engines": {
-        "node": ">=20"
+        "node": ">=18.14.1"
       },
       "peerDependencies": {
         "hono": "^4"
@@ -945,12 +944,11 @@
       }
     },
     "node_modules/@modelcontextprotocol/sdk": {
-      "version": "1.30.0",
-      "resolved": "https://registry.npmjs.org/@modelcontextprotocol/sdk/-/sdk-1.30.0.tgz",
-      "integrity": "sha512-xKd8OIzlqNzcqcNumGAa6g+PW2kjD5vrpcKOnfldAUPP3j7lnqMPwlTXQm8gF+UwH72z0lqaRbjr9hqGz0eITA==",
-      "license": "MIT",
+      "version": "1.27.1",
+      "resolved": "https://registry.npmjs.org/@modelcontextprotocol/sdk/-/sdk-1.27.1.tgz",
+      "integrity": "sha512-sr6GbP+4edBwFndLbM60gf07z0FQ79gaExpnsjMGePXqFcSSb7t6iscpjk9DhFhwd+mTEQrzNafGP8/iGGFYaA==",
       "dependencies": {
-        "@hono/node-server": "^1.19.9 || ^2.0.5",
+        "@hono/node-server": "^1.19.9",
         "ajv": "^8.17.1",
         "ajv-formats": "^3.0.1",
         "content-type": "^1.0.5",
@@ -1720,11 +1718,10 @@
       "dev": true
     },
     "node_modules/brace-expansion": {
-      "version": "1.1.18",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.18.tgz",
-      "integrity": "sha512-Edep/X9fGqVNmzKBVsDYIOtD+z1tuezV70LBjdCst9Tqu76lsnvRiZ6oTic1n+/BIwX6QDGAO94PN4N2SADvtw==",
+      "version": "1.1.12",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz",
+      "integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0",
         "concat-map": "0.0.1"
@@ -2879,9 +2876,9 @@
       "dev": true
     },
     "node_modules/fast-uri": {
-      "version": "3.1.5",
-      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.5.tgz",
-      "integrity": "sha512-gHwA1O9LDIcKunMKhObS/HimwtehO1nPUECKAu5TpKgaO19fcWEl4bliWe1jWxVFvIXztJjjQ4L8XQ1EU9f7Jw==",
+      "version": "3.1.4",
+      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.4.tgz",
+      "integrity": "sha512-8JnbkQ4juDyvYs4mgFGQqg4yCYtFDtUtmp2QIQq11ZZe5CFQ5wcqm1rqDgAh/QdMySuBnPzMUiJUNZG5N/AiQw==",
       "funding": [
         {
           "type": "github",
@@ -3435,9 +3432,9 @@
       }
     },
     "node_modules/hono": {
-      "version": "4.12.34",
-      "resolved": "https://registry.npmjs.org/hono/-/hono-4.12.34.tgz",
-      "integrity": "sha512-GqXJqY/xJkJmuloTrnV1ZEXG3fqte+VjkUqoRNZXcrUidiUOP4fMSIHHY4tsqZBK++kVyWmt/AAfSUuy57/eSA==",
+      "version": "4.12.31",
+      "resolved": "https://registry.npmjs.org/hono/-/hono-4.12.31.tgz",
+      "integrity": "sha512-zJIHFrl6bq3RDd2YusFNCDlM8qUprxKswyi/OPzPyzKDdyBXDqWx8bZlZ7R+saTdSTatUmb3O7K4SspGPaEOQg==",
       "license": "MIT",
       "engines": {
         "node": ">=16.9.0"
@@ -4196,9 +4193,9 @@
       "integrity": "sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ=="
     },
     "node_modules/ip-address": {
-      "version": "10.4.0",
-      "resolved": "https://registry.npmjs.org/ip-address/-/ip-address-10.4.0.tgz",
-      "integrity": "sha512-oSK96Grm3aP6OrS263xVxbNDGVL7rzBtYdpGqlDG8iQdoenDoTs/nkki+DflYbAEE8Xl6o5YxhxlrKvI3nqKXQ==",
+      "version": "10.2.0",
+      "resolved": "https://registry.npmjs.org/ip-address/-/ip-address-10.2.0.tgz",
+      "integrity": "sha512-/+S6j4E9AHvW9SWMSEY9Xfy66O5PWvVEJ08O0y5JGyEKQpojb0K0GKpz/v5HJ/G0vi3D2sjGK78119oXZeE0qA==",
       "license": "MIT",
       "engines": {
         "node": ">= 12"
@@ -4386,16 +4383,16 @@
       }
     },
     "node_modules/istanbul-lib-processinfo/node_modules/brace-expansion": {
-      "version": "5.0.9",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.9.tgz",
-      "integrity": "sha512-ScQ4IuvIEF1TMlP7Zt+vjJ//9zlPb2SDcxWxM3bk8s6t6GGdJ7KO1dCcTidOPJKePW30LE/2cT7wCyPho9/Wxg==",
+      "version": "5.0.6",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.6.tgz",
+      "integrity": "sha512-kLpxurY4Z4r9sgMsyG0Z9uzsBlgiU/EFKhj/h91/8yHu0edo7XuixOIH3VcJ8kkxs6/jPzoI6U9Vj3WqbMQ94g==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^4.0.2"
       },
       "engines": {
-        "node": "20 || >=22"
+        "node": "18 || 20 || >=22"
       }
     },
     "node_modules/istanbul-lib-processinfo/node_modules/glob": {
@@ -5281,16 +5278,16 @@
       }
     },
     "node_modules/nyc/node_modules/brace-expansion": {
-      "version": "5.0.9",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.9.tgz",
-      "integrity": "sha512-ScQ4IuvIEF1TMlP7Zt+vjJ//9zlPb2SDcxWxM3bk8s6t6GGdJ7KO1dCcTidOPJKePW30LE/2cT7wCyPho9/Wxg==",
+      "version": "5.0.6",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.6.tgz",
+      "integrity": "sha512-kLpxurY4Z4r9sgMsyG0Z9uzsBlgiU/EFKhj/h91/8yHu0edo7XuixOIH3VcJ8kkxs6/jPzoI6U9Vj3WqbMQ94g==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^4.0.2"
       },
       "engines": {
-        "node": "20 || >=22"
+        "node": "18 || 20 || >=22"
       }
     },
     "node_modules/nyc/node_modules/convert-source-map": {
@@ -5977,11 +5974,10 @@
       }
     },
     "node_modules/quick-temp/node_modules/brace-expansion": {
-      "version": "2.1.4",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.1.4.tgz",
-      "integrity": "sha512-hGfVzPxthbf3+2yjg/RBs60cB0FhqBS/zvdV/4wn4/BmN0bNMMHPc4V/BbFieqf1TKAGGAHnY4eSjajCl0f2Xg==",
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.1.0.tgz",
+      "integrity": "sha512-TN1kCZAgdgweJhWWpgKYrQaMNHcDULHkWwQIspdtjV4Y5aurRdZpjAqn6yX3FPqTA9ngHCc4hJxMAMgGfve85w==",
       "dev": true,
-      "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0"
       }
@@ -6573,16 +6569,16 @@
       }
     },
     "node_modules/spawn-wrap/node_modules/brace-expansion": {
-      "version": "5.0.9",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.9.tgz",
-      "integrity": "sha512-ScQ4IuvIEF1TMlP7Zt+vjJ//9zlPb2SDcxWxM3bk8s6t6GGdJ7KO1dCcTidOPJKePW30LE/2cT7wCyPho9/Wxg==",
+      "version": "5.0.6",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.6.tgz",
+      "integrity": "sha512-kLpxurY4Z4r9sgMsyG0Z9uzsBlgiU/EFKhj/h91/8yHu0edo7XuixOIH3VcJ8kkxs6/jPzoI6U9Vj3WqbMQ94g==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^4.0.2"
       },
       "engines": {
-        "node": "20 || >=22"
+        "node": "18 || 20 || >=22"
       }
     },
     "node_modules/spawn-wrap/node_modules/foreground-child": {
@@ -6906,16 +6902,16 @@
       }
     },
     "node_modules/test-exclude/node_modules/brace-expansion": {
-      "version": "5.0.9",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.9.tgz",
-      "integrity": "sha512-ScQ4IuvIEF1TMlP7Zt+vjJ//9zlPb2SDcxWxM3bk8s6t6GGdJ7KO1dCcTidOPJKePW30LE/2cT7wCyPho9/Wxg==",
+      "version": "5.0.6",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.6.tgz",
+      "integrity": "sha512-kLpxurY4Z4r9sgMsyG0Z9uzsBlgiU/EFKhj/h91/8yHu0edo7XuixOIH3VcJ8kkxs6/jPzoI6U9Vj3WqbMQ94g==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^4.0.2"
       },
       "engines": {
-        "node": "20 || >=22"
+        "node": "18 || 20 || >=22"
       }
     },
     "node_modules/test-exclude/node_modules/glob": {
@@ -7138,9 +7134,9 @@
       }
     },
     "node_modules/undici": {
-      "version": "7.29.0",
-      "resolved": "https://registry.npmjs.org/undici/-/undici-7.29.0.tgz",
-      "integrity": "sha512-IDxfleLmmbSskfWSUATiN1nfn2rDuvnMOqb5CWR92iIfojA0Ud+ulOAAEQ57LPr9rWmsreUyf5lwyao+7GNNVw==",
+      "version": "7.28.0",
+      "resolved": "https://registry.npmjs.org/undici/-/undici-7.28.0.tgz",
+      "integrity": "sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==",
       "dev": true,
       "license": "MIT",
       "engines": {

core/http/react-ui/package.json

@@ -19,7 +19,7 @@
     "coverage:report": "nyc report"
   },
   "overrides": {
-    "hono": "4.12.34"
+    "hono": "4.12.25"
   },
   "dependencies": {
     "@codemirror/autocomplete": "^6.18.6",
@@ -35,10 +35,10 @@
     "@fortawesome/fontawesome-free": "^6.7.2",
     "@lezer/highlight": "^1.2.1",
     "@modelcontextprotocol/ext-apps": "^1.2.2",
-    "@modelcontextprotocol/sdk": "^1.30.0",
+    "@modelcontextprotocol/sdk": "^1.25.1",
     "dompurify": "^3.4.12",
     "highlight.js": "^11.11.1",
-    "hono": "4.12.34",
+    "hono": "4.12.25",
     "i18next": "^26.0.8",
     "i18next-browser-languagedetector": "^8.2.1",
     "i18next-http-backend": "^3.0.6",

core/http/react-ui/src/pages/Traces.jsx

@@ -664,16 +664,10 @@ export default function Traces() {
                     <td><span className="badge badge-info">{trace.request?.method || '-'}</span></td>
                     <td className="text-mono text-sm">{trace.request?.path || '-'}</td>
                     <td className="text-sub cell-clip" title={trace.user_name || trace.user_id || ''}>{trace.user_name || trace.user_id || '-'}</td>
-                    <td>
-                      {trace.response?.status === 0
-                        ? <span className="badge badge-info">Running</span>
-                        : <span className={`badge ${trace.response.status < 400 ? 'badge-success' : 'badge-error'}`}>{trace.response.status}</span>}
-                    </td>
+                    <td><span className={`badge ${(trace.response?.status || 0) < 400 ? 'badge-success' : 'badge-error'}`}>{trace.response?.status || '-'}</span></td>
                     <td><LatencyCell ns={trace.duration} max={slowestTrace} /></td>
                     <td className="text-center">
-                      {trace.response?.status === 0
-                        ? <i className="fas fa-spinner fa-spin text-primary" title="In progress" />
-                        : trace.error
+                      {trace.error
                         ? <i className="fas fa-times-circle text-error" title={trace.error} />
                         : <i className="fas fa-check-circle text-success" />}
                     </td>

core/services/advisorylock/leader_loop_test.go

@@ -54,57 +54,62 @@ var _ = Describe("RunLeaderLoop", func() {
 				close(done)
 			}()
 
-			Eventually(func() int32 {
-				return atomic.LoadInt32(&callCount)
-			}, 500*time.Millisecond, 10*time.Millisecond).Should(BeNumerically(">=", 1))
+			// Let it run a bit then cancel
+			time.Sleep(150 * time.Millisecond)
 			cancel()
 
+			// RunLeaderLoop should return
 			Eventually(done, 500*time.Millisecond).Should(BeClosed())
+
+			// Record count after cancellation
+			countAfterCancel := atomic.LoadInt32(&callCount)
+			time.Sleep(150 * time.Millisecond)
+			countLater := atomic.LoadInt32(&callCount)
+
+			Expect(countLater).To(Equal(countAfterCancel),
+				"function should stop being called after context cancellation")
 		})
 
 		It("only one leader executes at a time (two concurrent loops)", func() {
 			db := testutil.SetupTestDB()
 			const lockKey int64 = 5002
 
-			var running int32
-			entered := make(chan struct{}, 2)
-			release := make(chan struct{})
-			var releaseOnce sync.Once
+			var (
+				mu         sync.Mutex
+				maxRunning int32
+				running    int32
+			)
 
 			ctx, cancel := context.WithCancel(context.Background())
-			done := make(chan struct{}, 2)
-			DeferCleanup(func() {
-				cancel()
-				releaseOnce.Do(func() { close(release) })
-			})
+			defer cancel()
 
 			fn := func() {
-				atomic.AddInt32(&running, 1)
-				select {
-				case entered <- struct{}{}:
-				default:
+				cur := atomic.AddInt32(&running, 1)
+				mu.Lock()
+				if cur > maxRunning {
+					maxRunning = cur
 				}
-				<-release
+				mu.Unlock()
+
+				time.Sleep(30 * time.Millisecond)
+
 				atomic.AddInt32(&running, -1)
 			}
 
-			for range 2 {
-				go func() {
-					RunLeaderLoop(ctx, db, lockKey, 1*time.Millisecond, fn)
-					done <- struct{}{}
-				}()
-			}
-
-			Eventually(entered, 500*time.Millisecond).Should(Receive())
-			Consistently(func() int32 {
-				return atomic.LoadInt32(&running)
-			}, 50*time.Millisecond, 5*time.Millisecond).Should(Equal(int32(1)),
-				"expected only the lock holder to run while both loops tick")
+			// Start two competing leader loops with the same lock key
+			go RunLeaderLoop(ctx, db, lockKey, 50*time.Millisecond, fn)
+			go RunLeaderLoop(ctx, db, lockKey, 50*time.Millisecond, fn)
 
+			// Let them run for a while
+			time.Sleep(400 * time.Millisecond)
 			cancel()
-			releaseOnce.Do(func() { close(release) })
-			Eventually(done, 500*time.Millisecond).Should(Receive())
-			Eventually(done, 500*time.Millisecond).Should(Receive())
+
+			mu.Lock()
+			observed := maxRunning
+			mu.Unlock()
+
+			Expect(observed).To(BeNumerically("<=", 1),
+				"expected at most 1 goroutine running the leader function at a time")
 		})
 	})
 })

docs/content/features/backends.md

@@ -72,44 +72,6 @@ tags:
   - "text-generation"
 ```
 
-### Verifying OCI Backends
-
-Backend galleries can require keyless Sigstore signatures for every OCI image
-they provide. Add a `verification` policy to the gallery configuration, then
-enable strict integrity mode:
-
-```bash
-export LOCALAI_BACKEND_GALLERIES='[{"name":"localai","url":"github:mudler/LocalAI/backend/index.yaml@master","verification":{"issuer":"https://token.actions.githubusercontent.com","identity_regex":"^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@refs/(heads/master|tags/.+)$"}}]'
-export LOCALAI_REQUIRE_BACKEND_INTEGRITY=1
-local-ai run
-```
-
-The policy pins the Fulcio issuer and the GitHub Actions workflow identity that
-signed the image. The identity expression covers development images produced
-from `master` and release images produced from tags. Use a narrower expression
-if your deployment only accepts one release channel.
-
-Without strict mode, an OCI gallery without a verification policy installs
-with a warning. With strict mode, LocalAI refuses galleries without a policy,
-images without a compatible Sigstore bundle, and signatures that do not match
-the configured identity. Existing images published before bundle signing was
-enabled must be rebuilt or re-signed before strict deployments can install
-them.
-
-An optional `not_before` RFC3339 value revokes signatures logged before that
-time. Advance it after a signing-workflow compromise, then rebuild or re-sign
-the trusted images:
-
-```json
-{
-  "verification": {
-    "issuer": "https://token.actions.githubusercontent.com",
-    "identity_regex": "^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@refs/(heads/master|tags/.+)$",
-    "not_before": "2026-08-05T00:00:00Z"
-  }
-}
-```
-
 ## Pre-installing Backends
 
 You can pre-install backends when starting LocalAI using the `LOCALAI_EXTERNAL_BACKENDS` environment variable:

docs/content/features/text-generation.md

@@ -685,6 +685,83 @@ The `cache_type_k` / `cache_type_v` fields map to llama.cpp's `-ctk` / `-ctv` fl
 - [Tracked branch: `feature/turboquant-kv-cache`](https://github.com/TheTom/llama-cpp-turboquant/tree/feature/turboquant-kv-cache)
 
 
+### buun-llama-cpp (DFlash speculative decoding + TurboQuant/TCQ KV-cache)
+
+[buun-llama-cpp](https://github.com/spiritbuun/buun-llama-cpp) is a fork-of-a-fork: spiritbuun forked `TheTom/llama-cpp-turboquant` (the `turboquant` backend above) and added two independent features on top:
+
+1. **DFlash** — a block-diffusion speculative decoding scheme that uses a dedicated drafter model (new `DFlashDraftModel` GGUF architecture). On a target/drafter pair it emits a block of tokens per speculation step and can be combined with tree-structured verification ("DDTree") for multi-branch draft expansion.
+2. **TCQ (Trellis-Coded Quantization)** — two additional KV-cache types (`turbo2_tcq`, `turbo3_tcq`) on top of the TurboQuant `turbo2` / `turbo3` / `turbo4` already shipped by the parent fork, delivering 10–44% KL reduction over scalar quantization at 2–3 bits per value.
+
+Like `turboquant`, this backend shares LocalAI's stock `llama-cpp` gRPC server sources — so any GGUF model that runs on `llama-cpp` also runs on `buun-llama-cpp`. Pick it over `turboquant` specifically when you want DFlash speculative decoding or the newer TCQ KV-cache variants.
+
+#### Features
+
+- Drop-in GGUF compatibility with upstream `llama.cpp`.
+- DFlash block-diffusion speculative decoding (CUDA/Metal; no CPU fallback).
+- TurboQuant KV-cache types (`turbo2`, `turbo3`, `turbo4`) inherited from the parent `turboquant` fork, plus buun-exclusive `turbo2_tcq` and `turbo3_tcq` variants.
+- Same feature surface as `llama-cpp`: text generation, embeddings, tool calls, multimodal via mmproj.
+- Available on CPU (AVX/AVX2/AVX512/fallback), NVIDIA CUDA 12/13, AMD ROCm/HIP, Intel SYCL f32/f16, Vulkan, and NVIDIA L4T — but note that DFlash and `turbo*` KV types have no CPU fallback and error at model-load on CPU-only builds.
+
+#### Setup
+
+`buun-llama-cpp` ships as a separate container image in the LocalAI backend gallery. Install it like any other backend:
+
+```bash
+local-ai backends install buun-llama-cpp
+```
+
+Or pick a specific flavor for your hardware (example tags: `cpu-buun-llama-cpp`, `cuda12-buun-llama-cpp`, `cuda13-buun-llama-cpp`, `rocm-buun-llama-cpp`, `intel-sycl-f16-buun-llama-cpp`, `vulkan-buun-llama-cpp`).
+
+#### YAML configuration — TCQ KV-cache
+
+To run a model with TurboQuant/TCQ quantized KV-cache, set the backend and pick a `turbo*` cache type:
+
+```yaml
+name: my-model
+backend: buun-llama-cpp
+parameters:
+  model: file.gguf
+# Accepted values for the two fork-aware backends include the stock llama.cpp
+# types (f16, f32, q8_0, q4_0, q4_1, q5_0, q5_1), the TurboQuant types
+# (turbo2, turbo3, turbo4), and the buun-only TCQ variants (turbo2_tcq,
+# turbo3_tcq). turbo3 / turbo4 / turbo*_tcq auto-enable flash_attention.
+cache_type_k: turbo3
+cache_type_v: turbo3_tcq
+context_size: 8192
+```
+
+#### YAML configuration — DFlash speculative decoding
+
+DFlash requires a **dedicated drafter model** in the new `DFlashDraftModel` GGUF architecture. At time of writing the only known public target/drafter pair is [`z-lab/Qwen3.5-27B`](https://huggingface.co/z-lab/Qwen3.5-27B) + [`z-lab/Qwen3.5-27B-DFlash`](https://huggingface.co/z-lab/Qwen3.5-27B-DFlash).
+
+```yaml
+name: qwen3-dflash
+backend: buun-llama-cpp
+parameters:
+  # Target model (quantized as usual)
+  model: Qwen3.5-27B-Q4_K_M.gguf
+# Drafter model produced by buun's convert_hf_to_gguf.py from the
+# DFlashDraftModel checkpoint. Resolved relative to the models path.
+draft_model: Qwen3.5-27B-DFlash.gguf
+options:
+  # Switches the speculative pipeline from the default draft-model mode to
+  # DFlash (block-diffusion). Required to activate the DFlash code path.
+  - spec_type:dflash
+  # Optional tuning:
+  # - tree_budget:0      # 0 = flat DFlash; >0 = DDTree verification budget
+  # - draft_topk:1       # drafter top-K per position (1 = argmax)
+  # - spec_n_max:16      # cap on draft tokens per speculation step
+```
+
+Under the hood LocalAI wires `draft_model` through to the grpc-server's `params.speculative.mparams_dft.path`, and `spec_type:dflash` is forwarded through the options passthrough to buun's `common_speculative_type_from_name("dflash")`. The `tree_budget` and `draft_topk` options are buun-exclusive; they reference struct fields that only exist in buun's fork, so they're surfaced on this backend only (passing them to stock `llama-cpp` is a no-op).
+
+#### Reference
+
+- [spiritbuun/buun-llama-cpp](https://github.com/spiritbuun/buun-llama-cpp)
+- [TCQ paper / dataset](https://huggingface.co/datasets/spiritbuun/turboquant-tcq-kv-cache) — *"Closing the Gap: Trellis-Coded Quantization for KV Cache at 2-3 Bits"*
+- DFlash target/drafter pair: [`z-lab/Qwen3.5-27B`](https://huggingface.co/z-lab/Qwen3.5-27B) + [`z-lab/Qwen3.5-27B-DFlash`](https://huggingface.co/z-lab/Qwen3.5-27B-DFlash)
+
+
 ### vLLM
 
 [vLLM](https://github.com/vllm-project/vllm) is a fast and easy-to-use library for LLM inference.

docs/content/features/tracing.md

@@ -9,11 +9,6 @@ LocalAI can retain recent API exchanges and backend operations for inspection
 on the **Traces** page in the management interface. Enable tracing in runtime
 settings or with the existing tracing configuration.
 
-API requests appear while they are still running. Their elapsed duration
-updates when the page refreshes, and the result column marks them as in
-progress until the response completes. In-flight requests live only in memory;
-the completed exchange is what LocalAI adds to the bounded, persistent history.
-
 API and backend trace histories are persisted in separate directories below
 the configured data path. They are restored after a clean service restart,
 whether or not authentication is enabled.

docs/content/reference/compatibility-table.md

@@ -23,6 +23,7 @@ All backends listed here can be installed on demand from the [Backend Gallery]({
 | [llama.cpp](https://github.com/ggerganov/llama.cpp) | LLM inference in C/C++. Supports LLaMA, Mamba, RWKV, Falcon, Starcoder, GPT-2, [and many others](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#description) | GPT, Functions | yes | yes | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T |
 | [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp) | Hard fork of llama.cpp optimized for CPU/hybrid CPU+GPU with IQK quants, custom quant mixes, and MLA for DeepSeek | GPT | yes | yes | CPU (AVX2+) |
 | [turboquant](https://github.com/TheTom/llama-cpp-turboquant) | llama.cpp fork adding the TurboQuant KV-cache quantization scheme | GPT | yes | yes | CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Jetson L4T |
+| [buun-llama-cpp](https://github.com/spiritbuun/buun-llama-cpp) | llama.cpp fork with DFlash block-diffusion speculative decoding and TurboQuant/TCQ KV-cache quantization (2–3 bits per value). Accelerated paths are CUDA/Metal only. | GPT, Functions | yes | yes | CUDA, Metal (CPU fallback for non-turbo/non-DFlash only) |
 | [ds4](https://github.com/antirez/ds4) | DeepSeek V4 Flash single-model inference engine, optimized for Metal and CUDA | GPT | no | yes | CPU, CUDA 12/13, Metal, Jetson L4T |
 | [vllm.cpp](https://github.com/mudler/vllm.cpp) | From-scratch C++20 port of vLLM by the LocalAI team: paged KV cache, continuous batching, prefix caching, safetensors + GGUF, engine-enforced structured output, no Python at inference | GPT, Functions | no | yes | CPU, CUDA 12/13 (Blackwell-family), Vulkan, Metal, Jetson L4T (GB10) |
 | [vLLM](https://github.com/vllm-project/vllm) | Fast LLM serving with PagedAttention; GPTQ/AWQ/FP8 quantization | GPT, Functions, Multimodal | no | yes | CUDA 12/13, ROCm, Intel SYCL, Jetson L4T |

docs/content/whats-new.md

@@ -6,11 +6,27 @@ url = '/basics/news/'
 icon = "newspaper"
 +++
 
-LocalAI news is published in two places, both kept current:
+Release notes have been now moved completely over Github releases.
 
-- **[Blog](https://localai.io/blog/)** for release write-ups, benchmark reports and engineering notes.
-- **[GitHub Releases](https://github.com/mudler/LocalAI/releases)** for the full changelog of every version.
+You can see the release notes [here](https://github.com/mudler/LocalAI/releases).
 
-For how the project got here, read [LocalAI, from March 2023 to now](https://localai.io/blog/localai-since-march-2023/).
+## 2026 Highlights
 
-This page used to carry a hand-maintained highlights list. It drifted against both sources above, so it now points at them instead.
+- **August 2026**: [Text moderation](/features/moderation/) - new OpenAI-compatible `POST /v1/moderations` endpoint. It uses any local completion model with a constrained JSON grammar and returns the standard safety categories, scores, and per-input flags.
+- **July 2026**: [LongCat video and avatar generation](/features/video-generation/) - dedicated CUDA backend for `LongCat-Video` text/image-to-video and `LongCat-Video-Avatar-1.5` speech-driven avatars. Includes multi-segment continuation, portrait and recorded-audio inputs in Studio, and an SDPA CUDA 13 ARM64 build for DGX Spark.
+- **April 2026**: [Audio Transform](/features/audio-transform/) - generic audio-in / audio-out endpoint with optional reference signal. First implementation: [LocalVQE](https://github.com/localai-org/LocalVQE) C++ backend (joint AEC + noise suppression + dereverberation, DeepVQE-style). Both batch (`POST /audio/transformations`) and bidirectional WebSocket streaming (`/audio/transformations/stream`). Studio "Transform" tab with synchronized waveform players for input / reference / output.
+- **April 2026**: [Face recognition backend](/features/face-recognition/) - `insightface`-powered 1:1 verification, 1:N identification, face embedding, face detection, and demographic analysis. Ships both a non-commercial `buffalo_l` model and an Apache 2.0 OpenCV Zoo alternative.
+- **May 2026**: [Speaker diarization](/features/audio-diarization/) - new `/v1/audio/diarization` endpoint returning "who spoke when" segments. Backed by `sherpa-onnx` (pyannote-3.0 + speaker embeddings + clustering) for pure diarization, and `vibevoice-cpp` for diarization bundled with long-form ASR. Supports `json` / `verbose_json` / `rttm` response formats.
+- **June 2026**: [Sound classification](/features/audio-classification/) - new `/v1/audio/classification` endpoint for audio tagging / sound-event classification, returning scored [AudioSet](https://research.google.com/audioset/) labels (baby cry, glass breaking, alarms, ...). Backed by [ced.cpp](https://github.com/localai-org/ced.cpp), a 527-class AudioSet tagger ported to ggml.
+- **June 2026**: [PII analyze / redact API](/features/middleware/#analyze--redact-api) - the PII detection pipeline (NER + restricted-regex pattern tiers) is now a standalone service: `POST /api/pii/analyze` returns detected entity spans and `POST /api/pii/redact` returns the sanitised text (or `400 pii_blocked`), without routing a chat request through the middleware. Events gain an `origin` (`middleware` / `proxy` / `pii_analyze` / `pii_redact`) so `/api/pii/events` can be filtered by source.
+- **July 2026**: [Model capabilities endpoint](/features/api-discovery/#model-capabilities) - `GET /v1/models/capabilities`, an additive superset of `/v1/models` that reports each model's `capabilities` plus its `input_modalities` / `output_modalities` (`text` / `image` / `audio` / `video`). Lets clients route attachments using inferred or explicitly declared model modalities instead of backend-name checks.
+- **June 2026**: Concurrent scoring and PII NER on llama.cpp - the `Score` (router classifier) and `TokenClassify` (PII NER) primitives now ride llama.cpp's server task queue instead of locking the context, so they run concurrently with chat/completion/embedding traffic and with each other. The `known_usecases` restriction that forced dedicated scorer/NER model configs on llama-cpp is lifted, repeated scoring calls reuse the prompt KV cache across candidates, and scoring inputs are no longer capped by the physical batch size.
+
+## 2024 Highlights
+
+- **April 2024**: [Reranker API](https://github.com/mudler/LocalAI/pull/2121)
+- **May 2024**: [Distributed inferencing](https://github.com/mudler/LocalAI/pull/2324), [Decentralized P2P llama.cpp](https://github.com/mudler/LocalAI/pull/2343) - [Docs](https://localai.io/features/distribute/)
+- **July/August 2024**: [P2P Dashboard, Federated mode and AI Swarms](https://github.com/mudler/LocalAI/pull/2723), [P2P Global community pools](https://github.com/mudler/LocalAI/issues/3113), FLUX-1 support, [P2P Explorer](https://explorer.localai.io)
+- **October 2024**: Examples moved to [LocalAI-examples](https://github.com/mudler/LocalAI-examples)
+- **November 2024**: [Voice Activity Detection (VAD)](https://github.com/mudler/LocalAI/pull/4204), [Bark.cpp backend](https://github.com/mudler/LocalAI/pull/4287)
+- **December 2024**: [stablediffusion.cpp backend (ggml)](https://github.com/mudler/LocalAI/pull/4289)

gallery/index.yaml

@@ -189,7 +189,7 @@
   files:
     - filename: DeepSeek-V4-Flash-0731-MXFP4.gguf
       uri: huggingface://ggml-org/DeepSeek-V4-Flash-0731-GGUF/DeepSeek-V4-Flash-0731-MXFP4.gguf
-      sha256: 65f73494afaf27d3add0751a5b716dd2d3e012c66ae0dbbcc1bf8477f92b3ab7
+      sha256: c8b46876c3939a6e141f9e4d4aa422981df4a9b84f19e9bb4e1c9a28be31e484
 - name: instella-moe-16b-a3b-think
   url: github:mudler/LocalAI/gallery/virtual.yaml@master
   urls:
@@ -311,7 +311,7 @@
   files:
     - filename: llama-cpp/models/Parable-Granite-4.1-3B-Claude-Fable-5-Q4_K_M/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF-Q4_K_M.gguf
       uri: https://huggingface.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF/resolve/main/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF-Q4_K_M.gguf
-      sha256: dbf202638af23e72508d8316577655d24ba2037fda51ce802b8996977e290bce
+      sha256: 67dc7695d92939c713165761f115c9d892fdff74fcbd987c8bb453b9b8ab645d
 - name: "parable-qwen3-4b-claude-fable-5"
   url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:
@@ -345,7 +345,7 @@
   files:
     - filename: llama-cpp/models/Parable-Qwen3-4B-Claude-Fable-5-Q4_K_M/Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q4_K_M.gguf
       uri: https://huggingface.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF/resolve/main/Parable-Qwen3-4B-Claude-Fable-5-GGUF-Q4_K_M.gguf
-      sha256: 65cc4824fb78ecaf55afdfcdb6dd2e27e1aa805d289db89eae94d32d450403f0
+      sha256: c94b06a912aa901f3da5689754577ad534415efafc50dcee3f389594a153bf38
 - name: "parable-granite-4.1-8b-claude-fable-5"
   url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:
@@ -381,7 +381,7 @@
   files:
     - filename: llama-cpp/models/Parable-Granite-4.1-8B-Claude-Fable-5-Q4_K_M/Parable-Granite-4.1-8B-Claude-Fable-5-GGUF-Q4_K_M.gguf
       uri: https://huggingface.co/AnkitAI/Parable-Granite-4.1-8B-Claude-Fable-5-GGUF/resolve/main/Parable-Granite-4.1-8B-Claude-Fable-5-GGUF-Q4_K_M.gguf
-      sha256: 57e464ae3d35253d4351639757dc35e71bab8324d12d49a5870695ce73dc19cf
+      sha256: 61a8133c344a0d0a00188395afe33c803e3b973cb4bbfd5ef1fa7110e80bc1c3
 - name: "parable-qwen3-8b-claude-fable-5"
   url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:
@@ -415,7 +415,7 @@
   files:
     - filename: llama-cpp/models/Parable-Qwen3-8B-Claude-Fable-5-Q4_K_M/Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q4_K_M.gguf
       uri: https://huggingface.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF/resolve/main/Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q4_K_M.gguf
-      sha256: 4532d2379d38a37279866a030e51d419561f9d4d22fee00d2a33647d66f05065
+      sha256: 956070afc8023b8665fe450842f7be76b505b53d142460fd9b588222f4e16112
 - &pocket-35b
   name: "pocket-35b"
   variants:
@@ -785,18 +785,35 @@
 - name: "qwen3.6-35b-a3b-uncensored-genesis-hermes-v6"
   url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:
-    - https://huggingface.co/HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive
     - https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V6-GGUF
   description: |
-    Qwen3.6-35B-A3B Uncensored Genesis Hermes V6 is LuffyTheFox's multimodal,
-    agentic derivative of HauhauCS's uncensored Qwen3.6-35B-A3B model. It
-    combines Genesis tensor calibration with Hermes function-calling data while
-    retaining the 35B mixture-of-experts architecture, roughly 3B active
-    parameters per token, and the native 262K-token context window.
+    # Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive
 
-    This entry installs the Q8_0 GGUF together with its F16 multimodal projector
-    for llama.cpp. The model card recommends Jinja chat templates and at least a
-    128K context for its thinking behavior. License: Apache-2.0.
+    > **Join the Discord** for updates, roadmaps, projects, or just to chat.
+
+    Qwen3.6-35B-A3B uncensored by HauhauCS. **0/465 Refusals.**
+
+    > **HuggingFace's "Hardware Compatibility" widget doesn't recognize K_P quants** — it may show fewer files than actually exist. Click **"View +X variants"** or go to **Files and versions** to see all available downloads.
+
+    ## About
+
+    No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals.
+
+    These are meant to be the best lossless uncensored models out there.
+
+    ## Aggressive Variant
+
+    Stronger uncensoring — model is fully unlocked and won't refuse prompts. May occasionally append short disclaimers (baked into base model training, not refusals) but full content is always generated.
+
+    For a more conservative uncensor that keeps some safety guardrails, check the Balanced variant when it's available.
+
+    ## Downloads
+
+    All quants generated with importance matrix (imatrix) for optimal quality preservation on abliterated weights.
+
+    ## What are K_P quants?
+
+    ...
   license: "apache-2.0"
   tags:
     - llm
@@ -1992,7 +2009,7 @@
   files:
     - filename: ds4flash.gguf
       uri: https://huggingface.co/unsloth/DeepSeek-V4-Flash-GGUF
-      sha256: ba1d64ad8d77038124839956b614db2e889daa1a4ddc83060bb06ccb5a1d7461
+      sha256: 856c407993ccffa9ad52e23fbef8bb7b458c792a52278f4ca7931741b0c20ce2
 - name: "qwopus3.6-35b-a3b-coder-mtp"
   url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:
@@ -2091,83 +2108,6 @@
     - filename: llama-cpp/models/Qwen-AgentWorld-35B-A3B-GGUF/Qwen-AgentWorld-35B-A3B-UD-Q4_K_M.gguf
       sha256: e7a8eafdd8013443b6bcc4b6fb47b2d2025f772d359650b9ceb7d75971e22cad
       uri: https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF/resolve/main/Qwen-AgentWorld-35B-A3B-UD-Q4_K_M.gguf
-- &agents-a1-4b
-  name: "agents-a1-4b"
-  variants:
-    - model: agents-a1-4b-q8
-  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
-  urls:
-    - https://huggingface.co/InternScience/Agents-A1-4B
-    - https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF
-  description: |
-    Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
-    Qwen3.5. It is trained for long-horizon search, engineering and scientific
-    research, instruction following, tool use, and multimodal tasks. This entry
-    uses the official Q4_K_M GGUF quantization and vision projector.
-  license: "apache-2.0"
-  tags:
-    - llm
-    - gguf
-    - vision
-    - multimodal
-    - gpu
-    - cpu
-  icon: https://huggingface.co/InternScience/Agents-A1-4B/resolve/main/figures/logo_nobg.png
-  overrides:
-    backend: llama-cpp
-    function:
-      automatic_tool_parsing_fallback: true
-      grammar:
-        disable: true
-    known_usecases:
-      - chat
-    mmproj: llama-cpp/mmproj/Agents-A1-4B-Q4_K_M/Agents-A1-4B-mmproj.gguf
-    options:
-      - use_jinja:true
-    parameters:
-      model: llama-cpp/models/Agents-A1-4B-Q4_K_M/Agents-A1-4B-Q4_K_M.gguf
-    template:
-      use_tokenizer_template: true
-  files:
-    - filename: llama-cpp/models/Agents-A1-4B-Q4_K_M/Agents-A1-4B-Q4_K_M.gguf
-      sha256: d93c393a9bd5139a4b5cfe24d31ef553c5a497bfb8afec178a354ecbf508f062
-      uri: huggingface://InternScience/Agents-A1-4B-Q4_K_M-GGUF/Agents-A1-4B-Q4_K_M.gguf
-    - filename: llama-cpp/mmproj/Agents-A1-4B-Q4_K_M/Agents-A1-4B-mmproj.gguf
-      sha256: 254145e7e03e9e8d3120813fac8033ffa04e411eb6d70a198833504935681084
-      uri: huggingface://InternScience/Agents-A1-4B-Q4_K_M-GGUF/Agents-A1-4B-mmproj.gguf
-- !!merge <<: *agents-a1-4b
-  name: "agents-a1-4b-q8"
-  variants: []
-  urls:
-    - https://huggingface.co/InternScience/Agents-A1-4B
-    - https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF
-  description: |
-    Agents-A1-4B is InternScience's Apache-2.0 dense 4B agentic model, based on
-    Qwen3.5. It is trained for long-horizon search, engineering and scientific
-    research, instruction following, tool use, and multimodal tasks. This entry
-    uses the official Q8_0 GGUF quantization and vision projector.
-  overrides:
-    backend: llama-cpp
-    function:
-      automatic_tool_parsing_fallback: true
-      grammar:
-        disable: true
-    known_usecases:
-      - chat
-    mmproj: llama-cpp/mmproj/Agents-A1-4B-Q8_0/Agents-A1-4B-mmproj.gguf
-    options:
-      - use_jinja:true
-    parameters:
-      model: llama-cpp/models/Agents-A1-4B-Q8_0/Agents-A1-4B-Q8_0.gguf
-    template:
-      use_tokenizer_template: true
-  files:
-    - filename: llama-cpp/models/Agents-A1-4B-Q8_0/Agents-A1-4B-Q8_0.gguf
-      sha256: c327f66e820dae550bd230394595071c79f48c88d411b452d013ee4b5999fcea
-      uri: huggingface://InternScience/Agents-A1-4B-Q8_0-GGUF/Agents-A1-4B-Q8_0.gguf
-    - filename: llama-cpp/mmproj/Agents-A1-4B-Q8_0/Agents-A1-4B-mmproj.gguf
-      sha256: 254145e7e03e9e8d3120813fac8033ffa04e411eb6d70a198833504935681084
-      uri: huggingface://InternScience/Agents-A1-4B-Q8_0-GGUF/Agents-A1-4B-mmproj.gguf
 - name: "ornith-1.0-9b"
   variants:
     - model: ornith-1.0-9b-mtp
@@ -2691,83 +2631,6 @@
     - filename: llama-cpp/models/LFM2.5-1.2B-Instruct-GGUF/LFM2.5-1.2B-Instruct-Q4_K_M.gguf
       sha256: b1b3de114215d9507409a662a501a631095a479a419584e8a2ded6304b19b4f5
       uri: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF/resolve/main/LFM2.5-1.2B-Instruct-Q4_K_M.gguf
-- &lfm2-5-2-6b
-  name: "lfm2.5-2.6b"
-  url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
-  urls:
-    - https://huggingface.co/LiquidAI/LFM2.5-2.6B
-    - https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF
-  description: |
-    LFM2.5-2.6B is LiquidAI's compact, text-only reasoning model for on-device
-    agentic workloads. It has 2.69B parameters, a 128K-token context window,
-    multilingual support, and post-training for tool use, instruction following,
-    data extraction, RAG, and multi-step agents. This entry uses the recommended
-    Q4_K_M GGUF quantization from LiquidAI's official repository.
-  license: "other"
-  tags:
-    - llm
-    - gguf
-    - reasoning
-    - cpu
-    - gpu
-  icon: https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png
-  variants:
-    - model: lfm2.5-2.6b-q8
-  overrides:
-    backend: llama-cpp
-    context_size: 131072
-    function:
-      automatic_tool_parsing_fallback: true
-      grammar:
-        disable: true
-    known_usecases:
-      - chat
-      - completion
-    options:
-      - use_jinja:true
-    parameters:
-      model: llama-cpp/models/LFM2.5-2.6B-GGUF/LFM2.5-2.6B-Q4_K_M.gguf
-      repeat_penalty: 1.1
-      temperature: 0.1
-      top_k: 50
-    template:
-      use_tokenizer_template: true
-  files:
-    - filename: llama-cpp/models/LFM2.5-2.6B-GGUF/LFM2.5-2.6B-Q4_K_M.gguf
-      sha256: 79fdf00351b46cf26f020aead28d01889886be87c55fa0eb907e6f9b00bfee14
-      uri: https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF/resolve/main/LFM2.5-2.6B-Q4_K_M.gguf
-- !!merge <<: *lfm2-5-2-6b
-  name: "lfm2.5-2.6b-q8"
-  description: |
-    LFM2.5-2.6B is LiquidAI's compact, text-only reasoning model for on-device
-    agentic workloads. It has 2.69B parameters, a 128K-token context window,
-    multilingual support, and post-training for tool use, instruction following,
-    data extraction, RAG, and multi-step agents. This entry uses the higher-quality
-    Q8_0 GGUF quantization from LiquidAI's official repository.
-  variants: null
-  overrides:
-    backend: llama-cpp
-    context_size: 131072
-    function:
-      automatic_tool_parsing_fallback: true
-      grammar:
-        disable: true
-    known_usecases:
-      - chat
-      - completion
-    options:
-      - use_jinja:true
-    parameters:
-      model: llama-cpp/models/LFM2.5-2.6B-GGUF/LFM2.5-2.6B-Q8_0.gguf
-      repeat_penalty: 1.1
-      temperature: 0.1
-      top_k: 50
-    template:
-      use_tokenizer_template: true
-  files:
-    - filename: llama-cpp/models/LFM2.5-2.6B-GGUF/LFM2.5-2.6B-Q8_0.gguf
-      sha256: 36587fdf27bdfc69caf2637273679a0870ec155162161bde6fd16e8c70bdb757
-      uri: https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF/resolve/main/LFM2.5-2.6B-Q8_0.gguf
 - name: "qwopus3.6-27b-coder-compat-mtp"
   url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:

scripts/build/backend-signing_test.sh

@@ -1,13 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-WORKFLOW="$(dirname "$(realpath "$0")")/../../.github/workflows/backend_merge.yml"
-
-sign_commands=$(grep -Ec -- '^[[:space:]]+cosign sign([[:space:]]|$)' "$WORKFLOW" || true)
-bundle_flags=$(grep -Ec -- '^[[:space:]]+--new-bundle-format([[:space:]]|$)' "$WORKFLOW" || true)
-if [ "$sign_commands" -ne 2 ] || [ "$bundle_flags" -ne "$sign_commands" ]; then
-  echo "FAIL: every backend signing command must request the new bundle format (commands=$sign_commands flags=$bundle_flags)"
-  exit 1
-fi
-
-echo "PASS: backend signing emits Sigstore bundles for both registries"

scripts/build/llama-cpp-build-target_test.sh

@@ -29,11 +29,4 @@ assert_target arm64 "" llama-cpp-cpu-all
 assert_target amd64 sycl_f16 llama-cpp-fallback
 assert_target amd64 sycl_f32 llama-cpp-fallback
 
-# ROCm exhausts the same 6h budget through volume rather than a stall: hipcc
-# compiles ggml's HIP kernels once per AMDGPU target, eleven of them, and the
-# CPU variant matrix goes on top. 2h27m before it was added, killed at exactly
-# 6h00m on every run since.
-assert_target amd64 hipblas llama-cpp-fallback
-assert_target arm64 hipblas llama-cpp-fallback
-
 echo "PASS: llama.cpp build target preserves CPU variants where supported"

scripts/lib/backend-filter.mjs

@@ -70,6 +70,11 @@ export function inferBackendPath(item) {
     // via a thin wrapper Makefile. Changes to either dir should retrigger it.
     return `backend/cpp/turboquant/`;
   }
+  if (item.dockerfile.endsWith("buun-llama-cpp")) {
+    // buun-llama-cpp is a llama.cpp fork that reuses backend/cpp/llama-cpp
+    // sources via a thin wrapper Makefile. Changes to either dir retrigger it.
+    return `backend/cpp/buun-llama-cpp/`;
+  }
   if (item.dockerfile.endsWith("bonsai")) {
     // bonsai is a llama.cpp fork that reuses backend/cpp/llama-cpp sources
     // via a thin wrapper Makefile. Changes to either dir should retrigger it.
@@ -144,7 +149,7 @@ export function backendChanged(backend, pathPrefix, changedFiles) {
 
   // Fork backends reuse backend/cpp/llama-cpp sources via thin wrappers;
   // changes to either directory must retrigger their pipelines.
-  return (backend === "turboquant" || backend === "bonsai") &&
+  return (backend === "turboquant" || backend === "buun-llama-cpp" || backend === "bonsai") &&
     changedFiles.some(file => file.startsWith("backend/cpp/llama-cpp/"));
 }
 

website/content/blog/_index.md

@@ -1,5 +1,5 @@
 ---
 title: "Blog"
-description: "Release write-ups, benchmark reports and engineering notes from the LocalAI team. Numbers link to the release, commit or benchmark run they came from."
+description: "Release write-ups, benchmark reports and engineering notes from the LocalAI team. Every number here comes out of a benchmark suite, a release or a commit, and the source is named so you can check it."
 extracss: ["blog.css"]
 ---

website/content/blog/apex-moe-quantization.md

@@ -4,13 +4,13 @@ date: 2026-04-10
 author: "Ettore Di Giacinto"
 category: "Research"
 tags: ["quantization", "APEX", "mixture-of-experts", "llama.cpp", "benchmarks"]
-summary: "Qwen3.5-35B-A3B goes from 64.6 GB to 12.2 GB and speeds up from 30.4 to 74.4 tokens per second. Perplexity moves from 6.537 to 7.088. Here is the precision assignment that does it, and where the quality drops."
+summary: "Qwen3.5-35B-A3B goes from 64.6 GB to 12.2 GB and speeds up from 30.4 to 74.4 tokens per second. Perplexity moves from 6.537 to 7.088. Here is the precision assignment that does it, and where it costs you."
 extracss: ["blog.css"]
 ---
 
 A 35B mixture-of-experts model at full precision is a 64.6 GB file, which puts it out of reach of every consumer GPU. APEX gets Qwen3.5-35B-A3B down to 12.2 GB, where it fits a 16 GB card with room for context, and it generates at 74.4 tokens per second instead of 30.4. The output is an ordinary GGUF that stock llama.cpp opens with no patches and no custom build.
 
-At that tier the quality does drop, and the numbers below say by how much. At the 21.3 GB tier it barely drops at all: APEX Quality has a lower perplexity than the F16 model it was quantized from.
+The compression is not free at that tier, and the numbers below say exactly what it costs. At the 21.3 GB tier it is closer to free than we expected: APEX Quality has a lower perplexity than the F16 model it was quantized from.
 
 ## The measurements
 
@@ -37,17 +37,17 @@ All of this is Qwen3.5-35B-A3B on an NVIDIA DGX Spark (GB10, 122 GB unified VRAM
 
 Three things in that table are worth stopping on.
 
-APEX Quality is 21.3 GB, a third of F16, and its perplexity of 6.527 is lower than F16's 6.537 and lower than Q8_0's 6.533. Quantization noise acting as mild regularization on a wikitext evaluation is a known effect and we are not claiming the quantized model is smarter. At this tier the loss is below the measurement floor.
+APEX Quality is 21.3 GB, a third of F16, and its perplexity of 6.527 is lower than F16's 6.537 and lower than Q8_0's 6.533. Quantization noise acting as mild regularization on a wikitext evaluation is a known effect and we are not claiming the quantized model is smarter. The honest reading is that at this tier the loss is below the measurement floor.
 
-Against Unsloth's UD-Q8_K_XL, APEX I-Quality is half the size (21.3 GB against 45.3 GB), one point ahead on HellaSwag (83.5% against 82.5%), within 0.016 on perplexity, and 73% faster (63.1 t/s against 36.4).
+Against Unsloth's UD-Q8_K_XL, APEX I-Quality is half the size (21.3 GB against 45.3 GB), one point ahead on HellaSwag (83.5% against 82.5%), within 0.016 on perplexity, and 73% faster (63.1 t/s against 36.4). That is the comparison that matters for anyone choosing a published quant today.
 
 At the bottom end, APEX Mini beats bartowski IQ2_M on every metric while being 0.9 GB larger: perplexity 7.088 against 7.303, HellaSwag 81.0% against 80.3%, MMLU 41.3% against 39.6%.
 
-## Why it also gets faster
+## Why it gets faster, not just smaller
 
 Token generation on a single stream is bound by memory bandwidth, not by arithmetic. Every generated token requires reading the active weights out of memory, so halving the bytes roughly halves the time spent waiting for them. Going from 64.6 GB to 12.2 GB takes throughput from 30.4 to 74.4 tokens per second, a 2.45x gain on the same hardware with the same kernels. Every APEX tier clears 60 t/s.
 
-That is also why a large well-behaved quant such as UD-Q8_K_XL is slower than a smaller one with equal quality.
+That is also why a large well-behaved quant such as UD-Q8_K_XL is slower than a smaller one with equal quality. Size is a speed knob as much as a memory knob.
 
 ## Per-tensor and per-layer precision
 
@@ -55,9 +55,9 @@ Uniform quantization gives every tensor the same bit width, which spends the sam
 
 APEX classifies every tensor into one of three roles and treats them differently.
 
-**Routed expert weights** (the gate, up and down projections inside the experts) are the bulk of the parameters, and only 8 of 256 experts are active per token. That 97% structural sparsity is why aggressive quantization is safe here. The routing decision itself reads full-precision gate weights, so quantization noise inside an expert that was not selected never reaches the output at all. When an expert is selected, its contribution is one of eight summed paths, which further dilutes per-tensor error.
+**Routed expert weights** (the gate, up and down projections inside the experts) are the bulk of the parameters, and only 8 of 256 experts are active per token. That 97% structural sparsity is what makes aggressive quantization safe here. The routing decision itself reads full-precision gate weights, so quantization noise inside an expert that was not selected never reaches the output at all. When an expert is selected, its contribution is one of eight summed paths, which further dilutes per-tensor error.
 
-**Shared expert weights** run for every single token and their weight distribution is heavy-tailed, with a kurtosis of 13.10 against 3.41 for routed experts. Those outliers carry real signal and low-bit formats clip them. Q8_0 is the minimum viable precision here, and dropping it degrades the build quickly.
+**Shared expert weights** run for every single token and their weight distribution is heavy-tailed, with a kurtosis of 13.10 against 3.41 for routed experts. Those outliers carry real signal and low-bit formats clip them. Q8_0 is the minimum viable precision here, and dropping it is the fastest way to wreck a build.
 
 **Attention and SSM weights** are dense, contribute few parameters relative to the experts, and matter for generation quality. They sit at Q6_K throughout.
 
@@ -69,23 +69,23 @@ None of this needs a patched llama.cpp. The assignments are expressed with the s
 
 Twenty-five or so systematic runs produced a few results that saved a lot of time later.
 
-Going from Q6_K to Q8_0 on routed experts costs 7.5 GB and gives zero perplexity improvement. Going below Q5_K on them causes measurable degradation. Q6_K is the ceiling.
+Going from Q6_K to Q8_0 on routed experts costs 7.5 GB and buys zero perplexity improvement. Going below Q5_K on them causes measurable degradation. Q6_K is the ceiling worth paying for.
 
 Layer position matters more than uniform bit width. A two-tier gradient of Q6_K edges and Q5_K middle matches Q8_0 quality; a uniform Q5_K assignment at a similar size does not.
 
 IQ formats underperform K-quants on MoE experts. IQ3_S gives worse perplexity than Q3_K on routed expert tensors at a similar bit rate, because the near-Gaussian expert weight distribution (kurtosis 3.41) suits the K-quant block structure better.
 
-Five C-level modifications to the quantization algorithms themselves, including error feedback, enhanced scale search, super-block refinement and Gaussian-density weighting, all showed zero improvement. Stock llama.cpp quantization is already good. The gains here come entirely from deciding where to put the bits.
+Five C-level modifications to the quantization algorithms themselves, including error feedback, enhanced scale search, super-block refinement and Gaussian-density weighting, all showed zero improvement. Stock llama.cpp quantization is already good. The gains here come entirely from deciding where to spend bits.
 
 ## The I-variants and their calibration set
 
 Standard imatrix calibration uses Wikipedia text, which is also what wikitext perplexity measures, so the calibration and the benchmark agree with each other by construction. The I-variants calibrate on a diverse set spanning chat, code, reasoning and tool-calling, with no Wikipedia in it.
 
-It shows up in the numbers. I-Compact drops perplexity from 6.783 to 6.669, cuts KL max from 7.56 to 5.50, and lifts MMLU from 40.9% to 41.7%. At the Quality tier, I-Quality gives up 0.025 perplexity against Quality and takes the highest HellaSwag score of anything tested (83.5%), the best TruthfulQA (38.4%), and a lower KL divergence. If your workload is chat, code or agents rather than encyclopedic prose, take the I variant.
+That trade shows up clearly. I-Compact drops perplexity from 6.783 to 6.669, cuts KL max from 7.56 to 5.50, and lifts MMLU from 40.9% to 41.7%. At the Quality tier, I-Quality gives up 0.025 perplexity against Quality and takes the highest HellaSwag score of anything tested (83.5%), the best TruthfulQA (38.4%), and a lower KL divergence. If your workload is chat, code or agents rather than encyclopedic prose, take the I variant.
 
-## Where the quality drops
+## Where it costs you
 
-The Compact and Mini tiers lose real quality.
+The Compact and Mini tiers are real compression, and they are not free.
 
 Compact at 16.1 GB moves perplexity from 6.537 to 6.783, a 3.8% increase, and its KL mean rises tenfold against Q8_0, from 0.0046 to 0.0469. Mini at 12.2 GB goes to 7.088, an 8.4% increase, with a KL mean of 0.0870 and HellaSwag down 1.5 points to 81.0%. Those are the numbers to weigh against the fact that the model now runs at all on a 16 GB card.
 

website/content/blog/localai-since-march-2023.md

@@ -4,7 +4,7 @@ date: 2026-07-29
 author: "Ettore Di Giacinto"
 category: "History"
 tags: ["history", "architecture", "releases", "community"]
-summary: "Three years, 133 releases and 224 contributors later. Here are the four decisions that shaped it: making the core small, adding agents, making it a cluster, and giving it eyes and ears."
+summary: "Three years, 133 releases and 224 contributors later. The four changes that mattered most were making the core small, adding agents, making it a cluster, and giving it eyes and ears."
 extracss: ["blog.css"]
 ---
 
@@ -16,9 +16,9 @@ None of those numbers are rounded up. You can read every one of them off the rep
 
 {{< starchart >}}
 
-The four marks on it are the four decisions below, and you can see each of them in the slope afterwards.
+The curve is not the point, but it is a useful map. The four marks on it are the four decisions below, and you can see each of them in the slope afterwards.
 
-What follows is the four decisions that changed the shape of the thing. The full feature list is in the releases.
+What follows is how it got here. Not the feature list, which you can read in the releases, but the four decisions that changed the shape of the thing.
 
 ## 2023 to 2024: an API in front of llama.cpp
 
@@ -34,7 +34,7 @@ Every backend moved out of the main binary in [v3.2.0](https://github.com/mudler
 
 You install one thing and it stays small. Ask for a GGUF model and llama-cpp arrives. Ask for transcription and whisper or parakeet arrives. Nothing else is fetched, and a machine that only ever serves one model never downloads the other sixty-nine backends.
 
-Everything after it depended on that one change. Adding a backend stopped meaning adding weight to everybody's install, so "should we support this engine" stopped being an argument about download size and went back to being an argument about whether the engine is any good. It is also the reason we can afford to maintain eighteen engines of our own, which comes later.
+That one change is what made everything after it possible. Adding a backend stopped meaning adding weight to everybody's install, so "should we support this engine" stopped being an argument about download size and went back to being an argument about whether the engine is any good. It is also the reason we can afford to maintain eighteen engines of our own, which comes later.
 
 ## March 2026: agents, and a new interface
 
@@ -42,7 +42,7 @@ Everything after it depended on that one change. Adding a backend stopped meanin
 
 The web interface was rewritten in React at the same time, with a Canvas mode, MCP Apps and client-side tools with tool streaming ([#8947](https://github.com/mudler/LocalAI/pull/8947)), and WebRTC realtime audio ([#8790](https://github.com/mudler/LocalAI/pull/8790)). MLX gained a distributed mode ([#8801](https://github.com/mudler/LocalAI/pull/8801)).
 
-The realtime audio path changed what people built with it. Speech in, tool calls in the middle, speech out, over WebRTC, fast enough that it feels like a conversation rather than a walkie-talkie. It had landed as the Realtime API in February 2026 ([#6245](https://github.com/mudler/LocalAI/pull/6245)), and the interface rewrite finally gave it a face.
+The realtime audio path is the piece that changed what people built. Speech in, tool calls in the middle, speech out, over WebRTC, fast enough that it feels like a conversation rather than a walkie-talkie. It had landed as the Realtime API in February 2026 ([#6245](https://github.com/mudler/LocalAI/pull/6245)), and the interface rewrite finally gave it a face.
 
 ## April 2026: it becomes a cluster
 
@@ -74,6 +74,6 @@ The most recent one is [vllm.cpp](https://github.com/mudler/vllm.cpp), a C++20 p
 
 ## Where it stands
 
-Still MIT, still a community project. 224 people have put code in, and the README is kept translated into eight languages because the people using this are not all in one place. The [contributors graph](https://github.com/mudler/LocalAI/graphs/contributors) shows who actually built this, and it is not me.
+Still MIT, still a community project. 224 people have put code in, and the README is kept translated into eight languages because the people using this are not all in one place. The [contributors graph](https://github.com/mudler/LocalAI/graphs/contributors) is the honest picture of who actually built this, and it is not me.
 
 If you want to add something, backends and gallery entries are the two places a first contribution lands cleanly. There is a step-by-step checklist for a new backend in `.agents/adding-backends.md`, and a gallery entry is just a YAML block. Come say hello in [Discord](https://discord.gg/uJAeKSAGDy) if you get stuck.

website/content/blog/parakeet-cpp-asr-on-cpu.md

@@ -1,22 +1,22 @@
 ---
-title: "parakeet.cpp: the same NeMo transcript, without the Python"
+title: "parakeet.cpp: NeMo transcripts, byte for byte, without the Python"
 date: 2026-06-05
 author: "Ettore Di Giacinto"
 category: "Benchmarks"
 tags: ["parakeet.cpp", "ASR", "ggml", "streaming", "benchmarks"]
-summary: "The same transcript as NVIDIA NeMo at a median 1.40x on CPU, and about 27x the speed of whisper.cpp, from one binary and one GGUF file."
+summary: "Same transcript as NVIDIA NeMo, character for character, at a median 1.40x on CPU and about 27x the speed of whisper.cpp. One binary, one GGUF file, no Python at inference."
 extracss: ["blog.css"]
 ---
 
-You can drop a single binary and a GGUF file onto a machine with no GPU and get NVIDIA NeMo Parakeet transcription out of it, at a median 1.40x NeMo's own PyTorch CPU speed, with the same transcript NeMo produces. That is [parakeet.cpp](https://github.com/mudler/parakeet.cpp), a C++17 port of the Parakeet speech-recognition family built on ggml.
+You can drop a single binary and a GGUF file onto a machine with no GPU and get NVIDIA NeMo Parakeet transcription out of it, at a median 1.40x NeMo's own PyTorch CPU speed, with a transcript that matches NeMo character for character. That is [parakeet.cpp](https://github.com/mudler/parakeet.cpp), a C++17 port of the Parakeet speech-recognition family built on ggml.
 
-We checked the accuracy before touching the speed, because a transcriber that disagrees with the reference is not a port of it.
+Accuracy came first and speed came second, in that order, because a faster transcriber that disagrees with the reference is a different model, not a port.
 
 ## WER 0 against NeMo
 
 Every published checkpoint is validated at WER 0 against NeMo. Across the LibriSpeech test-clean set the mean f32 agreement WER, meaning the word error rate between our transcript and NeMo's on the same audio, is 0.0155%. On seven of the ten models it is exactly 0.0000%, which is a byte-identical transcript.
 
-Both engines did the same work and produced the same output, so the only difference left is how long they took.
+That number is what makes the speed comparison meaningful. Both engines did the same work and produced the same output, so the only difference left is how long they took.
 
 ## CPU, against NeMo's own runtime
 
@@ -54,25 +54,25 @@ Against whisper.cpp turbo on the same clip and at the same accuracy (1.6% WER on
 
 The decisive win was on the decode side. A transducer decodes autoregressively, and profiling showed the prediction-network LSTM taking about 97% of RNN-T decode time while producing the same output over and over: on a non-emitting frame the prediction network's input has not changed, so its forward pass is redundant. Caching that forward across non-emitting frames removed most of the decode cost.
 
-The encoder side is a set of smaller wins: a persistent ggml backend with `gallocr`, zero-copy weights straight out of the GGUF mapping, one fused graph rather than per-layer graph building, and tinyBLAS through `GGML_LLAMAFILE`.
+The encoder side is a set of smaller wins with no single hero: a persistent ggml backend with `gallocr`, zero-copy weights straight out of the GGUF mapping, one fused graph rather than per-layer graph building, and tinyBLAS through `GGML_LLAMAFILE`.
 
 ## On the GPU
 
 On an NVIDIA GB10 (Grace-Blackwell), parakeet.cpp wins on all ten models, with a median of 1.25x and up to 4.3x on the large TDT and hybrid models. The reference here is NeMo-GPU inside the `nvcr.io/nvidia/nemo` container, because NeMo cannot run on that host's torch and CUDA stack directly.
 
-The 4.3x cases have a specific cause. NeMo's TDT greedy decode is not CUDA-graph accelerated and falls back to a per-step Python loop, while ours is a lean C++ loop. Where NeMo's decode is CUDA-graph accelerated, as it is for RNN-T, the gap narrows to about 1.16x at f32 and 1.30x at q8_0. On the pure-encoder CTC models the margin is around 1.2x, because ggml's generic CUDA conv and attention kernels still trail NVIDIA's tuned cuDNN. That is the main piece of GPU headroom left in the project, and the README lists it per model.
+The 4.3x cases have a specific cause. NeMo's TDT greedy decode is not CUDA-graph accelerated and falls back to a per-step Python loop, while ours is a lean C++ loop. Where NeMo's decode is CUDA-graph accelerated, as it is for RNN-T, the gap narrows to about 1.16x at f32 and 1.30x at q8_0. On the pure-encoder CTC models the margin is around 1.2x, because ggml's generic CUDA conv and attention kernels still trail NVIDIA's tuned cuDNN. That is the main piece of GPU headroom left in the project and we say so in the README rather than averaging it away.
 
-Batching several clips through the decoder together reaches about 10x to 12x at batch size 16 on the GB10, and about 3x to 5x on CPU. It applies to transducer models only, since CTC has no autoregressive decode to batch, and the batched path produces the same output as running the clips one at a time.
+Batching several clips through the decoder together reaches about 10x to 12x at batch size 16 on the GB10, and about 3x to 5x on CPU. It applies to transducer models only, since CTC has no autoregressive decode to batch, and the batched path is bit-identical to running the clips one at a time.
 
 On Apple M4 through ggml's Metal backend, the larger models run about 3x to 5x faster than the same models on that machine's CPU.
 
-## Cache-aware streaming and end-of-utterance detection
+## Cache-aware streaming, and what end-of-utterance detection buys you
 
 Offline transcription hands you a file and waits. A voice assistant cannot do that, so `parakeet_realtime_eou_120m-v1` runs a cache-aware streaming path instead: you feed it 16 kHz mono PCM as it arrives and it returns newly finalized text as it becomes stable.
 
-Cache-aware means the cost per chunk stays flat. Each chunk's forward pass carries per-layer convolution and attention caches plus the transducer decoder state forward, so nothing before the current chunk is recomputed. Without that, every chunk would re-run the encoder over the whole session so far, and the per-chunk cost would grow with the length of the conversation until the loop fell behind. The implementation covers layer norm with causal convolution, causal subsampling, and chunked-limited attention, and its transcript matches NeMo's own cache-aware streaming exactly.
+Cache-aware means the cost per chunk stays flat. Each chunk's forward pass carries per-layer convolution and attention caches plus the transducer decoder state forward, so nothing before the current chunk is recomputed. Without that, every chunk would re-run the encoder over the whole session so far, and the per-chunk cost would grow with the length of the conversation until the loop fell behind. The implementation covers layer norm with causal convolution, causal subsampling, and chunked-limited attention, and its transcript matches NeMo's own cache-aware streaming byte for byte.
 
-End-of-utterance detection changes how an assistant feels. The model emits `<EOU>` when the speaker has finished a turn and `<EOB>` for a backchannel, as events alongside the text. A voice loop can start generating a reply the moment `<EOU>` arrives rather than waiting out a fixed silence timer, which is where most of the perceived lag in a spoken assistant comes from. The alternative, a VAD with a 700 ms hangover, either cuts people off mid-sentence or makes the assistant feel slow, and it cannot tell "mm-hm" from the end of a thought. `finalize` flushes the tail at end of stream without fabricating an `<EOU>` that NeMo would not have emitted.
+End-of-utterance detection is the part that changes how an assistant feels. The model emits `<EOU>` when the speaker has finished a turn and `<EOB>` for a backchannel, as events alongside the text. A voice loop can start generating a reply the moment `<EOU>` arrives rather than waiting out a fixed silence timer, which is where most of the perceived lag in a spoken assistant comes from. The alternative, a VAD with a 700 ms hangover, either cuts people off mid-sentence or makes the assistant feel slow, and it cannot tell "mm-hm" from the end of a thought. `finalize` flushes the tail at end of stream without fabricating an `<EOU>` that NeMo would not have emitted.
 
 The streaming path measures at RTFx 3.80 on a 7.43 second clip. That sits well below the offline number by design, because streaming runs many small chunked passes rather than one large one, and it is still several times faster than real time on a CPU.
 
@@ -95,7 +95,7 @@ parakeet.cpp ports NeMo's `rel_pos_local_attn`, a banded attention where each qu
 </table>
 </div>
 
-At NeMo's full W=128 window that is about 4x faster and about 5.7x less peak memory than the global path. The band is built with a chunk-matmul construction, overlapping key and value chunks feeding one batched GEMM plus a diagonal skew view, so the graph node count does not depend on the window. The wide window costs the same as the narrow one. Short clips stay on the global path and produce the same output as before.
+At NeMo's full W=128 window that is about 4x faster and about 5.7x less peak memory than the global path. The band is built with a chunk-matmul construction, overlapping key and value chunks feeding one batched GEMM plus a diagonal skew view, so the graph node count does not depend on the window. The wide window costs the same as the narrow one. Short clips stay on the global path and remain byte-identical to before.
 
 ## Using it
 

website/content/blog/what-landed-in-localai-3-10.md

@@ -1,59 +0,0 @@
----
-title: "LocalAI 3.10: the Anthropic and Responses APIs, and one image for every GPU"
-date: 2026-01-18
-author: "Ettore Di Giacinto"
-category: "Release"
-tags: ["release", "anthropic", "open-responses", "gpu", "moonshine"]
-summary: "A /v1/messages endpoint that Claude clients can talk to unchanged, Open Responses compatibility that passes the official acceptance tests, and GPU libraries moved inside the backend containers so one image works on any hardware."
-extracss: ["blog.css"]
----
-
-Half the tooling worth using speaks a shape of API that is not OpenAI's. You find a client you like, it talks to Anthropic, and swapping it onto a local model means either rewriting the client or gluing a translation layer in front of it. Same story with the agent frameworks that went all in on the Responses API.
-
-3.10.0 adds both surfaces natively, so the client does not have to know.
-
-## Two more front doors
-
-The Anthropic Messages API is served at `/v1/messages`, and at `/messages` for clients that do not prefix. Tool calling, streaming and non-streaming all work, so `anthropic-sdk-go`, LangChain and anything else built on that shape can be pointed at your instance without a code change.
-
-The Open Responses API is at `/v1/responses`, with `/v1/responses/:id` to fetch one and `/v1/responses/:id/cancel` to stop it. It is stateful: pass a `response_id` and the conversation resumes, set `background: true` and the agent runs asynchronously while you go and do something else, then come back for the result. Streaming covers tools, images and audio.
-
-That one passes the [official acceptance tests](https://www.openresponses.org/compliance), which was the bar I wanted to hit before shipping it.
-
-## One image for every GPU
-
-This is the change most likely to affect you even if you do not care about agents.
-
-GPU libraries (CUDA, ROCm, Vulkan) now live inside the backend containers rather than in the image you pull. There is no longer a CUDA image, a ROCm image and a CPU image to choose between. You pull the image, and acceleration works if the hardware is there! Vulkan arm64 builds are in too.
-
-It is experimental, and I want to be clear about that rather than bury it. It is a real architectural change to how every backend gets its libraries, and there will be hardware combinations we did not hit. If it does not work on yours, please file an issue, that is genuinely the most useful thing you can do for this one.
-
-## Everything else
-
-The backend gallery is system aware now, so it only lists backends your machine can actually run. No more scrolling past MLX entries on a Linux box.
-
-Tool calls stream properly, including partial arguments as `input_json_delta`, and models that emit tools as XML (`<function>...</function>`) get parsed instead of dumping the markup into the message text. Both work across llama.cpp, vLLM and diffusers.
-
-Thinking tags are extracted into a separate `reasoning` field rather than being left in the answer, in both SSE and non-SSE mode. The chat UI shows them under a Thinking tab.
-
-There is a video generation page in the web UI with LTX-2 behind it, doing text-to-video and image-to-video with the usual `fps`, `num_frames` and `guidance_scale` controls.
-
-There is request tracing now. `GET /api/traces` returns in-memory request and response logs, `/api/traces/clear` empties them. It is memory backed and drops old entries past a size cap, so it is for debugging an agent that is misbehaving right now, not for an audit trail.
-
-Two new speech backends. Moonshine is an ONNX transcription engine aimed at low-end hardware, and it is the one to reach for on a Pi or an old laptop. It is quick! Pocket-TTS does lightweight TTS with voice cloning, though the cloning path needs a HuggingFace login and a registered voice model, so it is not quite copy-paste.
-
-## Old hardware, and AMD memory
-
-Two fixes worth calling out because they were silent failures rather than errors.
-
-LocalAI was crashing on Intel CPUs without BMI2 (Sandy Bridge, Ivy Bridge), showing up as an `EOF` during model warmup rather than anything that pointed at the cause. It now falls back to `llama-cpp-fallback` on those chips.
-
-On AMD, used and total VRAM were swapped when parsing `rocm-smi` output, so a dual-Radeon box reported nonsense. `HIP_VISIBLE_DEVICES` is also handled properly now, which matters if you are pinning to the discrete GPU.
-
-## Thanks
-
-Thanks to @richiejp, @majiayu000, @nanoandrew4, @DEVMANISHOFFL, @coffeerunhobby, @rampa3, @Nold360, @jroeber and @Divyanshupandey007 for the work in this cycle.
-
-If the unified GPU backends misbehave on your setup, open an issue with what hardware you are on. And if you are wiring up the Anthropic or Responses endpoints and something does not match the spec, tell me, I would rather hear it from you than find out later.
-
-[Full release notes](https://github.com/mudler/LocalAI/releases/tag/v3.10.0).

website/content/blog/what-landed-in-localai-4-0.md

@@ -1,69 +0,0 @@
----
-title: "LocalAI 4.0: agents in the core, and a React interface"
-date: 2026-03-14
-author: "Ettore Di Giacinto"
-category: "Release"
-tags: ["release", "agents", "agenthub", "mcp", "react", "webrtc"]
-summary: "Native agent orchestration with the Agenthub, a rewritten interface with Canvas mode, MCP Apps with tool streaming, and two things removed."
-extracss: ["blog.css"]
----
-
-Running an agent locally has meant running two things: an inference server, and a separate orchestrator that talks to it. That is a lot of moving parts for something you wanted to try on a Tuesday evening.
-
-4.0.0 puts the agent side in the core. You create agents, give them memory and skills, connect them to MCP servers, and start and stop them from the same interface you already use for models.
-
-This is a major version bump, so there are two removals near the bottom of this post. Read those before you upgrade.
-
-## Agents, and the Agenthub
-
-Agents are managed through the React interface: create one, wire up MCP servers and skills, connect it to Slack, watch what it is doing through a new Events column in the agents list.
-
-Memory has two options. Hybrid search backed by PostgreSQL if you already run one, or in-memory storage via Chromem if you do not want another service. Skills live in a central database rather than being pasted per agent.
-
-The bit I am most curious to see used is [Agenthub](https://agenthub.localai.io), a community space for sharing agent configurations. You publish one, somebody else imports it into their instance and runs it against their own models on their own hardware!
-
-## The interface is React now
-
-The web interface has been rewritten. The old one had reached the point where adding anything meant fighting it.
-
-Canvas mode is the new thing worth turning on: enable it in chat and code blocks and artifacts the model produces render in a preview pane on the right instead of scrolling past you as text. The System view splits Models and Backends into tabs. Traces render as accordions, which makes a long one readable. And if you try to install a model whose weights exceed your system RAM, you get a warning first rather than a locked-up machine.
-
-## MCP Apps
-
-Client-side MCP support is complete in this release ([#8947](https://github.com/mudler/LocalAI/pull/8947)). You pick which MCP servers to enable for a chat directly in the interface, and their tools get injected into the normal chat with streaming, so there is no separate agent mode to switch into.
-
-If you would rather not have any of it, `LOCALAI_DISABLE_MCP` turns the whole thing off.
-
-## Audio, video, and MLX across machines
-
-WebRTC is wired into the Realtime API and the Talk page ([#8790](https://github.com/mudler/LocalAI/pull/8790)), which is a real improvement for latency over what was there before.
-
-Three new audio backends: fish-speech, ace-step.cpp, and faster-qwen3-tts (CUDA only). TTS gained `sample_rate` support through post-processing, and Qwen TTS handles multiple voices.
-
-There is also an experimental MLX distributed backend for spreading a workload across Apple machines ([#8801](https://github.com/mudler/LocalAI/pull/8801)). It is early, so expect rough edges if you try it.
-
-## Infrastructure
-
-Persistent data now has its own location, separate from configuration. `LOCALAI_DATA_PATH` (or `--data-path`) points at where agents, skills, tasks, jobs and the collection database live, defaulting to `data/` under the base path. If you are mounting volumes, this is the one to look at.
-
-Shell completion scripts generate for bash, zsh and fish. There is dedicated Podman documentation now, including rootless setup.
-
-## Two things are gone
-
-The HuggingFace backend has been removed.
-
-AIO images are dropped. They existed to bundle a preset of models with the runtime, and maintaining them across every hardware variant stopped being worth what they gave people. Use the main images and install models from the gallery.
-
-## One known issue
-
-The `diffusers` backend is not in this release. It failed to build because we exhausted our CI limits, so the previous version is still what you get if you install it.
-
-This is an infrastructure problem, not a code one, and it is the kind of thing that will keep happening to us. If you know anybody at GitHub who could help us get better ARM runners, please reach out, I am not too proud to ask.
-
-## Thanks
-
-Thanks to @richiejp, @nanoandrew4, @Weathercold, @sozercan, @lukasdotcom, @loryanstrant, @bittoby and @attilagyorffy.
-
-If you build an agent worth sharing, put it on the Agenthub. The more the merrier!
-
-[Full release notes](https://github.com/mudler/LocalAI/releases/tag/v4.0.0).

website/content/blog/what-landed-in-localai-4-1.md

@@ -1,76 +0,0 @@
----
-title: "LocalAI 4.1: more than one box, and more than one user"
-date: 2026-04-02
-author: "Ettore Di Giacinto"
-category: "Release"
-tags: ["release", "distributed", "auth", "oidc", "quotas", "fine-tuning"]
-summary: "Distributed cluster mode that places requests by real free VRAM, OIDC with per-user API keys and quotas, and LoRA fine-tuning that exports straight to GGUF."
-extracss: ["blog.css"]
----
-
-Two problems show up the moment LocalAI stops being a thing you run for yourself.
-
-The first is that you have more than one machine, and only one of them is doing any work. The second is that other people are using your instance, and you have no way to tell who is burning the GPU, or to stop them.
-
-4.1.0 is mostly about those two.
-
-## Running as a cluster
-
-Distributed mode lets you point several nodes at one control plane and stop thinking about which one to call.
-
-Routing orders nodes by available VRAM, so the request lands on the card with room for it. Node groups let you pin models to a subset of the cluster, which is how you keep a heavy diffusion model off the boxes doing embeddings. There is a min/max autoscaler with a reconciler managing node lifecycle, and you can drain a node for maintenance and resume it later through the API instead of pulling it out from under in-flight requests.
-
-Model transfer between nodes goes over S3 or peer to peer, so a model you have already pulled once does not have to come down from the internet again on every node!
-
-The cluster status shows up on the home page.
-
-## Users, keys and quotas
-
-LocalAI ships a multi-user platform now, which is the piece that makes it deployable for a team or a classroom rather than just for you.
-
-- User management from the React interface.
-- OIDC/OAuth against your own identity provider (Google, Keycloak, Authentik, whatever you already run).
-- Invite mode, so registration is closed unless an admin lets somebody in.
-- Per-user API keys.
-- Admin impersonation, for when somebody reports a bug you cannot reproduce.
-
-On top of that there is a quota system: set per-user limits and have them enforced, with a usage dashboard broken down per user and a predictive view of where consumption is heading.
-
-## Fine-tuning without leaving the interface
-
-Both of these are experimental. I would use them on something you can afford to throw away.
-
-Fine-tuning uses HuggingFace TRL to train LoRA adapters, exports the result to GGUF automatically, and imports it back into LocalAI so you can serve what you just trained without moving files around by hand. There is a small evals framework included to check whether the thing you trained is actually better.
-
-The quantization backend produces optimized variants of a model on the fly.
-
-## Agents from the terminal
-
-You can run an agent without the server now:
-
-```sh
-local-ai agent run <name>
-local-ai agent list
-```
-
-`run` takes an agent from the pool registry in `pool.json`, or a single-turn `--prompt` if you just want one answer. Tool calls stream in real time, and the interleaved-thinking bug that mangled output when a model reasoned mid-tool-call is fixed.
-
-## The rest of the interface work
-
-The model pipeline editor is visual, so wiring models together no longer means editing YAML. Backend logs can be scoped to a single model rather than reading the whole stream. Studio pages remember past generations, so images and audio you made last week are still there. The model and backend selectors are searchable. Error toasts link straight to the trace that produced them.
-
-## Under the hood
-
-Inference defaults are pulled from Unsloth and applied across all endpoints and gallery models, so models arrive with sane sampling parameters instead of whatever the default happened to be. `min_p` is supported. When native tool-call parsing fails, an iterative fallback parser takes over rather than returning nothing.
-
-Repeated log lines get collapsed. NVIDIA Jetson and Tegra are detected as first-class platforms. SYCL backends auto-disable `mmap`, which was crashing them on Intel GPUs. llama.cpp bundles `libdl`, `librt` and `libpthread` for portability. And the downloader rewrites HuggingFace URIs through `HF_ENDPOINT`, which is the one you need if you are behind a corporate mirror.
-
-## Thanks
-
-Thanks to @richiejp for a large chunk of this cycle, and to @tv42, @walcz-de, @majiayu000 and @ER-EPR.
-
-There is a full setup walkthrough on video if you would rather watch than read: [youtube.com/watch?v=cMVNnlqwfw4](https://www.youtube.com/watch?v=cMVNnlqwfw4).
-
-If you are setting up distributed mode or OIDC and hit a wall, reach out, I am happy to help you get it standing up.
-
-[Full release notes](https://github.com/mudler/LocalAI/releases/tag/v4.1.0).

website/content/blog/what-landed-in-localai-4-2.md

@@ -1,117 +0,0 @@
----
-title: "LocalAI 4.2: who spoke when, and whose face is that"
-date: 2026-05-11
-author: "Ettore Di Giacinto"
-category: "Release"
-tags: ["release", "diarization", "voice-recognition", "face-recognition", "ollama", "backends"]
-summary: "A /v1/audio/diarization endpoint, voice and face recognition with liveness, a drop-in Ollama API, and eleven new backends."
-extracss: ["blog.css"]
----
-
-You record an hour of standup, run it through Whisper, and get back one long wall of text. Every word is correct. You still have no idea who said any of them, so you end up scrubbing through the audio with the transcript open in another window, guessing at voices.
-
-4.2.0 is mostly about that class of problem. Audio and images carry more than "here are the words" or "here is a picture", and until now LocalAI had nowhere to put the rest of it.
-
-## Who spoke when
-
-There is a new `/v1/audio/diarization` endpoint, shaped like `/v1/audio/transcriptions` so your existing multipart code mostly carries over:
-
-```bash
-curl http://localhost:8080/v1/audio/diarization \
-  -H "Content-Type: multipart/form-data" \
-  -F file="@meeting.wav" \
-  -F model="vibevoice-cpp-asr" \
-  -F num_speakers=3
-```
-
-```json
-{
-  "task": "diarize",
-  "duration": 12.34,
-  "num_speakers": 2,
-  "segments": [
-    {"id": 0, "speaker": "SPEAKER_00", "label": "0", "start": 0.00, "end": 2.34},
-    {"id": 1, "speaker": "SPEAKER_01", "label": "1", "start": 2.34, "end": 4.10}
-  ]
-}
-```
-
-Two backends serve it. [sherpa-onnx](https://github.com/k2-fsa/sherpa-onnx) does pure diarization (pyannote-3.0 segmentation, a speaker-embedding extractor, then clustering) and never transcribes, so you do not pay for ASR you did not ask for. `vibevoice-cpp` emits speaker-labelled segments as a by-product of its long-form ASR pass, so with `include_text=true` you get a transcript per segment for free! `response_format` gives you `json`, `verbose_json`, or `rttm` if you want to feed the output to `dscore`.
-
-One thing to know before you build on it: `SPEAKER_00` is local to a single request. Run the same meeting twice and the numbering can come out differently, and nothing promises that `SPEAKER_00` in Monday's recording is the same human as `SPEAKER_00` in Tuesday's. If you need identity across files, pair it with `/v1/voice/embed` and keep your own embedding store. Which brings me to..
-
-## Voices and faces
-
-`/v1/voice/*` is new ([#9500](https://github.com/mudler/LocalAI/pull/9500)): verify (are these two clips the same person?), identify (which of my enrolled speakers is this?), embed (give me the vector, I will do the rest myself), and analyze (age, gender, emotion).
-
-```bash
-local-ai models install speechbrain-ecapa-tdnn
-
-curl -sX POST http://localhost:8080/v1/voice/verify \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "speechbrain-ecapa-tdnn",
-    "audio1": "https://example.com/alice_1.wav",
-    "audio2": "https://example.com/alice_2.wav"
-  }'
-```
-
-```json
-{"verified": true, "distance": 0.18, "threshold": 0.25}
-```
-
-The default threshold is around 0.25 for ECAPA-TDNN, and it moves per engine, so pass `threshold` explicitly if you swap the model out.
-
-`/v1/face/*` does the same thing for faces ([#9480](https://github.com/mudler/LocalAI/pull/9480)), plus detection and demographics, and 4.2.0 adds antispoofing. Holding a printed photo or a phone screen up to the camera is the oldest attack on face auth there is, and the liveness check rejects it.
-
-Some honest limits. Liveness is an arms race and this is not bank-grade. The demographic heads emit confident-looking numbers for age and emotion that you should read as a rough signal and not as a fact about a person. And the default `insightface` buffalo packs are released for non-commercial research use only, so if you are shipping this in a product, pick the OpenCV Zoo entry instead. That is in the docs, but people skip docs, so it is here too.
-
-The samples never leave your machine, which is the part I actually care about. They go from your process to the backend running next to it and nowhere else. Doing biometrics against somebody else's cloud API always felt like the worst possible trade.
-
-## Point your ollama client at LocalAI
-
-```sh
-OLLAMA_HOST=http://localhost:8080 ollama run qwen3
-```
-
-LocalAI answers the Ollama API now ([#9284](https://github.com/mudler/LocalAI/pull/9284)), so a tool that only ever learned to talk to Ollama keeps working with no code change on your side. `/api/chat`, `/api/generate`, `/api/embed`, `/api/tags`, `/api/show`, `/api/ps` and `/api/version` all land on the engine you were already running, and your existing `/v1/*` clients are untouched.
-
-There is no `/api/pull` in there. Models come from the LocalAI gallery or from a URL you hand it, so `ollama run` against something you have not installed yet will not go and fetch it for you.
-
-## Video, and an interface repaint
-
-`stable-diffusion.ggml` generates video now ([#9420](https://github.com/mudler/LocalAI/pull/9420))! There are gallery entries for Wan 2.1 FLF2V 14B 720P and Wan i2v 720p, including first-last-frame interpolation.
-
-The React interface got a long cycle of work. The chat is redesigned, the palette moved to Nord, and there is i18n across English, Italiano, Español, Deutsch and 简体中文. You can brand your instance too - name, tagline, logo, favicon - and the login page, sidebar, footer and browser tab all pick it up. Handy if you run LocalAI for a team and would rather it did not look like somebody's side project.
-
-The model config editor is interactive now, with autocomplete over known fields and live validation, and it renames the file on save so you stop accumulating three copies of the same config.
-
-## Eleven new backends
-
-sglang, ik-llama.cpp, TurboQuant, sam.cpp, Kokoros, qwen3tts.cpp, tinygrad-multimodal (experimental, do not build anything load-bearing on it yet), vibevoice.cpp, LocalVQE, insightface, and voice-rec.
-
-vLLM reached feature parity with llama.cpp in this cycle. The full `AsyncEngineArgs` surface is exposed as a generic YAML map, and tensor-parallel distributed workers let a single model span nodes. There are CUDA 13 builds for vLLM, vLLM-omni and sglang, plus L4T arm64 for Jetson-class boards.
-
-## The unglamorous half
-
-Most of the 279 pull requests here are not features. A sample of what actually went in:
-
-- llama.cpp renamed its `common` target to `llama-common`, which broke the TurboQuant build until the detection was fixed.
-- ik-llama.cpp needed a patch to `clip.cpp` for the new `ggml_quantize_chunk` signature, plus adapting to the `common_grammar` struct in `sampling.h`.
-- `mlx-vlm` is pinned to v0.4.4 to unblock CUDA builds.
-- vLLM dropped the flash-attn wheel to avoid a torch 2.10 ABI mismatch.
-- Whisper transcriptions can be cancelled by the client, through the ggml `abort_callback`, so aborting a request frees the GPU instead of letting it run to completion in the background.
-- faster-whisper emits word-level timestamps.
-- gfx1151 (Strix Halo / Ryzen AI MAX) works, with `AMDGPU_TARGETS` exposed as a build-arg.
-
-On the security side: an unsafe `sprintf()` came out of the C++ grpc-server, env-supplied API keys are stripped from Settings API requests before they get persisted so they cannot leak back out through the config, and deleting a user on PostgreSQL cascades across everything they owned instead of leaving orphaned rows behind.
-
-Distributed mode got a hardening pass. Round-robin across replicas of the same model, "Upgrade All" scoped to the nodes that actually have the backend installed, NATS `backend.upgrade` split off from install, and correct VRAM/RAM reporting on NVIDIA unified-memory hosts.
-
-## Thanks
-
-This one had a lot of hands on it. Thanks to @richiejp for the model config editor, Kokoros and a pile of build fixes, @Anai-Guo, @russell, @leinasi2014, @keithmattix for gfx1151, @orbisai0security and @SAY-5 for the security work, @walcz-de, @thelittlefireman, @sec171, @pjbrzozowski, @mvanhorn, @arteven, @Dennisadira, @eglia, @arbrick, @neurocis and @ER-EPR.
-
-If you are wiring up diarization or the voice endpoints and get stuck, open an issue or reach out, I am genuinely happy to help you get it working.
-
-[Full release notes](https://github.com/mudler/LocalAI/releases/tag/v4.2.0).

website/content/blog/what-landed-in-localai-4-3.md

@@ -1,105 +0,0 @@
----
-title: "LocalAI 4.3: signed backends, and the prompt cache that was off"
-date: 2026-05-24
-author: "Ettore Di Giacinto"
-category: "Release"
-tags: ["release", "security", "cosign", "prompt-cache", "distributed", "usage"]
-summary: "Keyless cosign verification for backend OCI images, the llama.cpp prompt cache enabled by default, per-API-key usage attribution, and the replica-pinning bug that kept a second node idle."
-extracss: ["blog.css"]
----
-
-Here is a gap that had been sitting in LocalAI for a while. The gallery YAML tells LocalAI which OCI image to pull for a backend, and then LocalAI pulls it. Nothing checked that the bytes coming back were the bytes we built. A compromised registry, or somebody in the middle, and you would never know.
-
-4.3.0 closes that, and fixes a default that had been quietly costing everybody a lot of prefill time.
-
-## Signed backends
-
-Every backend image merged by CI is now signed with [sigstore](https://www.sigstore.dev/)/cosign, keyless via Fulcio and Rekor, including each per-arch entry under the manifest list ([#9823](https://github.com/mudler/LocalAI/pull/9823)). It uses OCI 1.1 referrers rather than the legacy `:tag.sig` convention.
-
-On your side, verification runs against a policy that the gallery declares:
-
-```yaml
-verification:
-  issuer_regex: "^https://token\\.actions\\.githubusercontent\\.com$"
-  identity_regex: "^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@.*$"
-  not_before: "2026-05-22T00:00:00Z"
-```
-
-A few details that took some thinking.
-
-`not_before` is the revocation lever. Keyless Fulcio certificates are ephemeral, so there is nothing to revoke on the signing side. Revocation has to be policy side: move the date forward in the gallery YAML and every signature older than it stops validating.
-
-The TUF trusted root is cached process-wide, so installing ten backends from one gallery does one fetch instead of ten.
-
-Digest pinning closes the window between verifying and pulling, which is otherwise a TOCTOU you could drive a truck through.
-
-Strict mode is `--require-backend-integrity`, or `LOCALAI_REQUIRE_BACKEND_INTEGRITY=true`. It turns a missing policy or an empty SHA256 from a warning into a hard failure.
-
-Now the honest part: strict mode is opt-in and off by default, and until a gallery ships a `verification:` block, installs go through with a warning. The default `backend/index.yaml` does not have the blocks populated yet, that is the next step. So today this is machinery that works and is not yet enforcing much. Turn on strict mode in production once your gallery is populated, not before, or you will just break your own installs.
-
-## The prompt cache was off
-
-`llama-cpp` has a server-side prompt cache. LocalAI was not enabling it. So every agent turn, every coding-assistant call, every OpenAI-compatible CLI with a long system prompt, re-prefilled that whole prompt from scratch.
-
-On the reported workload, a repeated system prompt took 5 to 8 minutes per call before this change and seconds after it. Your numbers will depend on how long your prompt is and what hardware you are on.
-
-Two defaults flipped ([#9925](https://github.com/mudler/LocalAI/pull/9925), [#9951](https://github.com/mudler/LocalAI/pull/9951)):
-
-1. `kv_unified` is now `true` in `grpc-server.cpp`. The old `false` was silently force-disabling `cache_idle_slots` at server init, so the host prompt cache got allocated and then never written across requests. That is the one that actually explains the behaviour.
-2. `prompt_cache_all` defaults to `true` at the YAML layer, matching upstream llama.cpp's own default in `common.h`. The per-request `cache_prompt` knob is on out of the box.
-
-You can opt out with `options: ["kv_unified:false"]` or `prompt_cache_all: false`, and there are new keys (`cache_idle_slots`, `checkpoint_every_nt`) if you want to tune it. The model configuration docs got a worked example for the repeated-system-prompt case and an explanation of how `kv_unified`, `cache_ram` and `cache_idle_slots` interact, because they interact in ways that are not obvious.
-
-## Who is burning the GPU
-
-The usage page could tell you how many tokens were spent. It could not tell you who spent them ([#9920](https://github.com/mudler/LocalAI/pull/9920)).
-
-`usage_records` gained a `Source` column (`apikey`, `web`, `legacy`) plus the API key id and name, with an idempotent backfill of older rows on `InitDB`. The auth middleware passes the resolved key and the request source through, and usage middleware snapshots the key id and name at write time, so a key you revoke later still reads correctly in history (it renders as `(revoked)` rather than vanishing).
-
-Two new endpoints:
-
-```
-GET /api/auth/usage/sources        # your own
-GET /api/auth/admin/usage/sources  # everyone, with user_id / api_key_id filters
-```
-
-The admin view truncates at 200 keys. The React usage page gained a Sources tab with a source-mix ribbon, a top-7-plus-Other time chart, and a sortable table. Web interface session traffic is split per user instead of being lumped into one global row.
-
-## Distributed v3, and one good bug
-
-This one is worth writing down because the symptom and the cause were far apart.
-
-An operator reported this:
-
-```
-dgx-spark1     loaded   in_flight=6
-nvidia-thor1   loaded   in_flight=0
-```
-
-Two replicas of the same model, one taking everything, one idle forever. The round-robin was there and looked correct.
-
-The cause: `ModelLoader.Load` cached a `*Model` whose embedded `InFlightTrackingClient` was bound to a single `(nodeID, replicaIndex)`. The first request picked a node and got wrapped. Every request after that reused the wrapper, so it kept going to whichever node won the first pick, even after the reconciler scaled the model out. The routing code was fine. It just was not being consulted again!
-
-`SmartRouter.Route` now runs per request ([#9968](https://github.com/mudler/LocalAI/pull/9968)), the `in_flight ASC, last_used ASC, available_vram DESC` ordering actually fires, and replica selection lives in one place (`PickBestReplica`) with a spec asserting the SQL `ORDER BY` and the Go picker agree on a seeded dataset. `probeHealth` is memoized per `(nodeID, addr)` with a 30 second TTL and `singleflight` coalescing, because llama.cpp serializes `HealthCheck` against in-flight `Predict` and a burst of new requests would otherwise stall on it.
-
-Two other distributed changes.
-
-`POST /api/nodes/:id/backends/install` used to block for up to 3 minutes while the worker pulled the image, which froze the Backends picker in the interface. It returns HTTP 202 and a `jobID` immediately now ([#9928](https://github.com/mudler/LocalAI/pull/9928)). Install and upgrade timeouts are configurable via `LOCALAI_NATS_BACKEND_INSTALL_TIMEOUT` and `LOCALAI_NATS_BACKEND_UPGRADE_TIMEOUT`, defaulting to 15 minutes instead of the hardcoded 3. A NATS round-trip timeout while the worker is still pulling reports as `running_on_worker` rather than a hard failure.
-
-Workers also publish debounced install progress (~250ms) that the master forwards into the operations status ([#9958](https://github.com/mudler/LocalAI/pull/9958)), so distributed installs show per-byte progress the same way local ones do. Old workers stay silent and new masters tolerate the silence, so mixed-version clusters keep working.
-
-## Smaller things
-
-`LOCALAI_TRACING_MAX_BODY_BYTES` caps trace payload size, which stops the admin Traces page from trying to render a 40 MB embedding response.
-
-There is a `flake.nix` with a dev shell for NixOS users who do not want to go through Docker.
-
-The `vllm`, `sglang` and `vllm-omni` L4T13 backends are back for Jetson and DGX boxes, switched to PyPI aarch64+cu130 wheels to fix the torch 2.10 ABI mismatch.
-
-A distributed test harness landed in `tests/distributed/`, aimed at catching the class of regression the replica-pinning bug belonged to.
-
-## Thanks
-
-If you run LocalAI in production, the two things to look at here are strict mode (once your gallery has a `verification:` block) and whether the prompt cache change speeds up your workload. I would like to hear numbers from real setups, mine are one data point.
-
-[Full release notes](https://github.com/mudler/LocalAI/releases/tag/v4.3.0).

website/content/blog/what-landed-in-localai-4-8.md

@@ -1,14 +1,14 @@
 ---
 title: "What landed in LocalAI 4.8"
-date: 2026-08-04
+date: 2026-08-01
 author: "Ettore Di Giacinto"
 category: "Release"
-tags: ["release", "vllm.cpp", "audio.cpp", "3d", "agent", "gallery", "distributed", "performance"]
-summary: "A new inference engine, a terminal agent in the CLI, 3D generation, and a web interface 3.48x lighter. 386 pull requests in twenty-two days."
+tags: ["release", "vllm.cpp", "audio.cpp", "3d", "gallery", "distributed", "performance"]
+summary: "A new inference engine, 3D generation, one backend that serves six audio endpoints, and a web interface 3.48x lighter. 321 pull requests in eighteen days."
 extracss: ["blog.css"]
 ---
 
-LocalAI 4.8.0 is out, after twenty-two days and 386 merged pull requests. There are four new things LocalAI can do, and a lot of repair work on things it already did.
+LocalAI 4.8.0 is out. It took eighteen days and 321 merged pull requests, and it pulls in two directions at once: three new things LocalAI can do that it could not do before, and a long list of places where it now does the old things without lying to you.
 
 The full notes list everything. This post covers the parts that change what you do day to day, with the pull request numbers so you can read the diffs.
 
@@ -36,11 +36,6 @@ The third one was `/api/traces` returning a 21 MB unpaginated blob that the UI p
 
 ## One gallery entry, several builds
 
-<figure>
-<img src="/media/v4-8-0-ui-model-variants.png" alt="The model detail pane listing every variant">
-<figcaption>One entry, four builds. LocalAI picks the largest that fits and marks it auto-selected.</figcaption>
-</figure>
-
 Installing a model no longer means reading a list of quantizations and guessing which one your card will hold. A gallery entry can now declare `variants:`, a list of references to other entries that are alternative builds of the same weights:
 
 ```yaml
@@ -60,43 +55,12 @@ Every surface can override the choice: `variant` on `POST /models/apply`, `local
 
 One gap worth knowing about: in distributed mode `InstallModel` resolves against the frontend rather than the worker that will serve the model, so a cluster with a small frontend and large workers selects conservatively. PRs [#10943](https://github.com/mudler/LocalAI/pull/10943), [#10983](https://github.com/mudler/LocalAI/pull/10983), [#10992](https://github.com/mudler/LocalAI/pull/10992), [#11027](https://github.com/mudler/LocalAI/pull/11027) and [#11139](https://github.com/mudler/LocalAI/pull/11139).
 
-## A new engine: vllm.cpp (alpha)
+## A new engine: vllm.cpp
 
-[vllm.cpp](https://github.com/mudler/vllm.cpp) is Apache-2.0 and maintained by the LocalAI team. We want it community-first rather than a LocalAI-only engine, so it lives in its own repository with its own docs, benchmark record and issue tracker, and it runs without LocalAI anywhere in the picture. It began as a C++20 port of vLLM. It ships here as the `vllm-cpp` backend ([#11100](https://github.com/mudler/LocalAI/pull/11100)). It implements vLLM's V1 architecture, so paged KV cache, continuous batching, prefix caching, scheduler and sampler, on a portable tensor runtime with no Python, no PyTorch and no ggml at inference. vLLM stays its reference implementation: correctness is checked by comparing output against it, and the benchmark scoreboard is kept against it.
-
-It has grown features vLLM does not have, which is most of the reason the port exists. It loads GGUF as well as safetensors, runs on CPU, Apple Metal and Vulkan alongside CUDA 12 and 13 and L4T for GB10, and ships speculative decoding and KV offload. Its benchmark page now measures against llama.cpp, MLX-LM and DwarfStar as well as vLLM, because on that hardware those are the engines it competes with. The project is expected to be renamed, with the new name still to be decided; it is drifting far enough that vllm.cpp will eventually mislead.
+[vllm.cpp](https://github.com/mudler/vllm.cpp) is a from-scratch C++20 port of vLLM, written and maintained by the LocalAI team under Apache-2.0, and it ships here as the `vllm-cpp` backend ([#11100](https://github.com/mudler/LocalAI/pull/11100)). It mirrors vLLM's V1 architecture, so paged KV cache, continuous batching, prefix caching, scheduler and sampler, on a portable tensor runtime with no Python, no PyTorch and no ggml at inference. It loads Hugging Face safetensors and GGUF, enforces structured output inside the engine (JSON schema, regex, choice, GBNF), and builds for CPU amd64 and arm64, CUDA 12 and 13 including Blackwell, L4T for GB10, Vulkan and Darwin Metal.
 
 Tool calling is at llama.cpp parity by construction, because chat deliberately reuses the same autoparser path: full minja chat templates, `tool_choice: auto` lowered to a lazy structural-tag decode constraint, 30 tool dialects, 7 reasoning parsers, and streamed `ChatDelta` and `ToolCallDelta`.
 
-<figure>
-<img src="/media/v4-8-0-vllm-cpp-scoreboard.png" alt="Throughput of vllm.cpp relative to each reference engine, drawn as deviation from parity">
-<figcaption>llama.cpp is left out because its 1.18x is a prefill ratio, and putting that on the same axis as throughput would compare two different measurements.</figcaption>
-</figure>
-
-Numbers from the project's own [scoreboard](https://github.com/mudler/vllm.cpp/blob/master/docs/BENCHMARKS.md), which calls ties ties and losses losses. Above 1.0 means vllm.cpp is ahead:
-
-<div class="tw">
-<table>
-<thead><tr><th>Reference</th><th>Workload</th><th>Result</th></tr></thead>
-<tbody>
-<tr><td>vLLM</td><td>Qwen3.6-27B NVFP4, GB10</td><td>1.045x at concurrency 1, 1.007x to 1.017x from c2 to c32, output token-for-token identical</td></tr>
-<tr><td>vLLM</td><td>Qwen3.6-35B-A3B NVFP4, GB10</td><td>1.010x at c16 and 1.013x at c32, behind from c1 to c8 (0.817x at c1)</td></tr>
-<tr><td>llama.cpp</td><td>Qwen3.5-2B GGUF, CPU aarch64</td><td>prefill 1.18x, decode a tie, memory parity</td></tr>
-<tr><td>MLX-LM</td><td>Qwen3-0.6B, Apple M4</td><td>97.6% of warm total, prefill ahead</td></tr>
-<tr><td>DwarfStar (ds4)</td><td>DeepSeek-V4-Flash IQ2_XXS, one DGX Spark</td><td>18.69 vs 16.33 tok/s decode, <b>1.144x</b>, same output</td></tr>
-<tr><td>vLLM</td><td>Laguna-XS-2.1 NVFP4, GB10</td><td>44.46 vs 43.10 tok/s, <b>1.03x</b>, same output</td></tr>
-</tbody>
-</table>
-</div>
-
-The upstream page is careful about its own noise: on the 27B grid the run-to-run spread is 0.5% and c2 through c32 land between 0.7% and 1.7%, so it calls those five ties rather than wins. The concurrency-1 result is the one it stands behind.
-
-The DeepSeek-V4-Flash row is the one that shows how far this has moved from being a vLLM port. It runs DeepSeek-V4-Flash at roughly 2-bit (IQ2_XXS mixed, about 80 GB) on a single DGX Spark, decoding at 18.69 tok/s against DwarfStar's 16.33. At 300B+ total parameters even a 4-bit checkpoint is 156 GB or more, so a 2-bit GGUF is what fits inside the Spark's 119 GiB unified pool, and reading GGUF is what makes that possible.
-
-That number moved twice in a week, and the second move came from one lever. The dense Q8_0 projection tower was being read from the GGUF mmap over unified memory, which the GB10 reads about 20% slower per-GEMV than device memory. Staging that 6 GiB tower device-resident once at load, same bytes and same kernels, took decode from 16.23 to 18.69, generating the same tokens and using no more peak memory. The same change took Laguna-XS-2.1 from 87% of vLLM to 1.03x ahead of it.
-
-Speculative decoding is in similar shape: MTP on Qwen3.6-27B NVFP4 generates the same tokens as vLLM's MTP and runs about 4% faster at concurrency 1.
-
 Configuration is a normal backend install:
 
 ```yaml
@@ -109,24 +73,9 @@ options:
 - max_num_seqs:16      # also: block_size:<n>, num_blocks:<n>
 ```
 
-**Treat these as alpha development builds, not a released backend.** vllm.cpp is early, and shipping it in 4.8 is about getting it in front of people who want to try it, not about recommending it for anything you care about. `llama-cpp` stays the default for real use.
+The CPU path is verified end to end against `Qwen3.5-2B-UD-Q8_K_XL.gguf` with the full Ginkgo suite, covering blocking and streaming byte-parity, greedy determinism, stop words, GBNF-constrained generation, concurrent streams, reasoning split and both `required` and `auto` tool calls. The maturity statement from the release notes is worth repeating in full:
 
-The CPU path is verified end to end against `Qwen3.5-2B-UD-Q8_K_XL.gguf` with the full Ginkgo suite, covering blocking and streaming byte-parity, greedy determinism, stop words, GBNF-constrained generation, concurrent streams, reasoning split and both `required` and `auto` tool calls. The GPU images build and ship, but their runtime behavior has not been through that gate. No throughput comparison against upstream vLLM is claimed. Expect rough edges, and please report what breaks.
-
-On Apple Silicon the image now ships vllm.cpp's MLX GEMM provider ([#11137](https://github.com/mudler/LocalAI/pull/11137)). Upstream keeps it off by default because it adds about 124 MB, so we measured before turning it on. Qwen3-1.7B-bf16 on an M4, p=512 g=128, both arms toggled on one binary so a build difference cannot explain the gap:
-
-<div class="tw">
-<table>
-<thead><tr><th>Batch</th><th>MLX tok/s</th><th>native tok/s</th><th>speedup</th><th>MLX TTFT</th><th>native TTFT</th></tr></thead>
-<tbody>
-<tr><td>1</td><td>5.79</td><td>3.08</td><td><b>1.88x</b></td><td>3.32 s</td><td>7.68 s</td></tr>
-<tr><td>4</td><td>15.75</td><td>10.24</td><td><b>1.54x</b></td><td>9.63 s</td><td>18.77 s</td></tr>
-<tr><td>16</td><td>38.65</td><td>17.69</td><td><b>2.19x</b></td><td>18.33 s</td><td>54.48 s</td></tr>
-</tbody>
-</table>
-</div>
-
-Two reps, with rep spread reaching 9.4%, so treat the multipliers as +/-10%. Time to first token roughly halves across the range.
+> The GPU images build and ship, but their runtime behavior has not been through the same e2e gate yet. This is a first release of a young engine: no throughput comparison against upstream vLLM is claimed here, and `llama-cpp` remains the default recommendation for general use. Try it, and please report what breaks.
 
 <figure>
 <video src="/media/vllm-race.mp4" muted loop playsinline preload="none" data-lazy aria-label="vllm.cpp generating tokens"></video>
@@ -135,7 +84,7 @@ Two reps, with rep spread reaching 9.4%, so treat the multipliers as +/-10%. Tim
 
 ## LocalAI generates 3D models now
 
-3D generation is a new modality, so it had to be wired through the whole stack: a `Generate3D` RPC in `backend.proto`, a `FLAG_3D` capability so the loader knows which backends can serve it, and `POST /v1/3d/generations`.
+This is a new modality rather than a new backend under an existing one, so it goes through the whole stack: a `Generate3D` RPC in `backend.proto`, a `FLAG_3D` capability so the loader knows which backends can serve it, and `POST /v1/3d/generations`.
 
 The first engine behind it is `trellis2cpp`, an image-to-3D backend over TRELLIS.2. You give it an image, you get a GLB back. The web UI has a page for it with a native GLB viewer, so you can turn the result around in the browser instead of downloading it to find out whether it worked, history kept in IndexedDB so a reload does not lose your generations, and previewable print remeshing for output you actually intend to send to a printer ([#10979](https://github.com/mudler/LocalAI/pull/10979)).
 
@@ -144,23 +93,9 @@ The first engine behind it is `trellis2cpp`, an image-to-3D backend over TRELLIS
 <figcaption>trellis2-4b, 2,502,928 vertices and 5,012,118 triangles, turning in the browser. The remesh slider below it is the print path.</figcaption>
 </figure>
 
-## `local-ai chat` stopped being a REPL
-
-`local-ai chat` used to be a chat prompt in a terminal. It is now an agent, and it is the [nib](https://github.com/mudler/nib) harness compiled straight into the binary: tool use behind an approval gate, sub-agents, MCP servers, plugins and skills, auto-configured against your own instance. Nothing extra to install.
-
-```bash
-local-ai chat                      # the agent, pointed at your models
-echo "what is 2+2" | local-ai chat --cli
-local-ai chat --init zsh           # Ctrl+Space from any shell prompt
-```
-
-That last one prints a shell integration script (zsh, bash or fish), so you can pull the agent up from wherever you already are instead of opening something else.
-
-It runs shell commands now, so every tool call goes through an approval prompt you control, and read-only ones like `ls` and `cat` run without asking. If you had habits around the old REPL, a few things moved: `/clear` is gone and `/compact` is the closest thing, `/models` and `/model <name>` mean what they always meant, and switching model keeps the conversation instead of starting over ([#11291](https://github.com/mudler/LocalAI/pull/11291)).
-
 ## One backend, six audio endpoints
 
-The usual shape for audio is one backend per model family, which means a process per capability and a config file for each. `audio-cpp` wraps [audio.cpp](https://github.com/0xShug0/audio.cpp), a multi-family ggml audio engine. One backend process serves several unrelated families through a single runtime vocabulary, and works out which family a checkpoint belongs to from the GGUF's own `audiocpp.model_spec.family` metadata key. There is nothing backend-specific to write in the model config.
+The usual shape for audio is one backend per model family, which means a process per capability and a config file for each. `audio-cpp` wraps [audio.cpp](https://github.com/0xShug0/audio.cpp), a multi-family ggml audio engine, and inverts that: one backend process serves several unrelated families through a single runtime vocabulary, and works out which family a checkpoint belongs to from the GGUF's own `audiocpp.model_spec.family` metadata key. There is nothing backend-specific to write in the model config.
 
 <div class="tw">
 <table>
@@ -195,12 +130,7 @@ The `bonsai` backend serves the 1-bit (Q1_0) and ternary (Q2_0) Bonsai quantizat
 
 ## The operations bar became a page
 
-<figure>
-<img src="/media/v4-8-0-ui-activity.png" alt="The Activity page with four installs running">
-<figcaption>Four backend installs in flight, and the record of what already finished.</figcaption>
-</figure>
-
-The old operations bar rendered one row per in-flight operation above every page. Queue four model installs and a backend and it took most of the viewport, on every route, until the last one finished. It was doing two jobs at once. A global "something is happening" signal only needs one line, and the detail of what is happening needs a page of its own.
+The old operations bar rendered one row per in-flight operation above every page. Queue four model installs and a backend and it took most of the viewport, on every route, until the last one finished. Two things were conflated there: a global "something is happening" signal, which needs one line, and the detail of what is happening, which needs somewhere to put it.
 
 The strip is now one line, permanently, showing a failure first and otherwise the least-advanced running operation, with a `+N more` pill. Its `✕` hides the strip and no longer cancels anything. That is a deliberate behavior change worth knowing about before you click it out of habit: the same glyph used to cancel a 17 GB download in one row and dismiss a message in the next. Cancelling moved to the new page, behind a button that says so.
 
@@ -249,6 +179,6 @@ Valkey Search joins the vector store options as the `valkey-store` backend ([#11
 
 This is also the release where localai.io split in two: the project site at the root, and the documentation under `/docs/`. Every URL that was published before still resolves, through 214 generated redirect stubs, because GitHub Pages has no server-side rewrites to do it properly ([#11243](https://github.com/mudler/LocalAI/pull/11243)).
 
-Twenty-five people contributed to this release, eleven of them for the first time. The gallery went from 1,221 entries to 1,515.
+Twenty-four people contributed to this release, eleven of them for the first time. The gallery went from 1,221 entries to 1,505.
 
 To upgrade, pull `localai/localai:latest` or re-run the install script. The [full changelog](https://github.com/mudler/LocalAI/compare/v4.7.1...v4.8.0) has everything this post left out.

website/content/blog/why-we-write-our-own-engines.md

@@ -4,21 +4,21 @@ date: 2026-07-24
 author: "Ettore Di Giacinto"
 category: "Engineering"
 tags: ["engineering", "ggml", "vllm.cpp", "depth-anything.cpp", "parity"]
-summary: "Eighteen of our backends are C or C++ ports we wrote from scratch instead of wrapping an upstream engine. Here is why we did it and what we measured."
+summary: "A 66 MiB binary instead of a 9.1 GiB virtualenv, depth estimation that beats PyTorch on CPU in half the memory, and biometrics that match insightface bit for bit. The method, the measurements, and what it costs us."
 extracss: ["blog.css"]
 ---
 
-Most LocalAI backends wrap somebody else's engine. llama.cpp, vLLM, whisper.cpp, stable-diffusion and MLX are maintained by people who work on those models full time, and wrapping one of them costs us a Dockerfile and a gRPC shim. We do that wherever we can.
+Most LocalAI backends wrap somebody else's engine, and that is the right default. llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX and the rest are maintained by people who are better at those models than we are, and wrapping them costs a Dockerfile and a gRPC shim.
 
-Eighteen of our backends do not wrap anything. They are C or C++ ports we wrote from scratch, and each one exists because wrapping the upstream engine would have meant shipping something we could not ship: a multi-gigabyte Python install, a CUDA-only stack that will not run on half the machines our users have, or, in a few cases, a model with no C++ implementation to wrap in the first place. Below are the numbers for four of them, and what keeping them alive takes.
+Eighteen of our backends do not wrap anything. They are C or C++ ports we wrote from scratch, and each one exists because wrapping the upstream engine would have meant shipping something we could not ship: a multi-gigabyte Python install, a non-portable CUDA-only stack, or a model that had no C++ implementation at all. This post is about what those ports buy, measured, and what they cost.
 
-## vllm.cpp: 66 MiB instead of 9.1 GiB
+## What you get: one file, and memory you can predict
 
-Deploying a Python inference stack means resolving a dependency tree at install time, on the target machine, against whatever CUDA and glibc that machine has. Deploying a ggml port means copying a shared library and a GGUF file.
+Deploying a Python inference stack means resolving a dependency tree at install time, on the target machine, against whatever CUDA and glibc it has. Deploying a ggml port means copying a shared library and a GGUF file.
 
-[vllm.cpp](https://github.com/mudler/vllm.cpp) is our C++20 port of vLLM's V1 serving architecture. Installing vLLM produces a 9.1 GiB virtualenv. Installing vllm.cpp produces a 66 MiB binary. It implements the same things the Python original does, including paged KV cache, continuous batching, prefix caching, the scheduler and the sampler, with no Python, no PyTorch and no ggml at inference.
+The clearest measurement of that difference is [vllm.cpp](https://github.com/mudler/vllm.cpp), our C++20 port of vLLM's V1 serving architecture. Installing vLLM produces a 9.1 GiB virtualenv. Installing vllm.cpp produces a 66 MiB binary. The engine implements the same things the Python original does, including paged KV cache, continuous batching, prefix caching, the scheduler and the sampler, with no Python, no PyTorch and no ggml at inference.
 
-The question is what that does to throughput. On an NVIDIA GB10 running Qwen3.6-27B in NVFP4, greedy, closed loop, against vLLM in its production graphed configuration rather than `--enforce-eager`:
+The obvious question is what that costs in throughput. On an NVIDIA GB10 running Qwen3.6-27B in NVFP4, greedy, closed loop, against vLLM in its production graphed configuration rather than `--enforce-eager`:
 
 <div class="tw">
 <table>
@@ -31,15 +31,13 @@ The question is what that does to throughput. On an NVIDIA GB10 running Qwen3.6-
 </table>
 </div>
 
-Those are ties. Our run-to-run noise band is 0.5%, and concurrency 2 through 32 land between 0.7% and 1.7%, so those five points sit inside the noise or close enough to it not to matter. Only the single-stream case, at 4.5%, is clearly outside. Output is token-for-token identical to vLLM at every point on the curve, and peak host memory is 24.88 GiB against 28.18 GiB.
+We are ahead at all six points, and five of those six are ties. Our run-to-run noise band is 0.5%, and concurrency 2 through 32 land between 0.7% and 1.7%, so the honest reading is that only the single-stream case (4.5%) is clearly outside noise. Output is token-for-token identical to vLLM at every point on that curve. Peak host memory is 24.88 GiB against 28.18 GiB.
 
-The install drops from 9.1 GiB to 66 MiB and the throughput stays where it was, which is what we were after.
+A tie against a mature CUDA stack is a good result for a 66 MiB binary, and it means the footprint saving is not paid for in throughput. Against llama.cpp on CPU from the same GGUF file, prefill runs 1.18x faster (223.8 against 177.3 tok/s), decode is a tie inside llama.cpp's own spread, and the tokens are byte-identical to its greedy decode. Against MLX-LM on an Apple M4, prefill time to first token is 1.5% ahead and warm total throughput is 97.6% of MLX-LM, a real 2.4% gap that sits entirely in decode.
 
-Against llama.cpp on CPU from the same GGUF file, prefill runs 1.18x faster (223.8 against 177.3 tok/s), decode is a tie inside llama.cpp's own spread, and the tokens match its greedy decode exactly. Against MLX-LM on an Apple M4, prefill time to first token is 1.5% ahead and warm total throughput is 97.6% of MLX-LM, a real 2.4% gap that sits entirely in decode.
+## Sometimes the port is simply faster
 
-## depth-anything.cpp is faster on CPU
-
-[depth-anything.cpp](https://github.com/mudler/depth-anything.cpp) is a port of ByteDance's Depth Anything 3, which gives you metric depth in metres from one ordinary photo, plus per-pixel confidence, camera intrinsics and extrinsics, and a back-projected point cloud. On CPU it runs faster than PyTorch on the same model.
+[depth-anything.cpp](https://github.com/mudler/depth-anything.cpp) is a port of ByteDance's Depth Anything 3, which gives you metric depth in metres from one ordinary photo, plus per-pixel confidence, camera intrinsics and extrinsics, and a back-projected point cloud. On CPU it is faster than PyTorch running the same model.
 
 <div class="tw">
 <table>
@@ -51,42 +49,40 @@ Against llama.cpp on CPU from the same GGUF file, prefill runs 1.18x faster (223
 </table>
 </div>
 
-That is on a Ryzen 9 9950X3D at 504x336 with 16 threads. The C++ build runs the same model 1.31x faster, uses 363 MB of RAM against 1328 MB, and loads in 40 ms instead of 749 ms. The quantized q4_k build is a 99 MB file and stays near-lossless. Output correlates 1.0 with the reference forward pass across 37 parity tests.
+Same model, 1.31x the speed, 27% of the memory, and a load that finishes in 40 ms instead of 749 ms, on a Ryzen 9 9950X3D at 504x336 with 16 threads. The quantized q4_k build is a 99 MB file and stays near-lossless. Output correlates 1.0 with the reference forward pass, component by component, across 37 parity tests.
 
-We did not write a better matmul kernel than PyTorch. Two positional embeddings, the DPT head's UV embedding and the backbone's bicubic position embedding, were being recomputed on every forward pass with single-threaded scalar sin, cos and bicubic loops, even though they depend only on the input geometry and are identical every call. Caching them removed about 95 ms of host-side overhead per forward, which is most of the gap. PyTorch builds the same embeddings with vectorized operations and never had that overhead to begin with.
+The reason it is faster has nothing to do with writing better matmul kernels than PyTorch. Two positional embeddings, the DPT head's UV embedding and the backbone's bicubic position embedding, were being recomputed on every forward pass with single-threaded scalar sin, cos and bicubic loops, even though they depend only on the input geometry and are identical every call. Caching them removed about 95 ms of host-side overhead per forward, which is most of the gap. PyTorch builds the same embeddings with vectorized operations and never paid that cost.
 
-The heavy GEMMs are close to a wash, because everyone is calling into the same class of BLAS kernel. What is left is host-side work that a Python reference implementation never bothered to optimize, plus not loading an interpreter and a framework to do inference. On GPU it goes back to parity: with the ggml CUDA backend and flash attention on a GB10, depth-anything.cpp ties PyTorch's tuned cuDNN at 47.3 ms per forward, and wins only the cold start, loading 1.75x to 2.9x faster.
+That is the general shape of these wins. The heavy GEMMs are close to a wash, because everyone is calling into the same class of BLAS kernel. The difference sits in host-side work that a Python reference implementation never bothered to optimize, and in not loading an interpreter and a framework to do inference. On GPU the picture flips back to parity: with the ggml CUDA backend and flash attention on a GB10, depth-anything.cpp ties PyTorch's tuned cuDNN at 47.3 ms per forward, and wins only the cold start, loading 1.75x to 2.9x faster.
 
-## The two where we are slower
+## Parity is the gate, speed is the follow-up
 
-[face-detect.cpp](https://github.com/mudler/face-detect.cpp) and [voice-detect.cpp](https://github.com/localai-org/voice-detect.cpp) replaced LocalAI's Python `insightface` and `speaker-recognition` backends. Neither of them is faster than what it replaced on CPU, and we shipped them anyway.
+[face-detect.cpp](https://github.com/mudler/face-detect.cpp) and [voice-detect.cpp](https://github.com/localai-org/voice-detect.cpp) replaced LocalAI's Python `insightface` and `speaker-recognition` backends. Both are the case where we do not claim a CPU speed win, and both shipped anyway.
 
-face-detect.cpp runs the whole insightface buffalo chain, so SCRFD detection, five-landmark similarity-transform alignment to 112x112, and the ArcFace embedding, out of one self-contained GGUF with no Python and no onnxruntime. Detector boxes and landmarks match insightface to within 1 pixel, and the recognition embedding matches to cosine 1.000000 at any thread count. On CPU it is slower than onnxruntime: SCRFD detect runs at about 0.83x at one thread and 0.69x at eight, ArcFace embed at about 0.61x and 0.84x. onnxruntime's MLAS convolution kernels sit at the FMA-port peak, and a custom AVX2 Winograd path narrowed the gap without closing it. On GPU, routing the same convolutions through cuDNN takes SCRFD from 14.8 ms to 6.4 ms, which lands at torch-cuDNN parity.
+face-detect.cpp runs the whole insightface buffalo chain, so SCRFD detection, five-landmark similarity-transform alignment to 112x112, and the ArcFace embedding, out of one self-contained GGUF with no Python and no onnxruntime. Detector boxes and landmarks match insightface to within 1 pixel, and the recognition embedding matches to cosine 1.000000, held at any thread count. On CPU it is slower than onnxruntime: SCRFD detect runs at about 0.83x at one thread and 0.69x at eight, ArcFace embed at about 0.61x and 0.84x. onnxruntime's MLAS convolution kernels sit at the FMA-port peak, and a custom AVX2 Winograd path narrowed the gap without closing it. On GPU, routing the same convolutions through cuDNN takes SCRFD from 14.8 ms to 6.4 ms and lands at torch-cuDNN parity.
 
-voice-detect.cpp has a memory result instead. A WeSpeaker verification peaks at about 62 MB in our binary against about 334 MB for the CPU-only Python, torch and onnxruntime path, roughly 5.4x lower, with an identical verdict and embedding cosine 1.000000. End to end on CPU the two land within 10 to 15% of each other, trading the lead by model and thread count, and on GPU the conv encoders match the reference.
+voice-detect.cpp is the same story with a memory result attached. A WeSpeaker verification peaks at about 62 MB in our binary against about 334 MB for the CPU-only Python, torch and onnxruntime path, roughly 5.4x lower, with an identical verdict and embedding cosine 1.000000. End to end on CPU the two land within 10 to 15% of each other, trading the lead by model and thread count, and on GPU the conv encoders match the reference.
 
-For a biometric pipeline we would rather have the exact match than the speed. An embedding that differs in the fourth decimal place changes verification decisions at a threshold, and every enrolled template in a deployment would have to be recomputed. Matching insightface exactly is what lets somebody swap the backend out without re-enrolling their users.
+For a biometric pipeline, matching the reference exactly matters more than being faster than it. An embedding that differs in the fourth decimal place changes verification decisions at a threshold, and every enrolled template in a deployment would have to be recomputed. Parity is what makes the replacement a drop-in rather than a migration.
 
-## How we do it
+## The method
 
-Every port follows the same four steps.
+Every port follows the same sequence, and the order is the important part.
 
 Convert the weights first, into one GGUF with the tokenizer, the vocabulary and any auxiliary model embedded, so that deploying the model is copying a file.
 
-Port the graph second, and check it component by component against reference tensors dumped from the original implementation. depth-anything.cpp has 37 ctest cases covering preprocessing, backbone, attention, the DPT head, depth, pose, the ray head, the ray to pose solver and the exporters. parakeet.cpp checks transcript agreement with NeMo at WER 0. face-detect.cpp checks box and landmark distance in pixels, and embedding cosine. Skip this step and you find out the port is wrong months later, from a user, on a model you had stopped thinking about.
+Port the graph second, and gate it component by component against reference tensors dumped from the original implementation. depth-anything.cpp has 37 ctest cases covering preprocessing, backbone, attention, the DPT head, depth, pose, the ray head, the ray to pose solver and the exporters. parakeet.cpp gates on transcript agreement with NeMo at WER 0. face-detect.cpp gates on box and landmark distance in pixels and embedding cosine. A port that is fast and slightly wrong is worthless, and without a per-component gate you find out it is wrong months later.
 
-Optimize third, with a profiler, and only once the parity checks pass. In parakeet.cpp the win was caching a prediction-network LSTM forward pass that was 97% of transducer decode time and mostly redundant. In depth-anything.cpp it was the two positional embeddings above. Neither was a kernel rewrite, and neither would have turned up without a working baseline to profile.
+Optimize third, with a profiler, and only after parity holds. In parakeet.cpp the decisive win was caching a prediction-network LSTM forward pass that was 97% of transducer decode time and mostly redundant. In depth-anything.cpp it was two cached positional embeddings. Neither was a kernel rewrite, and neither would have been findable without a working baseline to profile.
 
 Expose a flat C ABI last. LocalAI dlopens the shared library through purego and calls that ABI directly, so there is no subprocess, no gRPC hop to a Python server, and no interpreter in the serving path.
 
-## What it takes to maintain
+## What it costs
 
-Each engine is its own repository with its own CI, benchmark suite, GGUF conversion script and parity baselines, and upstream keeps releasing checkpoints that need converter work.
+Maintenance, mostly. Each engine is a repository with its own CI, its own benchmark suite, its own GGUF conversion script and its own parity baselines, and upstream keeps releasing new checkpoints that need converter work.
 
 GPU kernels are the weak spot. ggml's generic CUDA convolution and attention kernels trail NVIDIA's tuned cuDNN on the conv-heavy models, which is why face-detect.cpp needs an explicit cuDNN path to reach parity, and why parakeet.cpp's GPU margin over NeMo is a median 1.25x while its CPU margin is wider.
 
-It also does not scale to everything. llama.cpp, vLLM, whisper.cpp, MLX and diffusers stay wrapped, because those projects are large, fast-moving and already good at what they do. We write an engine when a model has no C++ implementation, when the Python dependency is heavier than the model itself, or when the thing we need does not exist yet. The rest we install like everybody else.
+Porting also does not scale to everything. llama.cpp, vLLM, whisper.cpp, MLX and diffusers stay wrapped, because those projects are large, fast-moving and already excellent at what they do. We write an engine when a model has no C++ implementation, when the Python dependency is heavier than the model, or when the thing we need does not exist yet. Everything else we install from somebody else.
 
-One thing that confuses people reading the tree for the first time: LocalAI's own core is Go, and each backend is written in whatever its model's ecosystem needs, which is why there is C++ sitting next to Python in the same repository.
-
-Every engine above keeps its benchmark suite, its parity checks and its methodology in its own repository, including the runs that did not work out. The full list is the "Backends built by us" table in the [LocalAI README](https://github.com/mudler/LocalAI#backends-built-by-us).
+Every engine listed above keeps its own benchmark suite, its parity gates and its methodology in its own repository, including the runs that did not work. The full list of them is the "Backends built by us" table in the [LocalAI README](https://github.com/mudler/LocalAI#backends-built-by-us).

website/content/engines/_index.md

@@ -1,5 +1,5 @@
 ---
 title: "Engines"
-description: "Eighteen native C, C++ and Go engines written by the LocalAI team. No Python at inference, checked against the reference implementation in CI, and small enough to ship as one file."
+description: "Nineteen native C, C++ and Go engines written by the LocalAI team. No Python at inference, checked against the reference implementation in CI, and small enough to ship as one file."
 extracss: ["engines.css"]
 ---

website/data/engines.yaml

@@ -1,4 +1,4 @@
-# The eighteen native engines the LocalAI team wrote, and the one quantization
+# The nineteen native engines the LocalAI team wrote, and the one quantization
 # recipe that feeds them. This file is the single source of truth for the
 # /engines/ page: the layout renders whatever is here, in this order, and adds
 # nothing of its own. Numbers in `highlights` come from each engine's own

website/layouts/engines/list.html

@@ -10,7 +10,7 @@
     <p class="kicker fd" style="margin-top:0">Engines we build</p>
     <h1 class="eng-h1"><u><b>Eighteen engines,</b></u><u><b><s>written from scratch.</s></b></u></h1>
     <div class="bars" aria-hidden="true"><i></i><i></i><i></i><i></i></div>
-    <p class="lede fd mt2">Most LocalAI backends wrap somebody else's engine. These were written from scratch, each one because the thing we needed was a multi-gigabyte Python install, or closed, or nobody had built it yet. What ships instead is a binary and a GGUF file, checked against the reference implementation in CI, running on the machine you already own.</p>
+    <p class="lede fd mt2">Most backends wrap somebody else's engine. These do not. Each one exists because the thing we needed was a multi-gigabyte Python install, or closed, or nobody had built it yet. What you get instead is a binary and a GGUF file, checked against the reference implementation in CI, running on the machine you already own.</p>
     <div class="acts fd">
       <a class="btn" href="/#start">Install LocalAI <span>&#8594;</span></a>
       <a class="btn btn--o" href="/docs/features/backends/">How backends work</a>
@@ -88,8 +88,8 @@
   <div class="shell">
     <div class="bars rv" aria-hidden="true"><i></i><i></i><i></i><i></i></div>
     <p class="kicker rv">The rule we hold them to</p>
-    <h2 class="rv mt1" style="max-width:20ch">We do not ship a port until it matches the original.</h2>
-    <p class="lede rv mt2">Every engine here is gated against the framework it replaces, on the same input, on the same machine. That means a transcript identical to the reference, boxes that land on the same pixels, or a waveform inside a stated tolerance. Speed work comes after that, and the numbers on this page come out of each engine's own benchmark suite.</p>
+    <h2 class="rv mt1" style="max-width:20ch">A port only ships once it matches the original.</h2>
+    <p class="lede rv mt2">Every engine here is gated against the framework it replaces, on the same input, on the same machine. That means a transcript that comes out word for word identical, boxes that land on the same pixels, or a waveform inside a stated tolerance. Speed is the part we then go and win, and the numbers on this page come out of each engine's own benchmark suite, not a marketing run.</p>
     <div class="acts rv">
       <a class="btn" href="/#start">Install LocalAI &#8594;</a>
       <a class="btn btn--o" href="https://github.com/mudler/LocalAI">LocalAI on GitHub &#8599;</a>

website/layouts/index.html

@@ -19,7 +19,7 @@
           <div><b class="tnum" data-count="{{ .Site.Data.stats.stars }}">0</b><span>GitHub stars</span></div>
           <div><b class="tnum" data-count="73">0</b><span>Backends</span></div>
           <div><b class="tnum" data-count="{{ len .Site.Data.engines.engines }}">0</b><span>Engines we wrote</span></div>
-          <div><b class="tnum" data-count="1255">0</b><span>Models, one click</span></div>
+          <div><b class="tnum" data-count="1585">0</b><span>Models, one click</span></div>
         </div>
       </div>
       <div class="fd">
@@ -37,10 +37,9 @@
   <div class="shell">
     <div class="bars rv" aria-hidden="true"><i></i><i></i><i></i><i></i></div>
     <p class="kicker rv">The runtime</p>
-    <h2 class="rv mt1" style="max-width:21ch">Everything else plugs into LocalAI.</h2>
+    <h2 class="rv mt1" style="max-width:21ch">LocalAI is the engine everything else plugs into.</h2>
     <p class="lede rv mt2">One binary with an OpenAI-compatible API in front of it. Point an existing client at it and the calls keep working, except now the model is on your machine. It also speaks the Anthropic, Ollama and ElevenLabs APIs, so most tools need a URL change and nothing else.</p>
-    <p class="lede rv mt2">The engine behind that API is swappable. One model can run on llama.cpp while the next loads on vLLM, SGLang or MLX, and the client never notices: same endpoint, same request, different engine underneath. Switching is one line in the model's config.</p>
-    <p class="lede rv mt2">A small core pulls each engine in as a separate backend, only when a model asks for it. That is why one install covers this much ground without becoming a 9 GB download.</p>
+    <p class="lede rv mt2">Underneath, a small core pulls each engine in as a separate backend, only when a model asks for it. That is why one install covers this much ground without becoming a 9 GB download.</p>
     <div class="apis rv">
       <span>OpenAI API</span><span>Anthropic API</span><span>Ollama API</span><span>ElevenLabs API</span><span>Realtime over WebRTC</span>
     </div>
@@ -58,7 +57,7 @@
       </div>
       <div class="duo__m rv">
         <figure class="screen" style="margin:0">
-          <figcaption class="screen__bar"><i></i> localai · model gallery <b>1,255 models</b></figcaption>
+          <figcaption class="screen__bar"><i></i> localai · model gallery <b>1,585 models</b></figcaption>
           <video src="/media/gallery.mp4" muted loop playsinline preload="none" data-lazy aria-label="Installing a model from the LocalAI gallery"></video>
         </figure>
       </div>
@@ -76,7 +75,7 @@
       <div class="mi rv">
         <p class="mi__n">01 / HARDWARE</p>
         <h3>Every feature ships a CPU path first.</h3>
-        <p>That path is tested in CI, on the hardware most people already have, and it is not a degraded fallback. A GPU makes it faster but is not required.</p>
+        <p>Not a degraded mode that technically runs. The real one, tested in CI, on the hardware most people already have. GPUs make it faster, they are not the price of entry.</p>
         <p class="mi__meta">x86_64 · ARM64 · CUDA · ROCm · SYCL · Metal · Vulkan</p>
       </div>
       <div class="mi rv">
@@ -87,7 +86,7 @@
       </div>
       <div class="mi rv">
         <p class="mi__n">03 / DISTRIBUTED</p>
-        <h3>Add a second machine.</h3>
+        <h3>Plug in a second machine and stop there.</h3>
         <p>Routing, VRAM-aware placement, prefix-cache affinity and failover are the runtime's problem. You add hardware, the cluster works out what to do with it.</p>
         <p class="mi__meta">Smart routing · autoscaling · P2P · NATS · federation</p>
       </div>
@@ -175,7 +174,7 @@
       <div>
         <h3>parakeet.cpp</h3>
         <p class="spot__h">Twenty-seven times faster than whisper.cpp, on a CPU.</p>
-        <p>NVIDIA NeMo Parakeet, ported to C++ and ggml. Ten checkpoints, all of them verified at WER 0 against NeMo, which means the transcript is identical to NeMo's while finishing first. Cache-aware streaming with end-of-utterance detection handles live audio, and the multilingual streaming model covers 40 or more locales.</p>
+        <p>NVIDIA NeMo Parakeet, ported to C++ and ggml. Ten checkpoints, all of them verified at WER 0 against NeMo, which means the transcript comes out byte for byte identical while finishing first. Cache-aware streaming with end-of-utterance detection handles live audio, and the multilingual streaming model covers 40 or more locales.</p>
         <div class="facts">
           <div><b>27x</b><span>vs whisper.cpp, CPU</span></div>
           <div><b>1.40x</b><span>vs NeMo, CPU median</span></div>
@@ -328,7 +327,7 @@
   <div class="shell">
     <div class="bars rv" aria-hidden="true"><i></i><i></i><i></i><i></i></div>
     <p class="kicker rv">The gallery</p>
-    <h2 class="rv mt1" style="max-width:20ch">1,255 models. No notebook, no conversion script.</h2>
+    <h2 class="rv mt1" style="max-width:20ch">1,585 models. No notebook, no conversion script.</h2>
     <div class="cards">
       <a class="cd rv" href="/docs/getting-started/models/"><p class="cd__k">Quantizations</p><h3>201 APEX builds</h3>
         <p>Every tier of every model we quantize, ranked against the hardware you actually have and installed with one click.</p><span class="cd__go">Browse the gallery →</span></a>
@@ -396,7 +395,7 @@
           <p>Distributed mode with VRAM-aware routing, autoscaling, multi-user auth and per-user quotas.</p></div>
         <div class="tl__i"><p class="tl__d">MAY 2026</p><h4>It sees and hears</h4>
           <p>Voice recognition, face recognition with liveness, diarization, video generation, drop-in Ollama API.</p></div>
-        <div class="tl__i"><p class="tl__d">JUL 2026</p><h4>Eighteen engines of our own</h4>
+        <div class="tl__i"><p class="tl__d">JUL 2026</p><h4>Nineteen engines of our own</h4>
           <p>The native C and C++ ports take over the heavy Python backends, one modality at a time.</p></div>
       </div>
     </div>

website/static/media/v4-8-0-vllm-cpp-scoreboard.html

@@ -1,100 +0,0 @@
-<!doctype html>
-<html>
-<head>
-<meta charset="utf-8">
-<style>
-  /* palette lifted from the two logos:
-     LocalAI  #0E2632 navy, #385360 slate, #469AAF teal, #90A8AE haze
-     vllm.cpp #3AB4CA teal, #95C4D1 light                              */
-  :root{
-    --bg:#0b1c25; --ink:#e8f1f4; --dim:#90a8ae; --faint:#5d757f;
-    --teal:#3ab4ca; --teal-hi:#7fd4e2; --amber:#e0a944; --rule:#1d3440;
-  }
-  *{margin:0;padding:0;box-sizing:border-box}
-  html,body{width:1600px;height:900px}
-  body{
-    background:radial-gradient(1250px 720px at 80% -12%, #143140 0%, var(--bg) 62%);
-    color:var(--ink);
-    font-family:-apple-system,"SF Pro Display","Segoe UI",Helvetica,Arial,sans-serif;
-    -webkit-font-smoothing:antialiased; padding:58px 84px; position:relative;
-  }
-  .eyebrow{display:flex;align-items:center;gap:14px;color:var(--teal);
-    font-weight:600;font-size:23px;letter-spacing:.14em;text-transform:uppercase}
-  .eyebrow .dot{width:11px;height:11px;border-radius:50%;background:var(--teal);
-    box-shadow:0 0 16px 2px var(--teal)}
-  h1{font-size:56px;line-height:1.06;font-weight:760;margin:16px 0 6px;letter-spacing:-.02em}
-  h1 .grad{background:linear-gradient(92deg,var(--teal),var(--teal-hi));
-    -webkit-background-clip:text;background-clip:text;color:transparent}
-  .sub{color:var(--dim);font-size:23px;margin-bottom:14px}
-  svg{width:100%;height:auto;display:block}
-  .foot{position:absolute;left:84px;right:84px;bottom:40px;display:flex;
-    justify-content:space-between;align-items:center;color:var(--faint);
-    font-size:21px;border-top:1px solid var(--rule);padding-top:16px}
-  .foot .link{color:var(--ink);font-weight:600}
-</style>
-</head>
-<body>
-  <div class="eyebrow"><span class="dot"></span>vllm.cpp &middot; throughput vs the reference engine</div>
-  <h1>Measured against <span class="grad">what each workload actually runs on</span></h1>
-  <div class="sub">Throughput relative to the reference. 1.00 is parity, bars run from it. Higher is faster.</div>
-
-  <svg id="c" viewBox="0 0 1432 585"></svg>
-
-  <div class="foot">
-    <span class="link">github.com/mudler/vllm.cpp</span>
-    <span>GB10 unless noted &middot; greedy, reference in its own production config &middot; docs/BENCHMARKS.md</span>
-  </div>
-
-<script>
-const rows = [
-  {ref:'DwarfStar (ds4)', work:'DeepSeek-V4-Flash IQ2_XXS', v:1.144, note:'18.69 vs 16.33 tok/s'},
-  {ref:'vLLM',            work:'Qwen3.6-27B NVFP4, c1',     v:1.045, note:'86.05 vs 82.32 tok/s'},
-  {ref:'vLLM',            work:'Laguna-XS-2.1 NVFP4',       v:1.030, note:'44.46 vs 43.10 tok/s'},
-  {ref:'vLLM',            work:'Qwen3.6-35B-A3B, c32',      v:1.013, note:'3030.5 vs 2993.0 tok/s'},
-  {ref:'MLX-LM',          work:'Qwen3-0.6B, Apple M4',      v:0.976, note:'97.6% of warm total'},
-];
-
-const W=1432, H=585;
-const AX=64;                   // axis strip reserved at the bottom
-const LBL=470;                 // left label gutter
-const R=150;                   // right gutter for the value
-const lo=-0.055, hi=0.165;     // deviation domain around parity
-const pw=W-LBL-R;
-const x = d => LBL + pw*((d-lo)/(hi-lo));
-const zero = x(0);
-const rowH = (H-AX)/rows.length;
-const barH = 46;
-
-let g='';
-// faint engineering grid at 2% steps
-for(let d=-0.04; d<=0.16001; d+=0.02){
-  const gx=x(d), on0=Math.abs(d)<1e-9;
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-       font-weight="${on0?'700':'400'}">${(1+d).toFixed(2)}</text>`;
-}
-
-rows.forEach((r,i)=>{
-  const cy = i*rowH + rowH/2;
-  const d  = r.v-1;
-  const ahead = d>=0;
-  const col = ahead ? '#3ab4ca' : '#e0a944';
-  const x0 = ahead ? zero : x(d);
-  const w  = Math.abs(x(d)-zero);
-
-  // reference + workload, two weights on one line
-  g+=`<text x="${LBL-26}" y="${cy-4}" fill="#e8f1f4" font-size="25" font-weight="670" text-anchor="end">${r.ref}</text>`;
-  g+=`<text x="${LBL-26}" y="${cy+22}" fill="#5d757f" font-size="19" text-anchor="end">${r.work}</text>`;
-
-  g+=`<rect x="${x0}" y="${cy-barH/2}" width="${Math.max(w,2)}" height="${barH}" rx="4" fill="${col}" opacity="0.92"/>`;
-
-  // value, then the raw measurement under it
-  const vx = ahead ? x(d)+18 : zero+18;
-  g+=`<text x="${vx}" y="${cy+1}" fill="${col}" font-size="27" font-weight="700"
-       font-variant-numeric="tabular-nums">${r.v.toFixed(3)}&times;</text>`;
-  g+=`<text x="${vx}" y="${cy+23}" fill="#5d757f" font-size="17">${r.note}</text>`;
-});
-document.getElementById('c').innerHTML=g;
-</script>
-</body>
-</html>