fix(buun-llama-cpp): shim cudaMemcpy{To,From}Symbol + WARP_SIZE on fwht128 shuffles

Two more hipblas-only build failures in buun's fattn.cu, fixed under the
same patches/ infrastructure:

1. cudaMemcpyToSymbol / cudaMemcpyFromSymbol — buun's Q² calibration +
   TCQ codebook upload paths call the symbol variants of cudaMemcpy.
   ggml/src/ggml-cuda/vendors/hip.h aliases every other cudaMemcpy*
   name (cudaMemcpy, cudaMemcpyAsync, cudaMemcpy2DAsync, …) but the
   symbol pair was never added. 15+ "use of undeclared identifier"
   errors across fattn.cu lines 40, 54, 74-76, 94, 100-101, 371, 883,
   905, 954, 976, 1449, 1463. Add the two missing aliases alongside
   the existing memcpy block.

2. __shfl_xor_sync fwht128 calls — same 3-arg omission pattern as the
   earlier argmax top-K fix. Lines 512 (ggml_cuda_fwht128 intra-warp
   butterfly) and 536 (fwht128_store_half neighbor fetch) drop the
   width argument that hip.h:33 requires. Add WARP_SIZE.

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

.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
+Only humans can certify the Developer Certificate of Origin (DCO). AI
-certify the Developer Certificate of Origin (DCO). The human submitter
+agents MUST NOT invent or guess a human identity for `Signed-off-by` —
-is responsible for:
+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.
+AI agents MUST NOT add `Co-Authored-By` trailers for themselves. A human
-A human reviewer owns the contribution; the AI's involvement is recorded
+reviewer owns the contribution; the AI's involvement is recorded via
-via `Assisted-by` (see below).
+`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.

.github/workflows/backend.yml

@@ -399,6 +399,19 @@ jobs:
             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'
+            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"
@@ -894,6 +907,19 @@ jobs:
             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'
+            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: 'cublas'
             cuda-major-version: "13"
             cuda-minor-version: "0"
@@ -920,6 +946,19 @@ jobs:
             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'
+            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"
@@ -1454,6 +1493,19 @@ jobs:
             dockerfile: "./backend/Dockerfile.turboquant"
             context: "./"
             ubuntu-version: '2404'
+          - build-type: 'hipblas'
+            cuda-major-version: ""
+            cuda-minor-version: ""
+            platforms: 'linux/amd64'
+            tag-latest: 'auto'
+            tag-suffix: '-gpu-rocm-hipblas-buun-llama-cpp'
+            runs-on: 'ubuntu-latest'
+            base-image: "rocm/dev-ubuntu-24.04:7.2.1"
+            skip-drivers: 'false'
+            backend: "buun-llama-cpp"
+            dockerfile: "./backend/Dockerfile.buun-llama-cpp"
+            context: "./"
+            ubuntu-version: '2404'
           - build-type: 'hipblas'
             cuda-major-version: ""
             cuda-minor-version: ""
@@ -1703,6 +1755,19 @@ jobs:
             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'
+            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: ""
@@ -1729,6 +1794,19 @@ jobs:
             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'
+            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: 'intel'
             cuda-major-version: ""
             cuda-minor-version: ""
@@ -2134,6 +2212,19 @@ jobs:
             dockerfile: "./backend/Dockerfile.turboquant"
             context: "./"
             ubuntu-version: '2404'
+          - build-type: ''
+            cuda-major-version: ""
+            cuda-minor-version: ""
+            platforms: 'linux/amd64,linux/arm64'
+            tag-latest: 'auto'
+            tag-suffix: '-cpu-buun-llama-cpp'
+            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: ''
             cuda-major-version: ""
             cuda-minor-version: ""
@@ -2173,6 +2264,19 @@ jobs:
             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'
+            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: 'vulkan'
             cuda-major-version: ""
             cuda-minor-version: ""
@@ -2199,6 +2303,19 @@ jobs:
             dockerfile: "./backend/Dockerfile.turboquant"
             context: "./"
             ubuntu-version: '2404'
+          - build-type: 'vulkan'
+            cuda-major-version: ""
+            cuda-minor-version: ""
+            platforms: 'linux/amd64,linux/arm64'
+            tag-latest: 'auto'
+            tag-suffix: '-gpu-vulkan-buun-llama-cpp'
+            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'
           # Stablediffusion-ggml
           - build-type: ''
             cuda-major-version: ""

.github/workflows/test-extra.yml

@@ -32,6 +32,7 @@ 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 }}
@@ -613,6 +614,30 @@ 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

@@ -1,5 +1,5 @@
 # Disable parallel execution for backend builds
-.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr 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/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/tinygrad backends/sherpa-onnx
+.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/buun-llama-cpp backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr 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/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/tinygrad backends/sherpa-onnx
 
 GOCMD=go
 GOTEST=$(GOCMD) test
@@ -545,6 +545,19 @@ test-extra-backend-turboquant: docker-build-turboquant
 	BACKEND_TEST_CACHE_TYPE_V=turbo3 \
 	$(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
@@ -949,6 +962,11 @@ BACKEND_IK_LLAMA_CPP = ik-llama-cpp|ik-llama-cpp|.|false|false
 # turboquant is a llama.cpp fork with TurboQuant KV-cache quantization.
 # Reuses backend/cpp/llama-cpp grpc-server sources via a thin wrapper Makefile.
 BACKEND_TURBOQUANT = turboquant|turboquant|.|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
@@ -1029,6 +1047,7 @@ endef
 $(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
 $(eval $(call generate-docker-build-target,$(BACKEND_IK_LLAMA_CPP)))
 $(eval $(call generate-docker-build-target,$(BACKEND_TURBOQUANT)))
+$(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_HUGGINGFACE)))
@@ -1080,7 +1099,7 @@ $(eval $(call generate-docker-build-target,$(BACKEND_SHERPA_ONNX)))
 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-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice 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-qwen3-tts-cpp docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx
+docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-buun-llama-cpp docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice 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-qwen3-tts-cpp docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx
 
 ########################################################
 ### Mock Backend for E2E Tests

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/buun-llama-cpp/Makefile

@@ -0,0 +1,85 @@
+
+# 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
+	$(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
+	$(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
+
+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 +1,162 @@
+#!/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. Wire up buun-exclusive speculative-decoding option handlers
+#      (tree_budget / draft_topk) alongside the existing spec_* handlers.
+#      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 'optname, "tree_budget"' "$SRC"; then
+    echo "==> $SRC already has DFlash option handlers, skipping"
+else
+    echo "==> patching $SRC to add tree_budget / draft_topk option handlers"
+
+    # Insert two new `else if` handlers between the inner close-brace of the
+    # `spec_p_split` block and the next `} else if (…spec_ngram_size_n…)` line.
+    # Upstream writes each `} else if` as a single physical line, so we don't
+    # emit an outer `}` ourselves — the existing next line provides both the
+    # close of our `draft_topk` block and the open of `spec_ngram_size_n`.
+    # Anchor on the exact 3-line body of spec_p_split so we can't drift.
+    awk '
+        prev2 == "        } else if (!strcmp(optname, \"spec_p_split\")) {" &&
+        prev1 ~ /^ +if \(optval != NULL\) \{$/ &&
+        $0    ~ /^ +try \{ params\.speculative\.p_split = std::stof\(optval_str\); \} catch \(\.\.\.\) \{\}$/ &&
+        !done {
+            print                        # print the try-line itself
+            getline inner_close          # read "            }" closing the inner if
+            print inner_close            # print it — this closes spec_p_split body
+            print "        // buun-llama-cpp DFlash options — added by patch-grpc-server.sh"
+            print "        } else if (!strcmp(optname, \"tree_budget\")) {"
+            print "            if (optval != NULL) {"
+            print "                try { params.speculative.tree_budget = std::stoi(optval_str); } catch (...) {}"
+            print "            }"
+            print "        } else if (!strcmp(optname, \"draft_topk\")) {"
+            print "            if (optval != NULL) {"
+            print "                try { params.speculative.draft_topk = std::stoi(optval_str); } catch (...) {}"
+            print "            }"
+            # The next source line (`} else if (…spec_ngram_size_n…) {`) closes
+            # our draft_topk block and continues the chain naturally; fall back
+            # into the main loop to emit it and everything after.
+            done = 1
+            prev2 = prev1
+            prev1 = inner_close
+            next
+        }
+        { print; prev2 = prev1; prev1 = $0 }
+        END {
+            if (!done) {
+                print "patch-grpc-server.sh: spec_p_split anchor not found" > "/dev/stderr"
+                exit 1
+            }
+        }
+    ' "$SRC" > "$SRC.tmp"
+    mv "$SRC.tmp" "$SRC"
+
+    echo "==> DFlash option-handler patch OK"
+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 $CURDIR/buun-llama-cpp-fallback "$@"

core/config/meta/constants.go

@@ -37,6 +37,14 @@ 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

@@ -34,6 +34,7 @@ func (i *LlamaCPPImporter) AdditionalBackends() []KnownBackendEntry {
 	return []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: "buun-llama-cpp", Modality: "text", Description: "GGUF drop-in replacement for llama-cpp with DFlash speculative decoding and TurboQuant/TCQ KV-cache quantization"},
 	}
 }
 
@@ -127,7 +128,7 @@ 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":
+		case "ik-llama-cpp", "turboquant", "buun-llama-cpp":
 			backend = b
 		}
 	}

core/gallery/importers/llama-cpp_test.go

@@ -181,6 +181,23 @@ 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 two curated drop-in replacements.
@@ -375,7 +392,7 @@ var _ = Describe("LlamaCPPImporter", func() {
 	})
 
 	Context("AdditionalBackends", func() {
-		It("advertises ik-llama-cpp and turboquant as drop-in replacements", func() {
+		It("advertises ik-llama-cpp, turboquant, and buun-llama-cpp as drop-in replacements", func() {
 			entries := importer.AdditionalBackends()
 
 			names := make([]string, 0, len(entries))
@@ -384,7 +401,7 @@ var _ = Describe("LlamaCPPImporter", func() {
 				names = append(names, e.Name)
 				byName[e.Name] = e
 			}
-			Expect(names).To(ConsistOf("ik-llama-cpp", "turboquant"))
+			Expect(names).To(ConsistOf("ik-llama-cpp", "turboquant", "buun-llama-cpp"))
 
 			ik := byName["ik-llama-cpp"]
 			Expect(ik.Modality).To(Equal("text"))
@@ -393,6 +410,10 @@ var _ = Describe("LlamaCPPImporter", func() {
 			tq := byName["turboquant"]
 			Expect(tq.Modality).To(Equal("text"))
 			Expect(tq.Description).NotTo(BeEmpty())
+
+			bn := byName["buun-llama-cpp"]
+			Expect(bn.Modality).To(Equal("text"))
+			Expect(bn.Description).NotTo(BeEmpty())
 		})
 	})
 })

docs/content/features/text-generation.md

@@ -631,6 +631,83 @@ 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/reference/compatibility-table.md

@@ -20,6 +20,7 @@ LocalAI will attempt to automatically load models which are not explicitly confi
 |---------|-------------|------------|------------|-----------|-------------|
 | [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+) |
+| [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) |
 | [vLLM](https://github.com/vllm-project/vllm) | Fast LLM serving with PagedAttention | GPT, Functions | no | yes | CPU, CUDA 12, ROCm, Intel |
 | [vLLM Omni](https://github.com/vllm-project/vllm) | Unified multimodal generation (text, image, video, audio) | Multimodal GPT, Functions | no | yes | CUDA 12, ROCm |
 | [transformers](https://github.com/huggingface/transformers) | HuggingFace Transformers framework | GPT, Embeddings, Multimodal | yes | yes* | CPU, CUDA 12/13, ROCm, Intel, Metal |

scripts/changed-backends.js

@@ -32,6 +32,12 @@ 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 fork-of-a-fork (spiritbuun/buun-llama-cpp forks
+    // TheTom/llama-cpp-turboquant) that reuses backend/cpp/llama-cpp sources
+    // the same way turboquant does. Changes to either dir retrigger it.
+    return `backend/cpp/buun-llama-cpp/`;
+  }
   if (item.dockerfile.endsWith("llama-cpp")) {
     return `backend/cpp/llama-cpp/`;
   }
@@ -138,9 +144,10 @@ async function getChangedFiles() {
   // Per-backend boolean outputs
   for (const [backend, pathPrefix] of allBackendPaths) {
     let changed = changedFiles.some(file => file.startsWith(pathPrefix));
-    // turboquant reuses backend/cpp/llama-cpp sources via a thin wrapper;
+    // turboquant and buun-llama-cpp reuse backend/cpp/llama-cpp sources via
-    // changes to either directory should retrigger its pipeline.
+    // thin wrapper Makefiles; changes to that directory should retrigger
-    if (backend === "turboquant" && !changed) {
+    // their pipelines too.
+    if ((backend === "turboquant" || backend === "buun-llama-cpp") && !changed) {
       changed = changedFiles.some(file => file.startsWith("backend/cpp/llama-cpp/"));
     }
     fs.appendFileSync(process.env.GITHUB_OUTPUT, `${backend}=${changed ? 'true' : 'false'}\n`);