现在Docker镜像仓库已经没有cpu低版本的vllm来部署大语言模型，我用源码来重新构建了一个docker镜像，然后部署了Qwen3:0.6B的模型，在cpu上部署的。
1. 拉取vllm源码：https://github.com/vllm-project/vllm.git
2. 用git命令切换到v0.9.2版本
3. 进入源码的docker文件夹下，有一个Dockerfile.cpu文件，这就是构建镜像的脚本
4. 我这边直接以Dockerfile.cpu构建镜像报错了，打开该文件，发现里面安装依赖时用的是requirements/cpu.txt
5. 我这边报错是transformers的错误，我在cpu.txt中加了一行
transformers==4.51.1
6. 然后再以Dockerfile.cpu来构建镜像，就可以了
docker build -f ./docker/Dockerfile.cpu -t vllm-cpu-env:v0.9.2 --build-arg VLLM_CPU_DISABLE_AVX512=true --shm-size=4g .
7. Dockerfile.cpu文件的内容如下，我这里记录下来：
# This vLLM Dockerfile is used to construct image that can build and run vLLM on x86 CPU platform. # # Build targets: # vllm-openai (default): used for serving deployment # vllm-test: used for CI tests # vllm-dev: used for development # # Build arguments: # PYTHON_VERSION=3.12 (default)|3.11|3.10|3.9 # VLLM_CPU_DISABLE_AVX512=false (default)|true # VLLM_CPU_AVX512BF16=false (default)|true # VLLM_CPU_AVX512VNNI=false (default)|true ########################## BASE IMAGE ######################### FROM ubuntu:22.04 AS baseWORKDIR /workspace/ARG PYTHON_VERSION=3.12 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"ENV LD_PRELOAD=""# Install minimal dependencies and uv RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \--mount=type=cache,target=/var/lib/apt,sharing=locked \apt-get update -y \&& apt-get install -y --no-install-recommends ccache git curl wget ca-certificates \gcc-12 g++-12 libtcmalloc-minimal4 libnuma-dev ffmpeg libsm6 libxext6 libgl1 jq lsof \&& update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12 \&& curl -LsSf https://astral.sh/uv/install.sh | sh \&& echo 'export PATH="/root/.local/bin:$PATH"' >> /root/.bashrc \&& /root/.local/bin/uv --version # 立即更新 PATH 并设置缓存和编译相关环境变量 ENV CCACHE_DIR=/root/.cache/ccache ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache ENV PATH="/root/.local/bin:$PATH" # 创建虚拟环境（此时 uv 已在 PATH 中） ENV VIRTUAL_ENV="/opt/venv" ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV} ENV PATH="$VIRTUAL_ENV/bin:$PATH" ENV UV_HTTP_TIMEOUT=500# Install Python dependencies ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} ENV UV_INDEX_STRATEGY="unsafe-best-match" ENV UV_LINK_MODE="copy" RUN --mount=type=cache,target=/root/.cache/uv \--mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \--mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \uv pip install --upgrade pip && \uv pip install -r requirements/cpu.txtENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/opt/venv/lib/libiomp5.so:$LD_PRELOAD"RUN echo 'ulimit -c 0' >> ~/.bashrc######################### BUILD IMAGE ######################### FROM base AS vllm-buildARG GIT_REPO_CHECK=0 # Support for building with non-AVX512 vLLM: docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" ... ARG VLLM_CPU_DISABLE_AVX512=0 ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512} # Support for building with AVX512BF16 ISA: docker build --build-arg VLLM_CPU_AVX512BF16="true" ... ARG VLLM_CPU_AVX512BF16=0 ENV VLLM_CPU_AVX512BF16=${VLLM_CPU_AVX512BF16} # Support for building with AVX512VNNI ISA: docker build --build-arg VLLM_CPU_AVX512VNNI="true" ... ARG VLLM_CPU_AVX512VNNI=0 ENV VLLM_CPU_AVX512VNNI=${VLLM_CPU_AVX512VNNI}WORKDIR /workspace/vllmRUN --mount=type=cache,target=/root/.cache/uv \--mount=type=bind,src=requirements/cpu-build.txt,target=requirements/build.txt \uv pip install -r requirements/build.txtCOPY . . RUN --mount=type=bind,source=.git,target=.git \if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fiRUN --mount=type=cache,target=/root/.cache/uv \--mount=type=cache,target=/root/.cache/ccache \--mount=type=cache,target=/workspace/vllm/.deps,sharing=locked \--mount=type=bind,source=.git,target=.git \VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel ######################### TEST DEPS ######################### FROM base AS vllm-test-depsWORKDIR /workspace/vllmRUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \cp requirements/test.in requirements/cpu-test.in && \sed -i '/mamba_ssm/d' requirements/cpu-test.in && \sed -i 's/torch==.*/torch==2.6.0/g' requirements/cpu-test.in && \sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpuRUN --mount=type=cache,target=/root/.cache/uv \uv pip install -r requirements/cpu-test.txt ######################### DEV IMAGE ######################### FROM vllm-build AS vllm-devWORKDIR /workspace/vllmRUN --mount=type=cache,target=/var/cache/apt,sharing=locked \--mount=type=cache,target=/var/lib/apt,sharing=locked \apt-get install -y --no-install-recommends vim numactl xz-utils# install development dependencies (for testing) RUN --mount=type=cache,target=/root/.cache/uv \uv pip install -e tests/vllm_test_utils RUN --mount=type=cache,target=/root/.cache/uv \--mount=type=cache,target=/root/.cache/ccache \--mount=type=bind,source=.git,target=.git \VLLM_TARGET_DEVICE=cpu python3 setup.py develop COPY --from=vllm-test-deps /workspace/vllm/requirements/cpu-test.txt requirements/test.txtRUN --mount=type=cache,target=/root/.cache/uv \uv pip install -r requirements/dev.txt && \pre-commit install --hook-type pre-commit --hook-type commit-msgENTRYPOINT ["bash"]######################### TEST IMAGE ######################### FROM vllm-test-deps AS vllm-testWORKDIR /workspace/RUN --mount=type=cache,target=/root/.cache/uv \--mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \uv pip install dist/*.whlADD ./tests/ ./tests/ ADD ./examples/ ./examples/ ADD ./benchmarks/ ./benchmarks/ ADD ./vllm/collect_env.py . ADD ./.buildkite/ ./.buildkite/# install development dependencies (for testing) RUN --mount=type=cache,target=/root/.cache/uv \uv pip install -e tests/vllm_test_utils ENTRYPOINT ["bash"]######################### RELEASE IMAGE ######################### FROM base AS vllm-openaiWORKDIR /workspace/RUN --mount=type=cache,target=/root/.cache/uv \--mount=type=cache,target=/root/.cache/ccache \--mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \uv pip install dist/*.whlENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
8. 运行Qwen3:0.6B模型
sudo docker run -v ./Qwen:/home/models --name vllm-qwen -p 9091:8000 vllm-cpu-env:v0.9.2 --model /home/models/Qwen3-0.6B --served-model-name qwen3:0.6b --enable-reasoning --reasoning-parser deepseek_r1 --max-model-len 30720 --gpu-memory-utilization 0.95 --disable-log-stats
9. 之后我还用这个镜像部署过qwen的嵌入模型，还部署过重排模型
# 嵌入模型 docker run \-v ./Qwen:/home/models \--name vllm-embedding \-p 9092:8000 \vllm-cpu-env:v0.9.2 \--model /home/models/Qwen3-Embedding-0.6B \--served-model-name qwen3-embedding:0.6b \--task embed--max-model-len 30720 --gpu-memory-utilization 0.95 --disable-log-stats# 重排模型 docker run \-v ./BAAI:/home/models \--name vllm-reranker \-p 9095:8000 \vllm-cpu-env:v0.9.2 \--model /home/models/bge-reranker-v2-m3 \--served-model-name bge-reranker-v2-m3 \--task score--disable-log-stats
10. 后面有GPU环境了，我还用vllm的GPU版本镜像部署过多模态模型
docker run --gpus '"device=0,1"' \-p 33003:8000 \--ipc=host \-v /mnt/inaisfs/user-fs/iei/Qwen3.6-35B-A3B:/home/models/Qwen3.6-35B-A3B \vllm/vllm-openai:v0.22.0-cu129 \--model /home/models/Qwen3.6-35B-A3B \--served-model-name Qwen/Qwen3.6-35B-A3B \--tensor-parallel-size 2 \--reasoning-parser qwen3 \--enable-prefix-caching \--max-model-len 262144