# --8<-- [start:installation]

vLLM supports basic model inferencing and serving on x86 CPU platform, with data types FP32, FP16 and BF16.

# --8<-- [end:installation]
# --8<-- [start:requirements]

- OS: Linux
- CPU flags: `avx512f`, `avx512_bf16` (Optional), `avx512_vnni` (Optional)

!!! tip
    Use `lscpu` to check the CPU flags.

# --8<-- [end:requirements]
# --8<-- [start:set-up-using-python]

# --8<-- [end:set-up-using-python]
# --8<-- [start:pre-built-wheels]

# --8<-- [end:pre-built-wheels]
# --8<-- [start:build-wheel-from-source]

--8<-- "docs/getting_started/installation/cpu/build.inc.md"

# --8<-- [end:build-wheel-from-source]
# --8<-- [start:pre-built-images]

[https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo](https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo)

!!! warning
    If deploying the pre-built images on machines only contain `avx512f`, `Illegal instruction` error may be raised. It is recommended to build images for these machines with `--build-arg VLLM_CPU_AVX512BF16=false` and `--build-arg VLLM_CPU_AVX512VNNI=false`.

# --8<-- [end:pre-built-images]
# --8<-- [start:build-image-from-source]

```bash
docker build -f docker/Dockerfile.cpu \
        --build-arg VLLM_CPU_AVX512BF16=false (default)|true \
        --build-arg VLLM_CPU_AVX512VNNI=false (default)|true \
        --tag vllm-cpu-env \
        --target vllm-openai .

# Launching OpenAI server
docker run --rm \
            --privileged=true \
            --shm-size=4g \
            -p 8000:8000 \
            -e VLLM_CPU_KVCACHE_SPACE=<KV cache space> \
            -e VLLM_CPU_OMP_THREADS_BIND=<CPU cores for inference> \
            vllm-cpu-env \
            --model=meta-llama/Llama-3.2-1B-Instruct \
            --dtype=bfloat16 \
            other vLLM OpenAI server arguments
```

# --8<-- [end:build-image-from-source]
# --8<-- [start:extra-information]
# --8<-- [end:extra-information]