vllm/docs/getting_started/installation/cpu.arm.inc.md

--8<-- [start:installation]

vLLM offers basic model inferencing and serving on Arm CPU platform, with support NEON, data types FP32, FP16 and BF16.

--8<-- [end:installation]

--8<-- [start:requirements]

• OS: Linux
• Compiler: gcc/g++ >= 12.3.0 (optional, recommended)
• Instruction Set Architecture (ISA): NEON support is required

--8<-- [end:requirements]

--8<-- [start:set-up-using-python]

--8<-- [end:set-up-using-python]

--8<-- [start:pre-built-wheels]

Pre-built vLLM wheels for Arm are available since version 0.11.2. These wheels contain pre-compiled C++ binaries. Please replace <version> in the commands below with a specific version string (e.g., 0.11.2).

uv pip install --pre vllm==<version>+cpu --extra-index-url https://wheels.vllm.ai/<version>%2Bcpu/

??? console "pip" bash pip install --pre vllm==<version>+cpu --extra-index-url https://wheels.vllm.ai/<version>%2Bcpu/

The uv approach works for vLLM v0.6.6 and later. A unique feature of uv is that packages in --extra-index-url have higher priority than the default index. If the latest public release is v0.6.6.post1, uv's behavior allows installing a commit before v0.6.6.post1 by specifying the --extra-index-url. In contrast, pip combines packages from --extra-index-url and the default index, choosing only the latest version, which makes it difficult to install a development version prior to the released version.

Install the latest code

LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides working pre-built Arm CPU wheels for every commit since v0.11.2 on https://wheels.vllm.ai/nightly. For native CPU wheels, this index should be used:

• https://wheels.vllm.ai/nightly/cpu/vllm

To install from nightly index, copy the link address of the *.whl under this index to run, for example:

uv pip install -U https://wheels.vllm.ai/c756fb678184b867ed94e5613a529198f1aee423/vllm-0.13.0rc2.dev11%2Bgc756fb678.cpu-cp38-abi3-manylinux_2_31_aarch64.whl # current nightly build (the filename will change!)

Install specific revisions

If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), specify the full commit hash in the index: https://wheels.vllm.ai/${VLLM_COMMIT}/cpu/vllm . Then, copy the link address of the *.whl under this index to run:

uv pip install -U <wheel-url>

--8<-- [end:pre-built-wheels]

--8<-- [start:build-wheel-from-source]

First, install the recommended compiler. We recommend using gcc/g++ >= 12.3.0 as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run:

sudo apt-get update  -y
sudo 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
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12

Second, clone the vLLM project:

git clone https://github.com/vllm-project/vllm.git vllm_source
cd vllm_source

Third, install required dependencies:

uv pip install -r requirements/cpu-build.txt --torch-backend cpu
uv pip install -r requirements/cpu.txt --torch-backend cpu

??? console "pip" bash pip install --upgrade pip pip install -v -r requirements/cpu-build.txt --extra-index-url https://download.pytorch.org/whl/cpu pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu

Finally, build and install vLLM:

VLLM_TARGET_DEVICE=cpu uv pip install . --no-build-isolation

If you want to develop vLLM, install it in editable mode instead.

VLLM_TARGET_DEVICE=cpu uv pip install -e . --no-build-isolation

Testing has been conducted on AWS Graviton3 instances for compatibility.

--8<-- [end:build-wheel-from-source]

--8<-- [start:pre-built-images]

Currently, there are no pre-built Arm CPU images.

--8<-- [end:pre-built-images]

--8<-- [start:build-image-from-source]

docker build -f docker/Dockerfile.cpu \
        --tag vllm-cpu-env .

# 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

!!! tip An alternative of --privileged=true is --cap-add SYS_NICE --security-opt seccomp=unconfined.

--8<-- [end:build-image-from-source]

--8<-- [start:extra-information]

--8<-- [end:extra-information]