Cpu.S390x.Inc - Vllm

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vLLM has experimental support for s390x architecture on IBM Z platform. For now, users must build from source to natively run on IBM Z platform.

Currently, the CPU implementation for s390x architecture supports FP32, BF16 and FP16.

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OS: Linux
SDK: gcc/g++ >= 14.0.0 or later with Command Line Tools
Instruction Set Architecture (ISA): VXE support is required. Works with Z14 and above.
Build install python packages: torchvision, llvmlite, numba, pyarrow (for testing), opencv-headless

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Currently, there are no pre-built IBM Z CPU wheels.

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Install the following packages from the package manager before building the vLLM. For example on RHEL 9.6:

bash

dnf install -y \
    which procps findutils tar vim git gcc-toolset-14 gcc-toolset-14-binutils gcc-toolset-14-libatomic-devel zlib-devel \
    libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
    openssl-devel openblas openblas-devel autoconf automake libtool cmake numpy libsndfile \
    clang llvm-devel llvm-static clang-devel

Install rust>=1.80 which is needed for outlines-core and uvloop python packages installation.

bash

curl https://sh.rustup.rs -sSf | sh -s -- -y && \
    . "$HOME/.cargo/env"

Execute the following commands to build and install vLLM from source.

!!! tip Please build the following dependencies, torchvision, llvmlite, numba, llguidance, pyarrow, opencv-headless from source before building vLLM.

bash

    uv pip install -v \
        --extra-index-url https://download.pytorch.org/whl/cpu \
        --torch-backend auto \
        -r requirements/build/cpu.txt \
        -r requirements/cpu.txt \
    VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
        uv pip install dist/*.whl

??? console "pip" bash pip install -v \ --extra-index-url https://download.pytorch.org/whl/cpu \ -r requirements/build/cpu.txt \ -r requirements/cpu.txt \ VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \ pip install dist/*.whl

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Currently, there are no pre-built IBM Z CPU images.

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bash

docker build -f docker/Dockerfile.s390x \
    --tag vllm-cpu-env .

# Launch 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 float \
    other vLLM OpenAI server arguments

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

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