vllm-cpu-avx512bf16 0.12.0

vLLM CPU inference engine (AVX512 + VNNI + BF16 optimized)

Easy, fast, and cheap LLM serving for everyone

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About

vLLM is a fast and easy-to-use library for LLM inference and serving. This PyPl package has only supports AVX512+VNNI+AVX512BF16. No support for AMXBF16 is available in this package. CPU inference will have the above available instruction set accelerations.

Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

vLLM is fast with:

• State-of-the-art serving throughput
• Efficient management of attention key and value memory with PagedAttention
• Continuous batching of incoming requests
• Fast model execution with AVX512+VNNI+AVX512BF16 on supported CPUs Use this package ONLY IF your CPU have avx512bf16 or newer instruction sets.
• Quantizations: GPTQ, AWQ, AutoRound, INT4, INT8, and FP8
• Optimized CPU kernels, including integration with FlashAttention and FlashInfer
• Speculative decoding
• Chunked prefill

vLLM is flexible and easy to use with:

• Seamless integration with popular Hugging Face models
• High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more
• Tensor, pipeline, data and expert parallelism support for distributed inference
• Streaming outputs
• OpenAI-compatible API server
• Support for x86_64, PowerPC CPUs, Arm CPUs and Applie Scilicon (CPU inference). This package does not support any GPU inference. For GPU inference support use the official vLLM PypI
• Prefix caching support
• Multi-LoRA support

vLLM seamlessly supports most popular open-source models on HuggingFace, including:

• Transformer-like LLMs (e.g., Llama)
• Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
• Embedding Models (e.g., E5-Mistral)
• Multi-modal LLMs (e.g., LLaVA)

Find the full list of supported models here.

Importnt Notes

• Install this package on Linux envirenment only. For Windows you will have to use WSL2 or later
• This package has a Container.io (Docker/Podman etc.) compatible image in Docker Hub
• Apache Licence of main vLLM project
• GPL License of this CPU specific vLLM package

Getting Started

Install vLLM with pip or uv

mkdir -p /path/to/vllm
cd /path/to/vllm
uv venv
uv pip install torch==2.8.0 torchvision --index-url https://download.pytorch.org/whl/cpu
uv pip install vllm-cpu-avx512bf16

Install uv on Linux environment using CURL:

curl -LsSf https://astral.sh/uv/install.sh | sh

or using WGET

wget -qO- https://astral.sh/uv/install.sh | sh

if you wanna install a specific version of uv

curl -LsSf https://astral.sh/uv/0.9.11/install.sh | sh

vllm-cpu

This CPU specific vLLM has 5 optimized wheel packages from the upstream vLLM source code:

Package	Optimizations	Target CPUs
vllm-cpu	Baseline (no AVX512)	All x86_64 and ARM64 CPUs
vllm-cpu-avx512	AVX512	Intel Skylake-X and newer
vllm-cpu-avx512vnni	AVX512 + VNNI	Intel Cascade Lake and newer
vllm-cpu-avx512bf16	AVX512 + VNNI + BF16	Intel Cooper Lake and newer
vllm-cpu-amxbf16	AVX512 + VNNI + BF16 + AMX	Intel Sapphire Rapids (4th gen Xeon+)

Each package is compiled with specific CPU instruction set flags for optimal inference performance.

Check available CPU instruction sets

lscpu | grep -i flags

Example list of CPUs with their supported instruction sets

CPU Architecture (Intel/AMD)	AVX2	AVX-512 F (Base)	VNNI (INT8)	BF16 (BFloat16) (via AVX-512)	AMX-BF16 (via Tile Unit)
Intel 4th Gen / AMD Ryzen Zen2 & Newer	Yes	No	No	No	No
Intel Skylake-SP / Skylake-X / AMD Zen 4 & Newer	Yes	Yes	No	No	No
Intel Cooper Lake (3rd Gen Xeon) / AMD Zen 4 (EPYC) / Ryzen Zen5 & Newer	Yes	Yes	Yes	Yes	No
Intel Sapphire Rapids (4th Gen Xeon) & Newer	Yes	Yes	Yes	Yes	Yes

***Currently no AMD CPU support AMXBF16. AMD expected to include AMXBF16 support from AMD Zen 7 CPUs

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Buy Me a Coffee

Your support encourages me to keep creating/supporting my open-source projects. If you found value in this project, you can buy me a coffee to keep me up all the sleepless nights.

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• vLLM documentation to learn more.
• GitHub Repo for this package
• DockerHub
• List of Supported Models