vllm-cpu
A high-throughput and memory-efficient inference and serving engine for LLMs
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you need CPU-only LLM inference and have a compatible processor (x86_64 with AVX2 or aarch64 with NEON). The package is actively maintained, has no known vulnerabilities, and uses a permissive license. However, be aware it is community-maintained (not official vLLM), has 63 runtime dependencies creating medium install friction, and performance will be significantly slower than GPU inference—suitable for development, testing, and edge deployment rather than high-throughput production serving.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10+, Linux with glibc 2.28+ (Debian 10+, Ubuntu 18.04+, RHEL 8+), and x86_64 CPU with AVX2 minimum or aarch64 with NEON.
- Medium install friction due to 63 runtime dependencies including transformers, fastapi, and specialized libraries like lm-format-enforcer and xgrammar.
- Package is actively maintained with recent releases (3 days old) and no archived status, though it is community-maintained rather than part of the official vLLM project.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production deployments.
last release 2026-08-11 (3 days) · last repo commit 2026-08-11 · 8 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 255,523 downloads/mo, #8,477 on PyPI
Alternatives
Verify before relying
pip3 install vllm-cpu
from vllm import LLM, SamplingParams
llm = LLM(model="Qwen/Qwen3-0.6B", dtype="bfloat16")
outputs = llm.generate(["Hello, my name is"], SamplingParams(max_tokens=50))
print(outputs[0].outputs[0].text)- Actual performance gains relative to GPU inference or other CPU inference engines on representative models.
- Stability and production-readiness claims for the community-maintained fork versus official vLLM.
- Memory overhead and latency characteristics for different model sizes on various CPU architectures.
What it is and what it does
vllm-cpu is a community-maintained CPU inference engine for large language models that unifies multiple CPU instruction set architectures (AVX2, AVX-512, AMX, NEON, BF16, DOTPROD) into a single wheel. It automatically detects and uses the best available instruction set at runtime, eliminating the need for manual ISA-specific builds. The package ships with fallback implementations so the same wheel works across different x86_64 and aarch64 platforms.
It enables LLM inference on servers, laptops, and edge devices without requiring a GPU, making it suitable for development, testing, and moderate-scale deployments. The package includes an OpenAI-compatible API server via FastAPI and supports batch processing through the vLLM Python API. It depends on transformers, tokenizers, safetensors for model loading, and specialized libraries like lm-format-enforcer and xgrammar for output constraints.
Use it for
- Run inference on development laptops or CI/CD systems without GPU hardware.
- Deploy LLM services on ARM-based cloud instances (AWS Graviton, Ampere Altra) with automatic NEON/BF16 detection.
- Batch process text through language models on multi-socket CPU servers using NUMA optimization.
- Serve an OpenAI-compatible API endpoint on edge devices or on-premises infrastructure.
- Prototype and test LLM applications before scaling to GPU clusters.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you need CPU-only LLM inference and have a compatible processor (x86_64 with AVX2 or aarch64 with NEON). The package is actively maintained, has no known vulnerabilities, and uses a permissive license. However, be aware it is community-maintained (not official vLLM), has 63 runtime dependencies creating medium install friction, and performance will be significantly slower than GPU inference—suitable for development, testing, and edge deployment rather than high-throughput production serving.
Install
vllm-cpu on PyPI
Before you install
Medium install friction due to 63 runtime dependencies including transformers, fastapi, and specialized libraries like lm-format-enforcer and xgrammar. Package is actively maintained with recent releases (3 days old) and no archived status, though it is community-maintained rather than part of the official vLLM project.
Requires Python 3.10+, Linux with glibc 2.28+ (Debian 10+, Ubuntu 18.04+, RHEL 8+), and x86_64 CPU with AVX2 minimum or aarch64 with NEON.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production deployments.
Quickstart
pip3 install vllm-cpu
from vllm import LLM, SamplingParams
llm = LLM(model="Qwen/Qwen3-0.6B", dtype="bfloat16")
outputs = llm.generate(["Hello, my name is"], SamplingParams(max_tokens=50))
print(outputs[0].outputs[0].text)Verify before relying
- Actual performance gains relative to GPU inference or other CPU inference engines on representative models.
- Stability and production-readiness claims for the community-maintained fork versus official vLLM.
- Memory overhead and latency characteristics for different model sizes on various CPU architectures.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 63 packagesregexcachetoolspsutilsentencepiecenumpyrequeststqdmblake3py-cpuinfotransformerstokenizerssafetensorsprotobuffastapistarletteaiohttpopenaipydanticprometheus_clientpillowprometheus-fastapi-instrumentatortiktokenlm-format-enforcerllguidanceoutlines_corelarkxgrammartyping_extensionsfilelockpartial-json-parser |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 255,523 / month, #8,477 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information Analysis |
Evidence: vllm_cpu-0.27.1-cp38-abi3-manylinux_2_28_aarch64.whl; vllm_cpu-0.27.1-cp38-abi3-manylinux_2_28_x86_64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “cpu llm inference”
- vllm-cpuvllm-cpu provides CPU-optimized inference for large language models…
- llama-cpp-pythonPython bindings for llama.cpp that enable running large language…
- lmcacheLMCache is a KV cache management layer that stores, reuses, and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also py-cpuinfo · vllm · ipex-llm · vllm-tpu · keystone-engine · tpu-inference · lmcache · auto-round · gllm-inference-binary · mlx-vlm