vllm
vLLM是一款面向大语言模型（LLM）的高吞吐量、内存高效的推理与服务引擎，支持多类主流硬件和上百种主流大模型架构。
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vLLM是一款面向大语言模型（LLM）的高吞吐量、内存高效的推理与服务引擎，支持多类主流硬件和上百种主流大模型架构。
- 解决什么问题
- 企业部署大语言模型应用时，普遍面临推理响应慢、GPU显存利用率低、单卡可承载并发请求量少、部署成本高，以及适配不同模型、硬件需投入大量开发成本的问题。
- 适合什么团队
- 需要自建大模型推理服务的企业AI业务团队、大模型应用开发团队，以及有降低部署成本、提升服务吞吐量需求的团队。
- 使用前注意
- 本项目采用Apache-2.0许可可免费商用，目前处于活跃维护状态，部署需提前适配对应硬件的驱动环境，不同硬件的优化支持程度存在差异。
本页用于缩短初步筛选时间，不构成技术、采购或法律结论。 正式使用前请在真实业务数据上验证，并以官方说明与许可证为准。
从官方资料看清能力、部署与采用边界
以下内容依据项目公开 README 或模型卡翻译整理，代码、命令和产品名保持原样。
vLLM项目导读
1. 项目定位
vLLM是一款面向大语言模型推理与服务部署的开源工具，最初由加州大学伯克利分校Sky计算实验室研发，目前已发展为活跃的开源AI项目，由来自全球多所学术机构、企业的2000余名贡献者共同维护，核心目标是为用户提供简单、高效、低成本的大模型部署方案。
2. 核心能力
vLLM的核心能力可分为性能优化与易用性支持两类：
性能优化能力
- 通过PagedAttention技术实现注意力键值内存的高效管理，搭配请求连续批处理、前缀缓存等机制，提升服务吞吐量；
- 支持FP8、INT8、INT4等多种量化方案，降低显存占用；
- 内置多款优化的注意力、MoE内核，搭配推测解码、自动内核生成等技术，进一步提升推理速度。
易用性支持能力
- 无缝兼容Hugging Face上的主流大模型，无需额外适配即可直接部署；
- 支持并行采样、波束搜索等多种解码算法，以及分布式推理、流式输出、结构化输出、工具调用等常见业务需求；
- 自带兼容OpenAI规范的API服务，同时支持Anthropic Messages API和gRPC协议，可直接替换现有OpenAI接口的下游应用；
- 支持多LoRA（低秩适配，一种低成本微调大模型的技术）部署，可在单个服务上同时运行多个微调后的模型分支。
3. 支持的模型与硬件范围
模型支持
vLLM目前支持Hugging Face上200余种模型架构，覆盖以下主流类别：
- 纯解码器类大模型，如Llama、Qwen、Gemma；
- MoE（混合专家模型，一种通过拆分多个小专家网络提升大模型能力的架构）类大模型，如Mixtral、DeepSeek-V3、Qwen-MoE；
- 多模态模型，如LLaVA、Qwen-VL；
- 嵌入与检索模型，可用于RAG（检索增强生成，一种通过外接知识库提升大模型回答准确性的技术）相关场景；
- 奖励与分类模型，如Qwen-Math。
完整的支持模型列表可查阅官方文档。
硬件支持
vLLM支持多类主流硬件，包括NVIDIA、AMD、Intel的GPU，x86、ARM、PowerPC架构的CPU，同时支持Google TPU、华为昇腾、苹果芯片等硬件的插件适配。
4. 快速上手方式
vLLM的安装方式十分简单，官方推荐使用uv工具安装，执行命令：
uv pip install vllm也可使用pip安装，若需要二次开发可从源码编译安装。官方提供了完整的安装指南、快速入门教程，用户可根据需求查阅对应文档完成部署。
5. 许可证与采用建议
vLLM采用Apache-2.0开源许可，允许企业免费商用、修改与二次分发，无许可付费风险。
目前项目处于活跃维护状态，官方提供了完善的文档、用户论坛、开发者沟通渠道，普通技术问题可提交GitHub Issue，用户交流可访问官方论坛，贡献代码可加入Slack社区，商务合作可联系官方邮箱collaboration@vllm.ai。
官方资料与来源
- amd
- blackwell
- cuda
- deepseek
- deepseek-v3
- gpt
- gpt-oss
- inference
- kimi
- llama
- llm
- llm-serving
## Getting Started Install vLLM with [`uv`](https://docs.astral.sh/uv/) (recommended) or `pip`: ```bash uv pip install vllm ``` Or [build from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source) for development. Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more. - [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html) - [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html) - [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html)
该片段来自项目 README，仅用于初步判断；实际部署请以官方文档为准。
核对上游原始说明节选
A high-throughput and memory-efficient inference and serving engine for LLMs
Easy, fast, and cheap LLM serving for everyone
| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack |
🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us.
---
About
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open-source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors.
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, chunked prefill, prefix caching
- Fast and flexible model execution with piecewise and full CUDA/HIP graphs
- Quantization: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, ModelOpt, TorchAO, and more
- Optimized attention kernels including FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, and Triton
- Optimized GEMM/MoE kernels for various precisions using CUTLASS, TRTLLM-GEN, CuTeDSL
- Speculative decoding including n-gram, suffix, EAGLE, DFlash
- Automatic kernel generation and graph-level transformations using torch.compile
- Disaggregated prefill, decode, and encode
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, expert, and context parallelism for distributed inference
- Streaming outputs
- Generation of structured outputs using xgrammar or guidance
- Tool calling and reasoning parsers
- OpenAI-compatible API server, plus Anthropic Messages API and gRPC support
- Efficient multi-LoRA support for dense and MoE layers
- Support for NVIDIA GPUs, AMD GPUs, Intel GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more.
vLLM seamlessly supports 200+ model architectures on Hugging Face, including:
- Decoder-only LLMs (e.g., Llama, Qwen, Gemma)
- Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)
- Hybrid attention and state-space models (e.g., Mamba, Qwen3.5)
- Multi-modal models (e.g., LLaVA, Qwen-VL, Pixtral)
- Embedding and retrieval models (e.g., E5-Mistral, GTE, ColBERT)
- Reward and classification models (e.g., Qwen-Math)
Find the full list of supported models here.
Getting Started
Install vLLM with uv (recommended) or pip:
uv pip install vllmOr build from source for development.
Visit our documentation to learn more.
- Installation
- Quickstart
- List of Supported Models
Contributing
We welcome and value any contributions and collaborations. Please check out Contributing to vLLM for how to get involved.
Citation
If you use vLLM for your research, please cite our paper:
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}Contact Us
- For technical questions and feature requests, please use GitHub Issues
- For discussing with fellow users, please use the vLLM Forum
- For coordinating contributions and development, please use Slack
- For security disclosures, please use GitHub's Security Advisories feature
- For collaborations and partnerships, please contact us at collaboration@vllm.ai
Media Kit
- If you wish to use vLLM's logo, please refer to our media kit repo
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