# vLLM 开源项目：面向所有人的快速、易用、低成本 LLM 推理与服务库

- 来源：蚂蚁 inclusionAI：GitHub 新仓库
- 作者：inclusionAI
- 发布时间：2026-07-13 10:53
- AIHOT 分数：24
- AIHOT 链接：https://aihot.virxact.com/items/cmsfjarq7010troch9shx89kt
- 原文链接：https://github.com/inclusionAI/vllm-ling-v3

## AI 摘要

vLLM 是一个面向 LLM 推理与服务的快速易用库，由 UC Berkeley Sky Computing Lab 发起，现由来自 2000 多名贡献者的社区维护。它通过 PagedAttention、连续批处理、前缀缓存及 FP8/INT4 等多种量化技术实现高吞吐，支持 200+ Hugging Face 模型架构，并提供 OpenAI 兼容 API 及多硬件后端。

## 正文

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, 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 vllm

Or 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