别再用原生 HuggingFace硬扛本地大模型推理了，显存直接爆满，吞吐量慢得像乌龟爬！

伯克利实验室出品的 vLLM，用搞操作系统内存分页（PagedAttention）的那套黑科技，直接把 GPU 显存压榨到了极限，KV Cache 显存浪费近乎归零。

连续批处理（Continuous Batching）加上 OpenAI 兼容的 API 接口，这玩意儿就是目前部署开源大模型（Llama / Qwen / DeepSeek）的绝对性能霸主。

项目信息 • GitHub：vllm-project/vllm • 数据：40k+ Stars | Apache 2.0 开源协议
• 来源：UC Berkeley Sky Computing Lab

核心卖点 • 吞吐量翻倍： 比传统推理引擎吞吐量高 2-24 倍，能同时扛住极高并发请求。 • 显存零浪费： 独创 PagedAttention，显存利用率拉满，再长的上下文也不容易 OOM。 • 即插即用： 自带 OpenAI 兼容 API Server，两行代码替换现有接口。 • 全硬件适配： 支持 NVIDIA GPU、AMD、Intel XPU 乃至 CPU，通吃几乎所有开源模型。

链接 + 官网 GitHub：https://github.com/vllm-project/vllm… 官网：https://vllm.ai

vllm-project/vllm

Source: https://github.com/vllm-project/vllm

Easy, fast, and cheap LLM serving for everyone

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🔥 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 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 [email protected]

Media Kit

• If you wish to use vLLM’s logo, please refer to our media kit repo