opea/vllm repository overview

⁠ 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 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