{"info":{"author":"vLLM Team","author_email":null,"bugtrack_url":null,"classifiers":["Intended Audience :: Developers","Intended Audience :: Information Technology","Intended Audience :: Science/Research","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Programming Language :: Python :: 3.14","Topic :: Scientific/Engineering :: Artificial Intelligence","Topic :: Scientific/Engineering :: Information Analysis"],"description":"<!-- markdownlint-disable MD001 MD041 -->\n<p align=\"center\">\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/vllm-project/vllm/main/docs/assets/logos/vllm-logo-text-dark.png\">\n    <img alt=\"vLLM\" src=\"https://raw.githubusercontent.com/vllm-project/vllm/main/docs/assets/logos/vllm-logo-text-light.png\" width=55%>\n  </picture>\n</p>\n\n<h3 align=\"center\">\nEasy, fast, and cheap LLM serving for everyone\n</h3>\n\n<p align=\"center\">\n| <a href=\"https://docs.vllm.ai\"><b>Documentation</b></a> | <a href=\"https://blog.vllm.ai/\"><b>Blog</b></a> | <a href=\"https://arxiv.org/abs/2309.06180\"><b>Paper</b></a> | <a href=\"https://x.com/vllm_project\"><b>Twitter/X</b></a> | <a href=\"https://discuss.vllm.ai\"><b>User Forum</b></a> | <a href=\"https://slack.vllm.ai\"><b>Developer Slack</b></a> |\n</p>\n\n🔥 We have built a vLLM website to help you get started with vLLM. Please visit [vllm.ai](https://vllm.ai) to learn more.\nFor events, please visit [vllm.ai/events](https://vllm.ai/events) to join us.\n\n---\n\n## About\n\nvLLM is a fast and easy-to-use library for LLM inference and serving.\n\nOriginally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) 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.\n\nvLLM is fast with:\n\n- State-of-the-art serving throughput\n- Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)\n- Continuous batching of incoming requests, chunked prefill, prefix caching\n- Fast and flexible model execution with piecewise and full CUDA/HIP graphs\n- Quantization: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, ModelOpt, TorchAO, and [more](https://docs.vllm.ai/en/latest/features/quantization/index.html)\n- Optimized attention kernels including FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, and Triton\n- Optimized GEMM/MoE kernels for various precisions using CUTLASS, TRTLLM-GEN, CuTeDSL\n- Speculative decoding including n-gram, suffix, EAGLE, DFlash\n- Automatic kernel generation and graph-level transformations using torch.compile\n- Disaggregated prefill, decode, and encode\n\nvLLM is flexible and easy to use with:\n\n- Seamless integration with popular Hugging Face models\n- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more\n- Tensor, pipeline, data, expert, and context parallelism for distributed inference\n- Streaming outputs\n- Generation of structured outputs using xgrammar or guidance\n- Tool calling and reasoning parsers\n- OpenAI-compatible API server, plus Anthropic Messages API and gRPC support\n- Efficient multi-LoRA support for dense and MoE layers\n- 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.\n\nvLLM seamlessly supports 200+ model architectures on Hugging Face, including:\n\n- Decoder-only LLMs (e.g., Llama, Qwen, Gemma)\n- Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS)\n- Hybrid attention and state-space models (e.g., Mamba, Qwen3.5)\n- Multi-modal models (e.g., LLaVA, Qwen-VL, Pixtral)\n- Embedding and retrieval models (e.g., E5-Mistral, GTE, ColBERT)\n- Reward and classification models (e.g., Qwen-Math)\n\nFind the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).\n\n## Getting Started\n\nInstall vLLM with [`uv`](https://docs.astral.sh/uv/) (recommended) or `pip`:\n\n```bash\nuv pip install vllm\n```\n\nOr [build from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source) for development.\n\nVisit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.\n\n- [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html)\n- [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html)\n- [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html)\n\n## Contributing\n\nWe welcome and value any contributions and collaborations.\nPlease check out [Contributing to vLLM](https://docs.vllm.ai/en/latest/contributing/index.html) for how to get involved.\n\n## Citation\n\nIf you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):\n\n```bibtex\n@inproceedings{kwon2023efficient,\n  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},\n  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},\n  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},\n  year={2023}\n}\n```\n\n## Contact Us\n\n<!-- --8<-- [start:contact-us] -->\n- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues)\n- For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)\n- For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)\n- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature\n- For collaborations and partnerships, please contact us at [collaboration@vllm.ai](mailto:collaboration@vllm.ai)\n<!-- --8<-- [end:contact-us] -->\n\n## Media Kit\n\n- If you wish to use vLLM's logo, please refer to [our media kit 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