<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-dark.png">
<img alt="vLLM" src="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-light.png" width=55%>
</picture>
</p>
<h3 align="center">
Easy, fast, and cheap LLM serving for everyone
</h3>
<p align="center">
| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://discord.gg/jz7wjKhh6g"><b>Discord</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
</p>
---
*Latest News* 🔥
- [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing).
- [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
- [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/sessioncatalog?tab.day=20241001&search.sessiontracks=1719251906298001uzJ2) from other vLLM contributors and users!
- [2024/09] We hosted [the sixth vLLM meetup](https://lu.ma/87q3nvnh) with NVIDIA! Please find the meetup slides [here](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing).
- [2024/07] We hosted [the fifth vLLM meetup](https://lu.ma/lp0gyjqr) with AWS! Please find the meetup slides [here](https://docs.google.com/presentation/d/1RgUD8aCfcHocghoP3zmXzck9vX3RCI9yfUAB2Bbcl4Y/edit?usp=sharing).
- [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
- [2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
- [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) with IBM! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) with a16z! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
- [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
- [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
---
## About
vLLM is a fast and easy-to-use library for LLM inference and serving.
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
- Fast model execution with CUDA/HIP graph
- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8.
- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
- Speculative decoding
- Chunked prefill
**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script.
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 parallelism and pipeline parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
- Prefix caching support
- Multi-lora support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
- Transformer-like LLMs (e.g., Llama)
- Mixture-of-Expert LLMs (e.g., Mixtral)
- Embedding Models (e.g. E5-Mistral)
- Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).
## Getting Started
Install vLLM with `pip` or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
```bash
pip install vllm
```
Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to learn more.
- [Installation](https://vllm.readthedocs.io/en/latest/getting_started/installation.html)
- [Quickstart](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html)
- [Supported Models](https://vllm.readthedocs.io/en/latest/models/supported_models.html)
## Contributing
We welcome and value any contributions and collaborations.
Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
## Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
<!-- Note: Please sort them in alphabetical order. -->
<!-- Note: Please keep these consistent with docs/source/community/sponsors.md -->
- a16z
- AMD
- Anyscale
- AWS
- Crusoe Cloud
- Databricks
- DeepInfra
- Dropbox
- Google Cloud
- Lambda Lab
- NVIDIA
- Replicate
- Roblox
- RunPod
- Sequoia Capital
- Skywork AI
- Trainy
- UC Berkeley
- UC San Diego
- ZhenFund
We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.
## Citation
If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
```bibtex
@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 or discussions.
* For discussing with fellow users, please use Discord.
* For coordinating contributions and development, please use Slack.
* For security disclosures, please use Github's security advisory feature.
* For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
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"description": "<p align=\"center\">\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-dark.png\">\n <img alt=\"vLLM\" src=\"https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/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://vllm.ai\"><b>Blog</b></a> | <a href=\"https://arxiv.org/abs/2309.06180\"><b>Paper</b></a> | <a href=\"https://discord.gg/jz7wjKhh6g\"><b>Discord</b></a> | <a href=\"https://x.com/vllm_project\"><b>Twitter/X</b></a> | <a href=\"https://slack.vllm.ai\"><b>Developer Slack</b></a> |\n</p>\n\n---\n\n*Latest News* \ud83d\udd25\n- [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing).\n- [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!\n- [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/sessioncatalog?tab.day=20241001&search.sessiontracks=1719251906298001uzJ2) from other vLLM contributors and users!\n- [2024/09] We hosted [the sixth vLLM meetup](https://lu.ma/87q3nvnh) with NVIDIA! 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Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).\n- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) with IBM! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).\n- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) with a16z! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).\n- [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.\n- [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).\n\n---\n## About\nvLLM is a fast and easy-to-use library for LLM inference and serving.\n\nvLLM is fast with:\n\n- State-of-the-art serving throughput\n- Efficient management of attention key and value memory with **PagedAttention**\n- Continuous batching of incoming requests\n- Fast model execution with CUDA/HIP graph\n- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8.\n- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.\n- Speculative decoding\n- Chunked prefill\n\n**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script.\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 parallelism and pipeline parallelism support for distributed inference\n- Streaming outputs\n- OpenAI-compatible API server\n- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.\n- Prefix caching support\n- Multi-lora support\n\nvLLM seamlessly supports most popular open-source models on HuggingFace, including:\n- Transformer-like LLMs (e.g., Llama)\n- Mixture-of-Expert LLMs (e.g., Mixtral)\n- Embedding Models (e.g. E5-Mistral)\n- Multi-modal LLMs (e.g., LLaVA)\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 `pip` or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):\n\n```bash\npip install vllm\n```\n\nVisit our [documentation](https://vllm.readthedocs.io/en/latest/) to learn more.\n- [Installation](https://vllm.readthedocs.io/en/latest/getting_started/installation.html)\n- [Quickstart](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html)\n- [Supported Models](https://vllm.readthedocs.io/en/latest/models/supported_models.html)\n\n## Contributing\n\nWe welcome and value any contributions and collaborations.\nPlease check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.\n\n## Sponsors\n\nvLLM is a community project. 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