Easy, fast, and cheap LLM serving for everyone

|Documentation|Blog|Paper|Discord|Twitter/X|Developer Slack|

Latest News🔥

• [2024/10] We have just created a developer slack (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 teamhere.Learn more from thetalksfrom other vLLM contributors and users!
• [2024/09] We hostedthe sixth vLLM meetupwith NVIDIA! Please find the meetup slideshere.
• [2024/07] We hostedthe fifth vLLM meetupwith AWS! Please find the meetup slideshere.
• [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog posthere.
• [2024/06] We hostedthe fourth vLLM meetupwith Cloudflare and BentoML! Please find the meetup slideshere.
• [2024/04] We hostedthe third vLLM meetupwith Roblox! Please find the meetup slideshere.
• [2024/01] We hostedthe second vLLM meetupwith IBM! Please find the meetup slideshere.
• [2023/10] We hostedthe first vLLM meetupwith a16z! Please find the meetup slideshere.
• [2023/08] We would like to express our sincere gratitude toAndreessen Horowitz(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 poweredLMSYS Vicuna and Chatbot Arenasince mid-April. Check out ourblog post.

---

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 withPagedAttention
• Continuous batching of incoming requests
• Fast model execution with CUDA/HIP graph
• Quantizations:GPTQ,AWQ,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 ofour blog post.It compares the performance of vLLM against other LLM serving engines (TensorRT-LLM,SGLangandLMDeploy). The implementation is undernightly-benchmarks folderand you canreproducethis 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, includingparallel 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 modelshere.

Getting Started

Install vLLM withpiporfrom source:

pip install vllm

Visit ourdocumentationto learn more.

• Installation
• Quickstart
• Supported Models

Contributing

We welcome and value any contributions and collaborations. Please check outCONTRIBUTING.mdfor 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!

• 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 throughOpenCollective.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 ourpaper:

@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 security disclosures, please use Github's security advisory feature.
• For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.