vllm: A Python repository from rscohn2

Easy, fast, and cheap LLM serving for everyone

Ray Summit CPF is Open (June 4th to June 20th)!

There will be a track for vLLM at the Ray Summit (09/30-10/02, SF) this year! If you have cool projects related to vLLM or LLM inference, we would love to see your proposals. This will be a great chance for everyone in the community to get together and learn. Please submit your proposal here

Latest News 🔥

[2024/06] We hosted the fourth vLLM meetup with Cloudflare and BentoML! Please find the meetup slides here.
[2024/04] We hosted the third vLLM meetup with Roblox! Please find the meetup slides here.
[2024/01] We hosted the second vLLM meetup in SF! Please find the meetup slides here.
[2024/01] Added ROCm 6.0 support to vLLM.
[2023/12] Added ROCm 5.7 support to vLLM.
[2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
[2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
[2023/09] We released our PagedAttention paper on arXiv!
[2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
[2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
[2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
[2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog 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 with PagedAttention
Continuous batching of incoming requests
Fast model execution with CUDA/HIP graph
Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache
Optimized CUDA kernels

Performance benchmark: We include a performance benchmark that compares the performance of vllm against other LLM serving engines (TensorRT-LLM, text-generation-inference and lmdeploy).

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 pipieline parallelism support for distributed inference
Streaming outputs
OpenAI-compatible API server
Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs
(Experimental) Prefix caching support
(Experimental) 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)
Multi-modal LLMs (e.g., LLaVA)

Find the full list of supported models here.

Getting Started

Install vLLM with pip or from source:

pip install vllm

Visit our documentation to learn more.

Contributing

We welcome and value any contributions and collaborations. Please check out CONTRIBUTING.md 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}
}

rscohn2/vllm

Easy, fast, and cheap LLM serving for everyone

About

Getting Started

Contributing

Sponsors

Citation