vllm-pascal: A Python repository from cduk

Easy, fast, and cheap LLM serving for everyone

vLLM, AMD, Anyscale Meet & Greet at Ray Summit 2024 (Monday, Sept 30th, 5-7pm PT) at Marriott Marquis San Francisco

We are excited to announce our special vLLM event in collaboration with AMD and Anyscale. Join us to learn more about recent advancements of vLLM on MI300X. Register here and be a part of the event!

Latest News 🔥

[2024/09] We hosted the sixth vLLM meetup with NVIDIA! Please find the meetup slides here.
[2024/07] We hosted the fifth vLLM meetup with AWS! Please find the meetup slides here.
[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.
[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 with IBM! Please find the meetup slides here.
[2023/10] We hosted the first vLLM meetup with a16z! Please find the meetup slides here.
[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.
[2023/06] 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
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 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 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.

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}
}

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.