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vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
vLLM is fast with:
State-of-the-art serving throughput
Efficient management of attention key and value memory with PagedAttention
Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
Speculative decoding
Chunked prefill
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, pipeline, data and expert parallelism support for distributed inference
Streaming outputs
OpenAI-compatible API server
Support for NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, Arm CPUs, and TPU. Additionally, support for diverse hardware plugins such as Intel Gaudi, IBM Spyre and Huawei Ascend.
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, Deepseek-V2 and V3)
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}
}
