Easy, fast, and cheap LLM serving for everyone

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 at the end of our blog post. It compares the performance of vLLM against other LLM serving engines (TensorRT-LLM, SGLang and LMDeploy). The implementation is under nightly-benchmarks folder and you can reproduce 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.

Getting Started

Install vLLM with pip or from source:

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:

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.