mirror of https://github.com/vllm-project/vllm.git
51 lines
2.5 KiB
Markdown
51 lines
2.5 KiB
Markdown
# Welcome to vLLM
|
|
|
|
<figure markdown="span">
|
|
{ align="center" alt="vLLM" class="no-scaled-link" width="60%" }
|
|
</figure>
|
|
|
|
<p style="text-align:center">
|
|
<strong>Easy, fast, and cheap LLM serving for everyone
|
|
</strong>
|
|
</p>
|
|
|
|
<p style="text-align:center">
|
|
<script async defer src="https://buttons.github.io/buttons.js"></script>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm" data-show-count="true" data-size="large" aria-label="Star">Star</a>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm/subscription" data-show-count="true" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
|
|
<a class="github-button" href="https://github.com/vllm-project/vllm/fork" data-show-count="true" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
|
|
</p>
|
|
|
|
vLLM is a fast and easy-to-use library for LLM inference and serving.
|
|
|
|
Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) 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**](https://blog.vllm.ai/2023/06/20/vllm.html)
|
|
- Continuous batching of incoming requests
|
|
- Fast model execution with CUDA/HIP graph
|
|
- Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8
|
|
- 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 HuggingFace 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, Gaudi® accelerators and GPUs, IBM Power CPUs, TPU, and AWS Trainium and Inferentia Accelerators.
|
|
- Prefix caching support
|
|
- Multi-lora support
|
|
|
|
For more information, check out the following:
|
|
|
|
- [vLLM announcing blog post](https://vllm.ai) (intro to PagedAttention)
|
|
- [vLLM paper](https://arxiv.org/abs/2309.06180) (SOSP 2023)
|
|
- [How continuous batching enables 23x throughput in LLM inference while reducing p50 latency](https://www.anyscale.com/blog/continuous-batching-llm-inference) by Cade Daniel et al.
|
|
- [vLLM Meetups][meetups]
|