89 lines
3.5 KiB
Markdown
89 lines
3.5 KiB
Markdown
<p align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/logos/vllm-logo-text-dark.png">
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<img alt="vLLM" src="./docs/source/assets/logos/vllm-logo-text-light.png" width=55%>
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</picture>
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</p>
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<h3 align="center">
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Easy, fast, and cheap LLM serving for everyone
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</h3>
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<p align="center">
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| <a href="https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/"><b>Documentation</b></a> | <a href=""><b>Blog</b></a> |
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</p>
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---
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*Latest News* 🔥
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- [2023/06] We officially released vLLM! vLLM has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid April. Check out our [blog post]().
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---
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vLLM is a fast and easy to use library for LLM inference and serving.
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vLLM is fast with:
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- State-of-the-art serving throughput
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- Efficient management of attention key and value memory with **PagedAttention**
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- Dynamic batching of incoming requests
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- Optimized CUDA kernels
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vLLM is flexible and easy to use with:
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- Seamless integration with popular HuggingFace models
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- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
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- Tensor parallelism support for distributed inference
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- Streaming outputs
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- OpenAI-compatible API server
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Install vLLM with pip or [from source](https://llm-serving-cacheflow.readthedocs-hosted.com/en/latest/getting_started/installation.html#build-from-source):
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```bash
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pip install vllm
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```
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## Getting Started
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Visit our [documentation](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/) to get started.
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- [Installation](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/getting_started/installation.html)
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- [Quickstart](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/getting_started/quickstart.html)
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- [Supported Models](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/models/supported_models.html)
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## Performance
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vLLM outperforms HuggingFace Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput.
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For details, check out our [blog post]().
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<p align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a10g_n1_dark.png">
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<img src="./docs/source/assets/figures/perf_a10g_n1_light.png" width="45%">
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</picture>
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a100_n1_dark.png">
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<img src="./docs/source/assets/figures/perf_a100_n1_light.png" width="45%">
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</picture>
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<br>
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<em> Serving throughput when each request asks for 1 output completion. </em>
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</p>
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<p align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a10g_n3_dark.png">
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<img src="./docs/source/assets/figures/perf_a10g_n3_light.png" width="45%">
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</picture>
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/figures/perf_a100_n3_dark.png">
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<img src="./docs/source/assets/figures/perf_a100_n3_light.png" width="45%">
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</picture> <br>
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<em> Serving throughput when each request asks for 3 output completions. </em>
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</p>
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## Contributing
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We welcome and value any contributions and collaborations.
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Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
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