55 lines
2.4 KiB
Markdown
55 lines
2.4 KiB
Markdown
# vLLM: Easy, Fast, and Cheap LLM Serving for Everyone
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| [**Documentation**](https://llm-serving-cacheflow.readthedocs-hosted.com/_/sharing/Cyo52MQgyoAWRQ79XA4iA2k8euwzzmjY?next=/en/latest/) | [**Blog**]() |
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vLLM is a fast and easy-to-use library for LLM inference and serving.
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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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## 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): `pip install vllm`
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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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## Key Features
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vLLM comes with many powerful features that include:
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- State-of-the-art performance in serving throughput
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- Efficient management of attention key and value memory with **PagedAttention**
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- Seamless integration with popular HuggingFace models
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- Dynamic batching of incoming requests
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- Optimized CUDA kernels
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- High-throughput serving with various decoding algorithms, including *parallel sampling* and *beam search*
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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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## 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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<img src="./assets/figures/perf_a10g_n1.png" width="45%">
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<img src="./assets/figures/perf_a100_n1.png" width="45%">
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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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<img src="./assets/figures/perf_a10g_n3.png" width="45%">
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<img src="./assets/figures/perf_a100_n3.png" width="45%">
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<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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