87 lines
2.6 KiB
ReStructuredText
87 lines
2.6 KiB
ReStructuredText
Welcome to vLLM!
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================
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.. figure:: ./assets/logos/vllm-logo-text-light.png
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:width: 60%
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:align: center
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:alt: vLLM
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:class: no-scaled-link
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.. raw:: html
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<p style="text-align:center">
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<strong>Easy, fast, and cheap LLM serving for everyone
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</strong>
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</p>
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<p style="text-align:center">
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<script async defer src="https://buttons.github.io/buttons.js"></script>
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<a class="github-button" href="https://github.com/vllm-project/vllm" data-show-count="true" data-size="large" aria-label="Star">Star</a>
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<a class="github-button" href="https://github.com/vllm-project/vllm/subscription" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
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<a class="github-button" href="https://github.com/vllm-project/vllm/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
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</p>
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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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* Continuous batching of incoming requests
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* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_
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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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* Support NVIDIA GPUs and AMD GPUs.
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For more information, check out the following:
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* `vLLM announcing blog post <https://vllm.ai>`_ (intro to PagedAttention)
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* `vLLM paper <https://arxiv.org/abs/2309.06180>`_ (SOSP 2023)
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* `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.
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Documentation
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-------------
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.. toctree::
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:maxdepth: 1
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:caption: Getting Started
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getting_started/installation
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getting_started/amd-installation
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getting_started/quickstart
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.. toctree::
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:maxdepth: 1
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:caption: Serving
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serving/distributed_serving
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serving/run_on_sky
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serving/deploying_with_triton
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serving/deploying_with_docker
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serving/serving_with_langchain
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serving/metrics
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.. toctree::
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:maxdepth: 1
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:caption: Models
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models/supported_models
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models/adding_model
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models/engine_args
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.. toctree::
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:maxdepth: 1
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:caption: Quantization
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quantization/auto_awq |