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--- *Latest News* 🔥 - [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM. - [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command! - [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click [example](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm) to start the vLLM demo, and the [blog post](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/) for the story behind vLLM development on the clouds. - [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai). --- 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 - Optimized CUDA kernels 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 support for distributed inference - Streaming outputs - OpenAI-compatible API server vLLM seamlessly supports many Hugging Face models, including the following architectures: - Aquila (`BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.) - Baichuan (`baichuan-inc/Baichuan-7B`, `baichuan-inc/Baichuan-13B-Chat`, etc.) - BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.) - Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.) - GPT-2 (`gpt2`, `gpt2-xl`, etc.) - GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.) - GPT-J (`EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc.) - GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.) - InternLM (`internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc.) - LLaMA & LLaMA-2 (`meta-llama/Llama-2-70b-hf`, `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.) - MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.) - OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.) - Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.) Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source): ```bash pip install vllm ``` ## Getting Started Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to get started. - [Installation](https://vllm.readthedocs.io/en/latest/getting_started/installation.html) - [Quickstart](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html) - [Supported Models](https://vllm.readthedocs.io/en/latest/models/supported_models.html) ## Performance vLLM outperforms Hugging Face Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput. For details, check out our [blog post](https://vllm.ai).
Serving throughput when each request asks for 1 output completion.
Serving throughput when each request asks for 3 output completions.