| Documentation | Blog |
--- *Latest News* 🔥 - [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](). --- 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** - Dynamic batching of incoming requests - Optimized CUDA kernels 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 support for distributed inference - Streaming outputs - OpenAI-compatible API server vLLM seamlessly supports many Huggingface models, including the following architectures: - GPT-2 (e.g., `gpt2`, `gpt2-xl`, etc.) - GPTNeoX (e.g., `EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.) - LLaMA (e.g., `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.) - OPT (e.g., `facebook/opt-66b`, `facebook/opt-iml-max-30b`, 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 HuggingFace 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]().
Serving throughput when each request asks for 1 output completion.
Serving throughput when each request asks for 3 output completions.