Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
https://github.com/vllm-project/vllm
amd cuda gpt inference inferentia llama llm llm-serving llmops mlops model-serving pytorch rocm tpu trainium transformer xpu
Last synced: about 1 month ago
JSON representation
A high-throughput and memory-efficient inference and serving engine for LLMs
- Host: GitHub
- URL: https://github.com/vllm-project/vllm
- Owner: vllm-project
- License: apache-2.0
- Created: 2023-02-09T11:23:20.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-30T02:06:12.000Z (about 1 month ago)
- Last Synced: 2024-07-30T02:09:23.429Z (about 1 month ago)
- Topics: amd, cuda, gpt, inference, inferentia, llama, llm, llm-serving, llmops, mlops, model-serving, pytorch, rocm, tpu, trainium, transformer, xpu
- Language: Python
- Homepage: https://docs.vllm.ai
- Size: 22 MB
- Stars: 23,855
- Watchers: 219
- Forks: 3,423
- Open Issues: 1,551
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-local-llms - vllm - throughput and memory-efficient inference and serving engine for LLMs | 20,855 | 2,882 | 1,145 | 358 | 27 | Apache License 2.0 | 0 days, 9 hrs, 13 mins | (Open-Source Local LLM Projects)
- Awesome-LLM-Productization - vLLM - A high-throughput and memory-efficient inference and serving engine for LLMs (Models and Tools / LLM Deployment)
- awesome-local-ai - vLLM - vLLM is a fast and easy-to-use library for LLM inference and serving. | GGML/GGUF | Both | ❌ | Python | Text-Gen | (Inference Engine)
- awesome-genai - vllm-Efficient LLM Serving
- Awesome_Multimodel_LLM - vLLM - A high-throughput and memory-efficient inference and serving engine for LLMs (Tools for deploying LLM)
- awesome-llm-list - vLLM
- awesome-ml-python-packages - vLLM
- awesome-llmops - vllm - throughput and memory-efficient inference and serving engine for LLMs. |  | (Serving / Large Model Serving)
- awesome-llm-and-aigc - vllm-project/vllm - project/vllm?style=social"/> : A high-throughput and memory-efficient inference and serving engine for LLMs. [vllm.readthedocs.io](https://vllm.readthedocs.io/en/latest/) (Summary)
- awesome-repositories - vllm-project/vllm - A high-throughput and memory-efficient inference and serving engine for LLMs (Python)
- AiTreasureBox - vllm-project/vllm - 09-08_26082_2](https://img.shields.io/github/stars/vllm-project/vllm.svg)|A high-throughput and memory-efficient inference and serving engine for LLMs| (Repos)
- StarryDivineSky - vllm-project/vllm
- awesome-llm-projects - vLLM - throughput and memory-efficient inference and serving engine for LLMs. (Projects / 🤯 LLMs Inference and Serving)
- Awesome-LLM - vLLM - A high-throughput and memory-efficient inference and serving engine for LLMs. (LLM Deployment)
- Awesome-LLM-Compression - [Code
- awesome-cuda-and-hpc - vllm-project/vllm - project/vllm?style=social"/> : A high-throughput and memory-efficient inference and serving engine for LLMs. [vllm.readthedocs.io](https://vllm.readthedocs.io/en/latest/) (Frameworks)
- awesome-production-machine-learning - vLLM - project/vllm.svg?style=social) - vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. (Model Serving and Monitoring)
- awesome-LLM-resourses - vllm - throughput and memory-efficient inference and serving engine for LLMs. (推理 Inference)
README
Easy, fast, and cheap LLM serving for everyone
| Documentation | Blog | Paper | Discord |
---
**Ray Summit CPF is Open (June 4th to June 20th)!**
There will be a track for vLLM at the Ray Summit (09/30-10/02, SF) this year!
If you have cool projects related to vLLM or LLM inference, we would love to see your proposals.
This will be a great chance for everyone in the community to get together and learn.
Please submit your proposal [here](https://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/eventsite)
---
*Latest News* 🔥
- [2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
- [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
- [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
- [2024/01] Added ROCm 6.0 support to vLLM.
- [2023/12] Added ROCm 5.7 support to vLLM.
- [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing).
- [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
- [2023/09] We released our [PagedAttention paper](https://arxiv.org/abs/2309.06180) on arXiv!
- [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).
---
## About
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
- Fast model execution with CUDA/HIP graph
- Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [SqueezeLLM](https://arxiv.org/abs/2306.07629), FP8 KV Cache
- 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
- Support NVIDIA GPUs, AMD GPUs, Intel CPUs and GPUs
- (Experimental) Prefix caching support
- (Experimental) Multi-lora support
vLLM seamlessly supports most popular open-source models on HuggingFace, including:
- Transformer-like LLMs (e.g., Llama)
- Mixture-of-Expert LLMs (e.g., Mixtral)
- Multi-modal LLMs (e.g., LLaVA)
Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).
## Getting Started
Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
```bash
pip install vllm
```
Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to learn more.
- [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)
## Contributing
We welcome and value any contributions and collaborations.
Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
## Sponsors
vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
- a16z
- AMD
- Anyscale
- AWS
- Crusoe Cloud
- Databricks
- DeepInfra
- Dropbox
- Lambda Lab
- NVIDIA
- Replicate
- Roblox
- RunPod
- Sequoia Capital
- Trainy
- UC Berkeley
- UC San Diego
- ZhenFund
We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.
## Citation
If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
```bibtex
@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}
```