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README.md
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@ -16,6 +16,7 @@ Easy, fast, and cheap LLM serving for everyone
---
*Latest News* 🔥
- [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
- [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
- [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
@ -33,7 +34,9 @@ Easy, fast, and cheap LLM serving for everyone
- [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.
Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
@ -127,6 +130,7 @@ We also have an official fundraising venue through [OpenCollective](https://open
## 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},
@ -138,11 +142,11 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
## Contact Us
* For technical questions and feature requests, please use Github issues or discussions.
* For discussing with fellow users and coordinating contributions and development, please use Slack.
* For security disclosures, please use Github's security advisory feature.
* For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
- For technical questions and feature requests, please use Github issues or discussions.
- For discussing with fellow users and coordinating contributions and development, please use Slack.
- For security disclosures, please use Github's security advisory feature.
- For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
## Media Kit
* If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit).
- If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit).