Release v0.2.5

Highlights

• We recently released a blog. Compared to TensorRT-LLM and vLLM, SGLang Runtime consistently delivers superior or competitive performance in both online and offline scenarios, handling models from Llama-8B to Llama-405B, and on A100 and H100 GPUs, using FP8 and FP16. SGLang consistently outperforms vLLM, achieving up to 3.1x higher throughput on Llama-70B. It also often matches or sometimes outperforms TensorRT-LLM.

• We have now automated the release processes for PyPI, Docker, and Release using GitHub workflows. Previously, because Release was not automated, GitHub Tags were not updated in time, leading to a jump from v0.2.0 directly to v0.2.5.

• Welcome everyone to try using https://github.com/sgl-project/sglang, and also welcome everyone to actively participate in the community, including but not limited to issues, PRs, and discussions. Cheers!