SGLang is yet another fast serving framework for large language models and vision language models.
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SGLang is a fast serving framework for large language models and vision language models. It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language. The core features include:
Learn more in our release blogs: v0.2 blog, v0.3 blog, v0.4 blog
The project is supported by (alphabetically): AMD, Baseten, Etched, Hyperbolic, Jam & Tea Studios, LinkedIn, Meituan, NVIDIA, RunPod, Stanford, UC Berkeley, xAI and 01.AI.
We learned from the design and reused code from the following projects: Guidance, vLLM, LightLLM, FlashInfer, Outlines, and LMQL. Please cite our paper, SGLang: Efficient Execution of Structured Language Model Programs, if you find the project useful.
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SGLang is yet another fast serving framework for large language models and vision language models.
We found that sglang demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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