Ying Sheng
SGLang co-author and codeowner; first author of FlexGen, for running large models on a single GPU
Ying Sheng works on systems for serving and running large language models. Sheng is first author of FlexGen, a 2023 system that runs large language models with limited GPU memory by spreading weights and cache across GPU, CPU memory, and disk and compressing them to 4 bits. Sheng is an author of the SGLang paper and a codeowner of the SGLang repository, whose maintenance guide names Sheng and Lianmin Zheng as the people who start nominations for new maintainers. Sheng is also an officer of LMSYS, the nonprofit that hosts SGLang.1764523
Work
SGLang
Listed as an author of the SGLang paper (arXiv 2312.07104) and as a codeowner for core areas of the repository, including the scheduler managers, memory cache, model executor, LoRA, and speculative decoding code.641
FlexGen (FlexLLMGen) (external site: github.com)
First author of the FlexGen paper on high-throughput inference of large language models with a single GPU. The project repository, now archived, describes running large models on a single GPU for throughput-oriented work.78
Earlier roles
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