Introduction to LLM serving with SGLang - Philip Kiely and Yineng Zhang, Baseten
Jul 26, 2025 · 43:42
Philip Kiely and Yineng Zhang introduce SGLang, an open-source fast serving framework for LLMs and VLMs, arguing it offers production-ready performance with day-zero support for new models like DeepSeek and Qwen, and a customizable codebase for contributions. Yineng, a core maintainer, traces SGLang's rapid growth from a December 2023 paper to nearly 15,000 GitHub stars and adoption by xAI, AMD, and Meituan. The workshop demonstrates deploying a first model via Baseten's trunk packaging, then tuning the CUDA Graph max batch size flag on an L4 GPU to maintain CUDA Graph-enabled decoding during higher concurrency, boosting generation throughput. They also cover Eagle 3 speculative decoding, where a draft model derived from the target model speculates tokens; users can benchmark different step and top-k configurations on representative prompts to find optimal settings for production. Finally, they invite contributions via GitHub's 'good first issue' tags and highlight Baseten's job openings.
