SGLang Overview - SGLang is an open-source, high-performance serving framework for large language models (LLMs) and large vision models (VLMs) [5] - SGLang supports day zero releases for new models from labs like Quen and DeepSeek, and has a strong open-source community [7] - The project has grown rapidly, from a research paper in December 2023 to nearly 15,000 GitHub stars in 18 months [9] Usage and Adoption - Base 10 uses SGLang as part of its inference stack for various models [8] - SGLang is also used by XAI for their Glock models, inference providers, cloud providers, research labs, universities, and product companies like Koser [8] Performance Optimization - SGLang's performance can be optimized using flags and configuration options, such as CUDA graph settings [20] - Eagle 3, a speculative decoding algorithm, can be used to improve performance by increasing the token acceptance rate [28][42][43] - The default CUDA graph max batch size on L4 GPUs is eight, but it can be adjusted to improve performance [31][36] Community and Contribution - The SGLang community is active and welcomes contributions [7][54] - Developers can get involved by starring the project on GitHub, filing issues, joining the Slack channel, and contributing to the codebase [9][54][55] - The codebase includes the SGLang runtime, a domain-specific front-end language, and a set of optimized kernels [58]

作者 | 笑渐不闻声渐悄 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1918033580103282744 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 >>点击进入→ 自动驾驶之心 『求职招聘』技术交流群 本文只做学术分享，如有侵权，联系删文 热乎出炉，刚面完Nvidia TRTLLM。本人bg是做llm推理加速的，主要在做speculative decoding，也 有一篇文章中了ICLR 2025。因为想继续做推理加速，所以尝试性的面了一下Nvidia，看能不能积累 connection。首先得吐槽一下这个面试机制：4位面试官一人面了我一个小时，整整连续面了4个小 时，面完感觉就是一个虚弱无力...然后简单聊一聊面试的问题 第一位面试官：自我介绍，讲一下自己的iclr 25关于spec的工作。面试官问的比较细致，从方法的 设置到evaluation都问到了，然后简单讲了一下自己nips 23的科研工作。感觉面试官对我的科研经 历还是比较满意，随后出了一道coding：n位数字插入任意数量的+，最后 ...