> ## Content Index > Fetch the complete content index at: https://www.notatechguy.com/llms.txt > Use this file to discover other available public pages before exploring further. # SGLang LLM serving framework hits GitHub trending at 37K stars - URL: https://www.notatechguy.com/sglang-llm-serving-framework-hits-github-trending-at-37k-stars/ - Published: 2026-10-11T08:15:43.000Z - Updated: 2026-10-11T08:15:43.000Z - Description: SGLang, an open-source LLM serving framework from Stanford and UC Berkeley researchers, is trending on GitHub with 36,973 stars and broad hardware - Author: Marcello Babbili - Tags: Technology & AI, AI Models, Nvidia Stanford and UC Berkeley researchers' open-source inference framework SGLang landed on GitHub's daily trending page with 36,973 stars, adding 42 today [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com). The project claims to run on seven hardware platforms from NVIDIA to Apple Silicon, but every performance claim is self-reported and a further seven chip families are still listed as works in progress [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com). **My read:** This is the first serving framework I've seen that lists fourteen hardware platforms in its README, seven supported and seven incomplete. That breadth is a bet, not a fact. The performance claims against vLLM and TensorRT-LLM come from the maintainers' own release notes [P²](https://github.com/sgl-project/sglang/releases/tag/v0.2.0?ref=notatechguy.com), not from an independent benchmark, and the source material itself flags that the README is the maintainers' description, not a review [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com). I'd want third-party latency numbers on my own hardware before betting a production pipeline on it. ### Repository has grown roughly 28% since an earlier snapshot The project traces back to an arXiv preprint by Lianmin Zheng, Liangsheng Yin, and collaborators at Stanford, UC Berkeley, Shanghai Jiao Tong University, and Texas A&M [P³](https://arxiv.org/html/2312.07104v2?ref=notatechguy.com). The repository was created in January 2024 under the Apache 2.0 licence [P⁴](https://github.com/sgl-project/SGLang?ref=notatechguy.com). An earlier snapshot showed 28,935 stars [P⁴](https://github.com/sgl-project/SGLang?ref=notatechguy.com); the current reading of 36,973 [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com) marks roughly 28% growth since that snapshot. Maintainers describe SGLang as a framework for running large language, vision-language, and diffusion models, tuned for agentic workloads (multi-step tasks where AI agents call models repeatedly), large-scale serving, and reinforcement learning rollouts, the repeated inference runs used to train models through trial and error [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com). It includes a built-in image and video generation engine called SGLang Diffusion, packaged inside the main Python library [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com). The vision-language support arrives as the field works through how [multimodal AI models merge vision and text via two distinct pathways](https://www.notatechguy.com/multimodal-ai-models-merge-vision-and-text-via-two-distinct-pathways/). SGLang lists NVIDIA, AMD, Google TPU, Intel, Apple Silicon, Huawei Ascend, and Moore Threads as supported platforms [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com). A further seven, including AWS Trainium, Qualcomm QAIC, and Cambricon MLU, are marked as incomplete or experimental [S¹](https://github.com/sgl-project/sglang?ref=notatechguy.com). The v0.2.0 release from July 2024 reported that SGLang matched or exceeded TensorRT-LLM and vLLM across models from Llama-8B to Llama-405B on A100 GPUs [P²](https://github.com/sgl-project/sglang/releases/tag/v0.2.0?ref=notatechguy.com). Those claims are self-reported: no error bars, no released benchmark code, and no independent evaluation appear in the evidence pack — the figures are the maintainers' own. For teams running reinforcement learning workloads, the framework's RL rollout optimisation is a distinguishing feature. ### Open issue count signals where integration friction sits A platform engineer evaluating SGLang today would start with the hardware they already own. If that is NVIDIA A100 or H100, the v0.2.0 release notes [P²](https://github.com/sgl-project/sglang/releases/tag/v0.2.0?ref=notatechguy.com) suggest the framework has been tested most thoroughly there. If it is AWS Trainium or Qualcomm QAIC, the incomplete tag means the integration is not production-ready. For an ML platform engineer running Llama-70B on A100 GPUs, switching from vLLM to SGLang means reconfiguring inference endpoints and absorbing a framework with 3,539 open issues [P⁴](https://github.com/sgl-project/SGLang?ref=notatechguy.com) — a trade-off that lands directly on their on-call queue. The project's 3,539 open issues [P⁴](https://github.com/sgl-project/SGLang?ref=notatechguy.com) give a rough sense of where the friction sits. By comparison, HuggingFace's transformers library has 164,655 stars and 2,410 open issues [P⁵](https://github.com/Huggingface/transformers?ref=notatechguy.com). That lower ratio reflects its longer maturity since 2018\. SGLang is not yet three years old. ![GitHub stars: SGLang vs HuggingFace transformers](https://storage.ghost.io/c/6e/89/6e896869-22ef-4281-a213-b4c462c17cff/content/images/2026/10/chart_ba687e9adc48af37dc80.png) The next checkpoint is the release history on the SGLang GitHub page, where version tags and changelogs show whether the incomplete hardware integrations are advancing toward full support or stalling. --- *Sources: [S1 — sgl-project/sglang: SGLang is a high-performance serving framework for](https://github.com/sgl-project/sglang?ref=notatechguy.com) · [P2 — Release v0.2.0](https://github.com/sgl-project/sglang/releases/tag/v0.2.0?ref=notatechguy.com) · [P3 — SGLang: Efficient Execution of Structured Language Model Programs](https://arxiv.org/html/2312.07104v2?ref=notatechguy.com) · [P4 — sgl-project/sglang](https://github.com/sgl-project/SGLang?ref=notatechguy.com) · [P5 — huggingface/transformers](https://github.com/Huggingface/transformers?ref=notatechguy.com)* --- *Written from 5 sourced items, 4 of them primary.*