Lianmin Zheng @lm_zheng
Member of technical staff @xAI | Prev: Ph.D. @UCBerkeley, Co-founder @lmsysorg lmzheng.net Bay Area, California Joined January 2018-
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🎉 Congrats to Meituan LongCat team on launching LongCat-Flash-Chat — a 560B MoE model now open-sourced! Powered by SGLang inference acceleration, it achieves high efficiency and strong benchmark results. Details in the blog 👉 lmsys.org/blog/2025-09-0…
🎉 Congrats to Meituan LongCat team on launching LongCat-Flash-Chat — a 560B MoE model now open-sourced! Powered by SGLang inference acceleration, it achieves high efficiency and strong benchmark results. Details in the blog 👉 lmsys.org/blog/2025-09-0…
🙌 Collaborated with the @lmsysorg (SGLang) team to integrate our new Model Optimizer gpt-oss fine-tuning recipe. This workflow trains in higher precision, then applies QAT to return to FP4 for optimized deployment on NVIDIA Blackwell — achieving improved alignment on the…
🙌 Collaborated with the @lmsysorg (SGLang) team to integrate our new Model Optimizer gpt-oss fine-tuning recipe. This workflow trains in higher precision, then applies QAT to return to FP4 for optimized deployment on NVIDIA Blackwell — achieving improved alignment on the…
👉 With great support from @AMD, SGLang now achieves PD Disaggregation on the MI300X — another major milestone! 🚀For more technical details, please read AMD's official blog.👇Cheers!
🚀 Introducing the first OSS example of fine-tuning gpt-oss with MXFP4 QAT! Powered by NVIDIA ModelOpt + SGLang. Highlights 1. Fine-tune gpt-oss while keeping the original MXFP4 format 2. Preserve FP4 efficiency and recover accuracy 3. Deploy seamlessly with SGLang! Full Blog👇
3 weeks ago, @OpenAI released gpt-oss, matching GPT-4o mini in capability. After Day-0 support, SGLang delivered major perf boosts: optimized attention, MoE, all-reduce fusion, quant, and Eagle-3. 🚀Up to 2.1× prefill and 2.25× decode throughput vs Day-0!👇
It’s a good model, sir
Grok 2 is now Open Source 🔥
Thank you SGLang team for the great meetup.
We open-source Grok-2 to everyone. This model feels like a baby to me. Glad it is going out to the world today. It also reminds me of the old days of studying posttraining recipes with my best friends. It’s still a good model for general-purpose use ;) huggingface.co/xai-org/grok-2
Reminder: SGLang x AMD SF Meetup is this Friday (Aug 22) at Shack15 🎉 Hands-on GPU workshop, talks from AMD/xAI/SGLang, food + networking . Don’t miss it! 📣 Seats are filling fast - if you haven’t registered yet, now’s the time. Bring a friend! 👉 Register here:…
Today was my last day at xAI, the company that I helped start with Elon Musk in 2023. I still remember the day I first met Elon, we talked for hours about AI and what the future might hold. We both felt that a new AI company with a different kind of mission was needed. Building…
Meet the newest member of the GLM-4.5 family — GLM-4.5V, open-sourced under the MIT license 🚀 github.com/zai-org/GLM-V Powered with full support from transformers @huggingface, sglang @lmsysorg, and vLLM @vllm_project 😀
Presenting the GLM-4.5 technical report!👇 arxiv.org/abs/2508.06471 This work demonstrates how we developed models that excel at reasoning, coding, and agentic tasks through a unique, multi-stage training paradigm. Key innovations include expert model iteration with…
⚡ SGLang Docker 0.5.0rc0 is officially released — now fully supporting GPT-OSS, including MXFP4, Function Calling, and the Response API, with complete compatibility for both Hopper and Blackwell. On B200, SGLang’s inference performance for OSS 120B with TP8 is 25% faster than…
Honored to see SGLang adopted in RL training for GLM-4.5 at @Zai_org — large-scale validation from a frontier AI lab pushing the boundaries of LLMs!
To me, diffusion LMs work because they remove unnecessary inductive biases. The left-to-right inductive bias is natural for human but is unlikely to be natural for AI. This gives more capacity to our models like Transformer having a bigger capacity than LSTM. Our experiment…
To me, diffusion LMs work because they remove unnecessary inductive biases. The left-to-right inductive bias is natural for human but is unlikely to be natural for AI. This gives more capacity to our models like Transformer having a bigger capacity than LSTM. Our experiment…
Answer: model is complete junk, it's a hallucination machine. Overfit to reasoning benchmarks and has absolutely zero recall ability
Answer: model is complete junk, it's a hallucination machine. Overfit to reasoning benchmarks and has absolutely zero recall ability
Like SGLang? Want speed of light decode perf? Checkout: github.com/sgl-project/sg…
🚀We are thrilled to announce that SGLang now supports OpenAI's latest open-weight model 'gpt-oss-120b', on both Hopper and Blackwell GPUs. Thanks to the collaborative efforts from @Eigen_AI_Labs , @nvidia , SGLang @lmsysorg and the OSS community! SGLang support landed within 4…
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