LMSYS Org(@lmsysorg):SGLang brings optimized inference to @nvidia Vera Rubin, with performance gains across attention, MoE, and speculative verification for Kimi K3 NVFP4. Miles by @radixark takes this further with end-to-end RL training, using SGLang for rollouts and the Vera CPU for concurrent agent sandboxes. The teams share their early results, benchmarks, and the engineering behind them 👇

2026.10.10 02:18

SGLang brings optimized inference to @nvidia Vera Rubin, with performance gains across attention, MoE, and speculative verification for Kimi K3 NVFP4. Miles by @radixark takes this further with end-to-end RL training, using SGLang for rollouts and the Vera CPU for concurrent agent sandboxes. The teams share their early results, benchmarks, and the engineering behind them 👇

SGLang@sgl_project

2026.10.09 23:23

We brought SGLang to NVIDIA Vera Rubin and accelerated Kimi K3 inference. Working closely with @NVIDIA, we optimized attention, MoE, and speculative verification kernels on early-access Rubin hardware. Highlights: • Up to 20% faster FP8 MLA at batch 1 / 128K context • 20% faster KDA verification, with bitwise-identical output • 5.9% end-to-end inference speedup from MoE tail fusion, removing 276 kernel launches per decode step SGLang also powers rollouts for Miles' end-to-end RL training on Rubin, including agentic RL with 64 concurrent sandboxes on the Vera CPU. Full results and engineering details 👉