SGLang Optimized for Nvidia Rubin

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 👉 https://t.co/xGwlR0ax4L