SGLang, led by RadixArk's Banghua Zhu, adds Day-0 support for Poolside's 225B Laguna M.1 model

Check out our cookbook: https://docs.sglang.io/cookbook/autoregressive/Poolside/Laguna-M.1

🎉 Day-0 support for Laguna M.1 from @poolsideai is live in SGLang! This is a 225B MoE built for agentic coding & long-horizon work. 1️⃣ 70-layer MoE: 3 dense SwiGLU layers + 67 sparse MoE layers, 256 experts, top-k=16 with aux-loss-free load balancing 2️⃣ Global attention across all layers: 64 Q-heads, 8 KV-heads, softplus output gating 3️⃣ Native interleaved reasoning: thinking between tool calls, toggleable per-request 4️⃣ Strong on SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro & Terminal-Bench 2.0.

Run it now with SGLang!