SGLang团队搞的FlashBoot，把大模型权重加载从20秒压到0.4秒，跨节点复制只要10毫秒。跑DeepSeek-V4系列，NVL72上实测数据，做弹性部署的值得看。
FlashBoot是SGLang上的权重加载子系统，针对MoE模型弹性部署中的延迟问题。它通过FabricArena连续内存布局和FlashLoad零拷贝批量传输，将单节点权重加载从20.1秒降至0.4秒，加速50倍。FlashClone利用远程映射替代NCCL初始化，将跨节点复制时间从10-110秒降至约10毫秒。在NVL72上测试DeepSeek-V4-Pro和DeepSeek-V4-Flash，并发机架级加载从87秒降至0.32秒，加速超270倍。
FlashBoot: Sub-Second Weight Loading for Large Models at Rack Scale
Flagship Mixture-of-Experts (MoE) models are growing fast along two axes at once: total parameter count and the number of experts. In elastic deployment scenarios, many GPUs across many nodes must become serving-ready quickly, and this growth makes weight loading a noticeable part of the latency budget. Even on NVIDIA's GB300 NVL72, today's state-of-the-art loaders leave most of that bandwidth unused. The losses are structural: (C1) weight memory is fragmented into tens of thousands of per-tensor objects, so transfers run far below link bandwidth; (C2) cross-node replication is gated by NCCL communicator setup, which costs 10-110 s before a single weight byte moves; and (C3) the existing cross-node GPU->GPU clone path is serial and scales poorly to concurrent multi-node bring-up. We present FlashBoot, a hardware-friendly, framework-workflow co-designed weight-loading subsystem built on SGLang. At its core is FabricArena, a contiguous, exportable and inter-node addressable tensor memory layout. On top of it, FlashLoad loads from CPU as a single bulk, zero-copy transfer, and FlashClone replicates a resident model from a remote GPU via a remote-mapping mechanism that removes NCCL setup. In experiments on NVL72 with DeepSeek-V4-Pro and DeepSeek-V4-Flash, FlashClone maps remote weight memory in ~10 ms (versus 10-110 s for NCCL) and sustains >=700 GB/s per clone. Against the state of the art, FlashBoot accelerates single-node weight loading by up to 50x (from 20.1 s to 0.4 s) and concurrent rack-level weight loading by >270x (from 87 s to 0.32 s). Our code will be made publicly available.