Unverified50% confidenceFactExact time
Traditional weight synchronization approaches include writing weights to disk and reloading them (high IO overhead, slow switching) or relying on collective communication libraries such as NCCL for direct GPU-to-GPU transfer (high requirements on topology and deployment environment).
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50%
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Long-term
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10/18/2026
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UnverifiedNCCL provides high-speed GPU-to-GPU communication primitives such as all-reduce and broadcast, and is the de facto standard for gradient synchronization in distributed training; using it for direct weight transfer requires both communicating parties to be within the same NCCL communication domain, which imposes strong constraints on network topology and launch methods and is difficult to maintain across clusters or when the inference service scales dynamically.73% similarUnverifiedRack-scale systems keep cross-card communication at low latency through unified high-speed interconnects (such as NVLink) and shared memory pools, thereby supporting real-time inference of large models.69% similarUnverified将通信控制权下放到GPU可以让集合通信等操作减少对CPU的依赖，在扩展到数千甚至上万GPU时保持更好的线性度67% similar
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