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MoE架构中每个Token需通过All-to-All通讯路由到对应专家GPU，随集群规模增大通讯开销呈近似平方级增长

1

Sources

50%

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Long-term

Relevance

7/25/2026

First Seen

Sources

中国开源AI模型为何越打越强？三条逆袭路径深度拆解

Related Claims

UnverifiedMoE专家并行架构中不同专家分布在不同GPU上，路由决策需要跨设备的All-to-All通信，该通信开销在高并发时成为系统瓶颈84% similar UnverifiedMoE架构中所有专家的参数都需要加载到内存中，对显存和通信带宽要求更高73% similar UnverifiedRing-AllReduce算法的通信量与GPU数量无关，每个节点发送和接收的数据总量固定为数据大小的2(N-1)/N倍，理论上是带宽最优的AllReduce实现64% similar VerifiedMoE架构面临负载均衡问题和通信开销问题两大核心工程挑战61% similar Unverified专家并行会带来跨设备的All-to-All通信开销，在高并发时往往成为系统瓶颈60% similar

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