Unverified50% confidenceFactExact time
Wide expert parallelism (wide-EP) means splitting experts across more GPUs (i.e., a large EP scale) so that each GPU carries fewer experts, which can reduce per-card memory pressure but amplifies the risk of load imbalance.
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50%
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Long-term
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10/17/2026
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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73% similarUnverifiedConsumer-grade GPUs are inferior to data center cards in memory bandwidth, NVLink interconnect capability, and long-term full-load stability, making them more suitable for inference deployment than large-scale distributed training.67% similarVerifiedThe MoE architecture uses multiple expert networks and a routing mechanism to selectively activate only a subset of experts during each forward pass, reducing computational overhead while maintaining large model capacity.66% similarUnverifiedTensor Parallelism horizontally shards the weight matrices of individual layers across multiple GPUs and aggregates results via AllReduce communication; it has lower latency but places extremely high demands on inter-GPU interconnect bandwidth (such as NVLink).66% similarUnverifiedCompared to traditional Dense architecture, the MoE architecture significantly reduces the computation per forward pass while maintaining the model's total knowledge capacity, but places extremely high demands on inter-GPU communication bandwidth.63% similar
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