UltraEP: Near-Optimal Load Balancing for Rack-Scale MoE Training
jiqizhixin · x · 2026-08-09
Researchers from Peking University, Xiaohongshu, and Shanghai AI Lab introduced UltraEP, a new framework to solve GPU synchronization and bottleneck issues during massive Mixture of Experts (MoE) training.
- Core Mechanism: It acts as a real-time load balancer that continuously redistributes work across GPUs at the rack-scale, rebalancing every microbatch and layer to prevent stragglers.
- Performance: Achieves 94.3% of ideal throughput with minimal overhead, delivering a 1.49x speedup over baseline and reducing severe load imbalance (up to 4x) to near-perfect symmetry.
- Scale: Validated on cutting-edge MoE models ranging from 106B to 671B parameters across up to 256 GPUs.
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