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cs.DC2026
UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing
Xinming Wei, Chao Jin, Tuo Dai +10
Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load imbalance into compute straggle…
cs.DC2026
PROBE: Co-Balancing Computation and Communication in MoE Inference via Real-Time Predictive Prefetching
Qianchao Zhu, Xucheng Ye, Yuliang Liu +2
Mixture-of-Experts models have become a dominant architecture for scaling Large Language Models by activating only a sparse subset of experts per token. However, latency-critical M…