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However, under expert parallelism, MoE suffers from inference inefficiencies due to imbalanced token-to-expert assignment, where underloaded experts complete computations early but must wait for overloaded experts, leading to global delays. We define this phenomenon as the \\textbf{\\textit{Straggler Effect}}, as the most burdened experts dictate the overall inference latency. To address this, we first propose \\textit{\\textbf{Capacity-Aware Token Drop}}, which enforces expert capacity limits by discarding excess tokens from overloaded experts, effectively reducing load imbalance with minimal performance impact (e.g., $30\\%$ speedup with only $0.9\\%$ degradation on OLMoE). 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To address this, we first propose \\textit{\\textbf{Capacity-Aware Token Drop}}, which enforces expert capacity limits by discarding excess tokens from overloaded experts, effectively reducing load imbalance with minimal performance impact (e.g., $30\\%$ speedup with only $0.9\\%$ degradation on OLMoE). 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