ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs
Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs), yet fixed top-k routing activates the same number of expert slots for every token, causing substantial redundant computation. Existing expert-skipping methods often rely on router confidence, calibration data,...
Zu-Kang Xu, Zhi-Xiong Zhao, Xing Hu et al.
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