Unverified50% confidenceFactExact time
MoE通过Top-K路由策略每次前向传播仅激活K个专家（通常为2-4个），实现稀疏激活以扩大模型容量而不线性增加计算量
1
Sources
50%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
Related Claims
UnverifiedThe MoE architecture uses a router to dynamically select the Top-K experts for computation for each token, allowing the total parameter count to scale while keeping the number of activated parameters per inference constant.80% similarVerifiedThe MoE architecture uses a gating network to dynamically select the K most relevant experts (typically Top-K, K=2) for each input token, activating only approximately 10%–30% of total parameters during inference.79% similarUnverifiedThe MoE architecture activates only the Top-K experts per inference, so the total parameter count far exceeds the number of parameters actually activated in a single inference.77% similarUnverifiedMoE uses a routing mechanism to let each input token dynamically select experts for processing, achieving an efficiency balance between a large total number of parameters and a small number of activated parameters.76% similarUnverifiedMoE uses a gating network to dynamically route each input token, activating only the most relevant few experts, thereby breaking the linear relationship between parameter scale and inference cost.75% similar
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