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The MoE architecture splits the FFN layer into N experts, and each token is routed through a gating network to only Top-K experts; because routing is dynamic, all experts may be activated during batch inference, so all expert weights must reside in VRAM.
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10/14/2026
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UnverifiedThe Mixture-of-Experts (MoE) architecture is a sparsely activated design that splits a single large model into multiple expert sub-networks, where each expert is usually a set of independent feed-forward layer (FFN) weights, and a lightweight router network decides which few experts to assign to based on input token features.79% similarUnverifiedMoE架构通过门控路由器为每个输入token动态选择激活的专家，从而将知识容量与推理成本解耦76% similarUnverified智能路由架构在学术上与混合专家系统（Mixture of Experts, MoE）的门控机制有相似之处，通过门控网络决定激活哪些专家模块处理输入74% similarUnverifiedThe Mixture of Experts architecture dynamically selects the most suitable combination of experts for the current input through a gating network (learned router), distributing computation only to the small number of top-scoring experts.74% similarUnverifiedIn the MoE architecture, the weights of all experts must reside in VRAM at all times, so computation is fast but VRAM usage does not decrease proportionally.74% similar
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