混合專家模型 (Mixture of Experts, MoE) 技術詳解
Original: Mixture of Experts Explained
Mixture of Experts (MoE) has become a core technology for improving the performance and efficiency of today's large language models (LLMs)…
本指南深入解析混合專家模型（MoE）的核心技術。MoE 透過門控網路（Router）將輸入 token 分流至不同的專家網路（FFN），實現「高參數量、低計算量」的優勢。文中探討了 MoE 的歷史、訓練挑戰（如負載均衡與記憶體佔用），以及如何高效部署與微調此類模型。
Mixture of Experts (MoE) has become a core technology for improving the performance and efficiency of today's large language models (LLMs). Traditional "dense models" activate all parameters when processing every token, whereas MoE is a "sparse activation" architecture that can massively scale model parameter counts without significantly increasing computational cost (FLOPs).
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