LLMModels & ArchitectureUpdated 2026.04.29

MoE

Mixture of Experts

Also known as전문가 혼합Mixture-of-ExpertsSparse MoE

In one line

MoE (Mixture of Experts) is an LLM architecture that activates only a subset of many smaller 'expert' networks per token — letting teams ship bigger models at roughly the same compute cost.

Going deeper

MoE puts dozens or hundreds of smaller 'expert' networks inside one model and uses a router to activate only a few of them per token. Total parameter count is enormous, but compute per call stays modest — so you get something close to a giant model's quality at a small model's price. Mixtral, DeepSeek and some GPT-4-family variants are well-known examples.

Marketers will not configure MoE themselves, but its second-order effect is real. Cheaper inference is pushing more products to embed LLMs, which means more AI surfaces where your brand may or may not show up. The pace of GEO surface expansion is partly an MoE story.

Worth knowing the trade-offs: MoE answers can be uneven if the router picks badly, and memory requirements are higher than the parameter math suggests. The 'same price, bigger model' headline has fine print.

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