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Mixture of Experts (MoE)

A model architecture that routes each input to a small subset of specialized sub-networks ("experts") instead of activating the entire network, cutting compute per token.

What Is Mixture of Experts (MoE)?

A dense model runs every parameter on every token. An MoE model has many expert sub-networks and a router that activates only a few per token, so a model with a large total parameter count runs at the cost of a much smaller one. Most frontier 2026 models use MoE. The trade-off is added routing complexity and memory to hold all experts even though only some fire.

How Groovy Web Uses This

We factor MoE behavior into model selection and cost forecasting for clients, since the active-vs-total parameter gap changes both latency and the inference bill.

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