What is Mixture of Experts (MoE)?

Making a language model more capable usually means making it bigger, and a bigger model costs more to run on every request. Mixture of Experts breaks that link. It gives a model a large pool of specialized sub-networks but activates only a few of them for any given input, so the model can hold far more knowledge without paying the full compute bill each time.

How Mixture of Experts Works

An MoE model replaces some of its dense layers with a set of parallel sub-networks called experts. A small routing network sits in front of them and, for each token, decides which experts should handle it, usually picking one or two out of many. Only the chosen experts run, and their outputs are combined into the result. The rest stay idle for that token, which is what keeps the compute cost down.

Why Sparsity Matters

A traditional dense model uses every parameter for every input. MoE uses conditional computation instead: total capacity grows with the number of experts, while the cost per token grows only with the few that fire. This is why MoE model cards often show two numbers, total parameters and active parameters, with the active count far smaller. A model can carry hundreds of billions of parameters yet run at the speed of a much smaller one, and different experts tend to specialize along the way.

MoE vs Mixture of Agents (MoA)

The two are easy to confuse. Mixture of Experts lives inside a single model, routing tokens through sub-networks with a learned gate, and it is trained end to end. Mixture of Agents works at a higher level, coordinating several complete, independent models and combining their full responses through prompting. MoE is an architecture baked into one model. MoA is a system that orchestrates many models.

Trade-offs and Where It Shows Up

MoE powers many of today's largest and most efficient language models because it delivers more capability per unit of compute. The catch is memory and complexity. Every expert has to be loaded to serve the model even though only a few run per token, so the full model is large to host. Routing also has to stay balanced, or some experts get overworked while others go unused, which training has to actively correct. For teams, the payoff shows up as stronger models that stay affordable to serve at scale.