MoE — Mixture of Experts
Definition: A neural network architecture that activates only a subset of specialized "expert" sub-networks for each input — dramatically cutting compute while preserving capability.
Example
Mistral's Mixtral and many frontier models use MoE to run cheaper than dense models of the same size.
When you'll hear it
MoE shows up most often in AI strategy reviews, model evaluation discussions, and product roadmap meetings. When someone uses it, they're usually referring to mixture of experts — and they expect the room to already know what that means.
FAQs
What does MoE stand for?
MoE stands for Mixture of Experts.
What does MoE mean in AI and machine-learning?
A neural network architecture that activates only a subset of specialized "expert" sub-networks for each input — dramatically cutting compute while preserving capability.
Where will I hear MoE used at work?
MoE comes up most often in AI strategy reviews, model evaluation discussions, and product roadmap meetings. It's used as shorthand for mixture of experts, so people assume you already know the term.