Mixture of Experts
An AI architecture where multiple specialized neural networks handle different types of tasks, improving efficiency and performance.
Definition
Mixture of Experts (MoE) is an AI architecture that uses multiple specialized sub-networks (experts), with a gating mechanism that routes each input to the most relevant experts. Only a fraction of the network activates for any given task.
This approach allows models to scale to massive sizes while keeping computational costs manageable. GPT-4 and other leading models use MoE architectures to achieve broad capabilities without proportional increases in processing requirements.
Why It Matters
MoE architectures represent the future of efficient AI scaling. For businesses, this means access to more powerful AI capabilities without proportional cost increases.
Understanding MoE helps organizations evaluate AI solutions intelligently—models using this architecture often deliver better performance per dollar spent, making enterprise AI adoption more economically viable.
Examples in Practice
A large language model uses MoE to handle diverse tasks: coding experts activate for programming questions, while writing experts handle creative content—all within one model that runs efficiently.
An enterprise selects an MoE-based AI platform because it provides specialized capabilities across legal, technical, and creative domains without requiring separate models for each use case.