Mixture of Experts (MoE) is a neural network design that splits a model into specialized subnetworks called 'experts.' For each input, a router picks only a few relevant experts to activate, saving compute while keeping the model large and capable.
Why it matters
Large AI models get better as they scale, but running every parameter for every request gets expensive fast. MoE lets companies build enormous models while only activating a fraction of parameters per query, similar to how a company routes a customer question to the right specialist instead of looping in the whole staff. This is part of why some frontier large language models can be both bigger and cheaper to run than older dense architectures.
How it actually works
An MoE layer contains many expert networks, each trained to handle different types of patterns in data. A small 'router' network looks at each input and decides which one or two experts should process it, then combines their outputs. The rest of the experts stay idle, which is what keeps inference costs down despite the model's total size.
Where it shows up
MoE has been used in several major foundation models to boost capability without proportionally increasing serving costs. For operators, the practical takeaway is that a model's total parameter count no longer tells you much about how expensive it is to run — architecture matters as much as scale.
Frequently asked
Is Mixture of Experts the same as a bigger model?
No. MoE models can have a large total parameter count but only use a small portion of those parameters for any single request, unlike dense models that use all parameters every time.
Does MoE make AI models cheaper to run?
Often yes for inference, since only a subset of experts activate per query, though training and memory requirements can still be significant.
Why don't all AI models use Mixture of Experts?
MoE adds complexity in training, routing, and infrastructure, so some teams prefer simpler dense models when cost savings don't outweigh that added complexity.