A mixture of experts model contains several neural network sections called experts. A router chooses a small number of them for each input. This lets the model hold many learned parameters without using every parameter for every calculation.
People often shorten mixture of experts to MoE. The “experts” do not map neatly to human professions. One expert may respond more to code or grammar, but training does not guarantee a clear label for each section.
The router sends work down selected paths
In a language model, each token reaches the router at certain layers. The router gives the token a score for every expert. A common setup selects the top one or two. Those experts process the token, and their outputs return to the main model path.
Another token in the same sentence can use different experts. The choice depends on the token’s current representation, not just the visible word. The rest of the transformer still supplies shared attention and context.
Parameter count and active work differ
A dense model uses all of its layers for each token. An MoE model may store far more total parameters while activating only a fraction. That design can add capacity without increasing every calculation by the same amount.
The full weights still need storage. Moving experts between chips also takes time. A large total parameter count does not mean the model runs on a small device. Published model sizes should separate total parameters from active parameters.
Training needs balanced traffic
A router may favor a few experts and ignore the rest. The popular experts then receive too many tokens, while unused sections learn little. Training systems add balancing signals that encourage a healthier spread.
Hardware creates another constraint. One expert may sit on a different processor. Tokens must travel across a fast connection to reach it. Uneven routing can leave one chip overloaded and another idle. Engineers care about network speed and capacity limits as much as model math.
Specialization grows without a job chart
Experts can learn different patterns because they see different streams of tokens. Researchers may find broad tendencies after training. A section could react to numbers, another language, or source code. Those tendencies overlap and can change by layer.
A router can make a bad choice. Rare input may reach experts with weak experience. Routing errors can also make output less stable across small wording changes. Evaluation needs to cover languages and subjects that appeared less often in training.
Mixture of experts describes a way to allocate model capacity. It does not create a committee that checks facts. The final output can still contain bias, unsafe material, or hallucinations. The design mainly changes how the neural network uses computation.