This chapter explains how Mixture of Experts (MoE) makes large models faster and potentially more accurate.
The idea is to activate only a small part of the model for any given input.
You treat the neural network as a graph and activate a subgraph based on the input query.
For example, a query about shopping might trigger a different part of the model than a query about emotions.
Only a few "experts" or parts of the model are active at any time.
This reduces compute cost while still leveraging a very large overall model.
The takeaway is that MoE improves efficiency by routing inputs through specialized parts of the model rather than using the full network for every request.