How the Mixture of Experts Architecture Works in AI Models

Mixture of Experts (MoE) is an AI architecture that divides a model into specialized subnetworks called experts, activating only a relevant subset for each input via a gating network. Key concepts include sparsity (activating only needed experts), top-k routing (selecting the best k experts per token), and noisy top-k gating to solve load balancing issues. A concrete walkthrough shows how a prompt is routed to specialized experts per layer. The Mixtral model is highlighted as a real-world example, using 8 experts per layer with 7B parameters each, activating only 2 per token — delivering high capability at lower compute cost.