Mixture of Experts is a neural network architecture that replaces some of a transformer's layers with many parallel subnetworks called experts, plus a small router that decides, for every token, which few experts should process it. Only the selected experts run, so while the model may contain hundreds of billions of parameters in total, each token activates only a fraction of them. Mixtral 8x7B, for example, holds 47 billion parameters but uses about 13 billion per token, routing each token to 2 of its 8 experts per layer.
The appeal is a better trade between capability and cost. Model quality tends to grow with parameter count, but in a dense model every parameter is computed for every token, so bigger means proportionally slower and more expensive. MoE breaks that link: capacity scales with the number of experts while per-token compute stays roughly flat. This is why many frontier models, including GPT-4 by wide reporting, DeepSeek's models, and Mixtral, use the technique. The costs are engineering ones: all experts must sit in memory even though few run at once, the router must be trained to balance load so experts neither starve nor overload, and serving infrastructure becomes more complex. The experts, despite the name, specialize in subtle statistical patterns rather than tidy human subjects.
For arosplatforms the relevance is mostly in model selection economics: MoE models often deliver top-tier quality at mid-tier inference prices, which changes the cost-per-token math in client platform decisions. When we help clients self-host open MoE models, we plan for the memory footprint and serving complexity that the architecture's cheap compute conceals.