Mixture-of-Experts and Routing Behavior Mixture-of-experts architectures are a direct response to a persistent constraint in modern AI: dense models get better when they get bigger, but bigger models are expensive to train and expensive to serve. MoE systems aim to increase model capacity without paying the full compute cost on every token. They do this […]

Model Ensembles and Arbitration Layers A single model is rarely the best answer to a product problem. It can be the simplest answer, and sometimes simplicity is the right constraint. But when a system must be both capable and dependable under real-world conditions, “one model does everything” becomes expensive and fragile. Ensembles and arbitration layers […]

Sparse vs Dense Compute Architectures Dense and sparse compute are two different answers to the same pressure: modern AI wants more capability than the average production budget wants to pay for on every token. Dense architectures spend roughly the same amount of compute on every input. Sparse architectures try to spend compute selectively, activating only […]