Dense and Mixture-of-Experts (MoE) model architectures differ fundamentally in parameter activation: dense models activate all parameters for every token, while MoE models route each token through only a subset of expert networks. The choice between them depends on deployment constraints like throughput, memory cost, and serving complexity rather than raw parameter count alone.
An AI coding dictionary defining key concepts in language models and AI agents, covering model architecture (parameters, training, inference), token mechanics (input/output, caching), agent components (tools, context, harness), and interaction patterns (sessions, turns, tool calls).