Dense vs. MoE Models: Navigating Architecture and Parameter Efficiency

The primary distinction between dense and Mixture-of-Experts (MoE) models lies in how they utilize their parameters during operation. [1] In a dense model, every parameter participates in every forward pass, routing tokens through a single shared feed-forward network block per decoder layer. [1] For example, all 27 billion parameters of a 27 billion parameter dense model will fire for every single token processed. [1] Conversely, an MoE architecture replaces the single shared block with multiple feed-forward networks, storing various expert networks and routing each token through only a selected subset. [1] The Nemotron 3.5 Lightning model illustrates this mechanism by activating only 3 billion parameters per token despite possessing a total capacity of 30 billion parameters. [1]

Choosing between dense and MoE architectures comes down to balancing deployment predictability against potential throughput. Dense models remain preferable for environments requiring straightforward, predictable deployment with limited memory overhead. However, models like Nemotron 3.5 Lightning show how MoE designs can unlock greater capacity without ballooning compute requirements for individual tokens, provided the infrastructure can handle the serving complexity. This trade-off emphasizes that how a model organizes its parameters affects performance as much as the raw parameter count itself. What the sources don't address: How the specific gating algorithms determine which tokens are sent to which expert networks during real-time inference.