MoE Architecture Explained: Router Picks Expert Networks Per Token to Save Compute
HarperSCarroll · x · 2026-10-27
An explainer of the Mixture of Experts (MoE) architecture, the foundation behind most modern AI models:
- A huge model doesn't need to activate all parameters for every input. A router selects which expert networks to activate for each token, instead of running the entire model, saving compute and processing time.
- Total parameter count can be enormous, but only a fraction is active at any moment: the selected experts' parameters plus shared parameters used for every input.
- This gives MoE models the learning capacity of much larger models while only using what each input needs.
The post ties this to Meta's newly released open-source Muse Spark Open model (claim unverified), noting MoE underpins nearly every recent model release.
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