How does a Mixture-of-Experts (MoE) architecture allow the computation of a frontier-scale model to fit within consumer GPU memory on an edge device?

What constitutes the central systems challenge of serving Mixture-of-Experts (MoE) models on edge devices?

What is the impact of architectural sparsity on the memory required to store an MoE model's full expert pool?

Where do inactive experts reside when the complete parameter footprint of an MoE model exceeds the memory of an edge device's GPU?

Match each MoE serving parameter or edge concept to its corresponding description.

Identify the primary systems bottleneck causing latency degradation in this scenario, and explain why expanding the total expert pool ($E$) while keeping the number of active experts ($k$) constant worsens this bottleneck.

In an edge MoE deployment where the full model footprint exceeds GPU capacity, by what mechanism do inactive experts participate in token processing?

In edge Mixture-of-Experts (MoE) serving, consumer GPU memory only needs to accommodate the active compute footprint for a given token rather than the entire parameter footprint simultaneously during execution.

Which two operational metrics are drastically reduced when an MoE architecture routes each token to a sparse subset of experts ($k \ll E$)?