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In a Mixture-of-Experts (MoE) architecture, all expert sub-networks must be hosted on a single hardware device.
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Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Ch.1 Edge Serving Bottlenecks and Dynamics - Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Edge MoE Serving and Architectural Bottlenecks - Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Related
Experts as Modular FFNs in LLM MoE Models
A large language model is deployed for inference across 8 powerful processing units. In one configuration, the entire model's computational graph is activated across all 8 units for every input. In a second configuration, the model is structured with 8 distinct 'expert' sub-networks, one on each unit. For a given input, a routing mechanism selects only the 2 most relevant expert sub-networks to perform computations. What is the primary efficiency benefit of the second configuration for processin
In a Mixture-of-Experts (MoE) architecture, all expert sub-networks must be hosted on a single hardware device.
What effect does selective execution have on computational efficiency and model quality in MoE inference?
Based on MoE operational principles, which expert sub-networks are activated for computation on this request?
Sparse-Activation and Full-Expert-Pool Storage Mismatch in Edge MoE Serving