Mixture-of-Experts (MoE) for Efficient Inference
Mixture-of-Experts (MoE) models exemplify an efficient architecture applicable to LLM inference. In this approach, different 'expert' sub-networks are placed on separate devices, and only the experts relevant to a given input are activated for computation. This selective execution significantly boosts computational efficiency without sacrificing model quality.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Prep Sessions
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
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Mixture-of-Experts (MoE) for Efficient Inference
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Mixture-of-Experts (MoE) for Efficient Inference
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Learn After
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