PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition
Mixture-of-Experts (MoE) architectures scale Large Language Model (LLM) capacity efficiently by activating a sparse subset of experts per token. However, modern MoE inference remains heavily constrained by the rigid, whole-expert abstraction. Existing frameworks manage, schedule, or prune experts as atomic execution units, which fixes the optimization boundary too early and leaves fine-grained intra-expert computational redundancy underexplored. In this work, we present PCoMoE, a path-compositional execution framework that shifts MoE inference from coarse-grained expert selection to fine-grain
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- PossiblePossibly related (embedding) · 54%Qwen3.6-27B UD Q3 with kv at q8 is quite amazing for simple proof of concepts →
- PossiblePossibly related (embedding) · 53%Adaptive Mixture of Experts Gate (AMG) [R] →
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- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
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- LinkedLinked via arxiv author · 85%Ziyan Gan →
“PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition”