How to Score Experts for One-Shot MoE Expert Pruning: A Unified Formulation and Selection Principle
arXiv:2606.15716v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models reduce per-token computation through sparse expert activation, yet deployment still requires storing the full expert pool, making one-shot expert pruning a practical approach for reducing memory usage. Although effective, existing criteria are largely heuristic, and no single criterion is universally optimal. Thus, establishing a principle for selecting pruning criteria suited to different deployment objectives remains an important yet largely underexplored problem in one-shot expert pruning. To this end,
The proliferation of increasingly large Mixture-of-Experts (MoE) models necessitates more efficient deployment strategies, making memory optimization a critical and timely research area.
Efficient expert pruning directly addresses the computational and memory bottlenecks of advanced AI models, impacting their practical scalability and accessibility for various applications.
New methodologies for MoE expert pruning could lead to significantly smaller, more efficient models without substantial performance degradation, expanding their deployability across diverse hardware and use cases.
- · AI developers
- · Cloud providers
- · Edge AI manufacturers
- · Companies using large language models
- · Legacy AI infrastructure providers
- · Anyone relying solely on dense model architectures
More memory-efficient MoE models become feasible for deployment on constrained hardware.
Increased adoption of large, specialized AI models across a wider range of industries due to reduced operational costs.
Democratization of advanced AI capabilities, potentially leading to new applications and services that were previously economically or technically unviable.
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Read at arXiv cs.LG