arXiv:2605.29350v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intensive. Existing post-training compression methods mainly shrink this cost by pruning experts or merging their weights. We formulate post-training MoE compression as expert-pool consolidation: retaining a smaller set of pretrained experts as reusable prototypes and deterministically remapping each original expert reference to one selected prototype. This view separates the reduced expert pool from th
The increasing scale of MoE models necessitates innovative compression techniques to make them more deployable and less memory-intensive, addressing immediate deployment constraints.
This research addresses a critical bottleneck in the real-world deployment of large AI models by making them more memory-efficient, broadening their applicability and reducing operational costs.
The proposed ConMoE method changes how MoE models are compressed by focusing on expert-pool consolidation through prototype reassignment, offering a novel approach beyond current pruning or merging techniques.
- · Cloud AI providers
- · Enterprises deploying large language models
- · Edge AI computing
- · AI hardware manufacturers
- · Companies with inefficient MoE model deployment strategies
- · Legacy AI infrastructure providers
MoE models become more affordable and practical to deploy in diverse environments.
Increased adoption of MoE architectures across various AI applications due to reduced resource requirements.
Democratization of sophisticated AI capabilities, potentially leading to new business models and services that were previously cost-prohibitive.
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Read at arXiv cs.AI