Dense2MoE unifies pruning of attention modules with upcycling of MLPs into MoE experts to produce on-device LLMs that improve the latency-accuracy Pareto frontier.
Hardware co-design scaling laws via roofline modelling for on-device llms.arXiv preprint arXiv:2602.10377,
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Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling
Dense2MoE unifies pruning of attention modules with upcycling of MLPs into MoE experts to produce on-device LLMs that improve the latency-accuracy Pareto frontier.