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A systematic framework converts mixture-of-experts models into dense architectures through expert scoring, selection, grouping, and knowledge distillation, achieving superior performance and efficiency compared to traditional pruning methods.
A discussion about the advantages of Mixture of Experts (MoE) models over dense models beyond speed, considering RAM constraints and scaling limits.
Discusses the trade-off between dense and Mixture-of-Experts (MoE) models for local AI, noting that high-RAM users have limited MoE options beyond Qwen 3.5 122B, and questioning if large GPU is the only viable path.