Unverified50% confidenceTradeoffExact time
MoE在推理时仍需将全部专家权重载入内存，内存占用并未因稀疏计算而减少
1
Sources
50%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
PrismML突破：270亿参数大模型如何跑进iPhone？
Related Claims
UnverifiedAll expert parameters of an MoE model need to reside in memory (even when not activated), which is also why ternary quantization is particularly important for MoE.80% similarUnverifiedThe MoE architecture activates only the Top-K experts per inference, so the total parameter count far exceeds the number of parameters actually activated in a single inference.68% similarPartially VerifiedMixture of Experts (MoE) architectures allow models to maintain large parameter counts while only activating a subset of parameters during inference, reducing computational overhead67% similarUnverifiedThe large-capacity, small-activation design of MoE allows a model's knowledge capacity and inference cost to be decoupled.66% similarUnverifiedMoE模型在推理延迟和计算成本上往往优于同参数规模的Dense模型，但代价是需要更大显存加载全部专家权重65% similar
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