Unverified50% confidenceFactExact time
Expert outputs in MoE layers exhibit structural redundancy, manifested as the stable rank and entropy effective rank of expert outputs being far lower than the nominal number of experts, and the output distributions having fewer effective modes.
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Long-term
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11/2/2026
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VerifiedIn MoE implementations, without constraints, the router tends to concentrate token assignments to a small number of experts, leading to Expert Collapse.75% similarUnverifiedThe sparse activation characteristic of MoE means that the actual computation during inference is tied only to the number of activated experts, rather than the total number of parameters.75% similarUnverifiedUnder the MoE architecture, VRAM needs to accommodate the weights of all experts, but compute consumption is reduced proportionally to the activation ratio.75% similarUnverifiedSelection based on internal expert activations requires a precomputation pass, while feeding multiple full representations separately into the same expert repeats that expert's computation, and under a fixed expert budget both approaches erode the efficiency advantage of MoE.74% similarUnverifiedFewer activated parameters in a MoE model does not equal lower memory usage, because the full weights of all experts still need to be loaded.74% similar
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