Unverified50% confidenceFactExact time
In MoE architectures, the number of gating activation calls grows linearly with the number of experts, causing the proportion of SwiGLU/SiLU computation to be far higher than in dense models.
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50%
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Long-term
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10/4/2026
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Related Claims
UnverifiedThe MoE architecture routes and activates only a small number of experts per pass (typically 2–8), enabling the total parameter scale to far exceed that of compute-dense models of equivalent computational cost.70% similarUnverifiedThe MoE sparse architecture activates only a small fraction of all experts during each forward pass, dramatically scaling up model parameter count without a proportional increase in computation.69% similarUnverifiedVRAM capacity often determines what size model can be loaded more than raw compute performance does.69% similarVerifiedAlthough MoE models infer quickly, they have higher demands on VRAM and memory capacity because all expert parameters must be loaded, representing a trade-off between speed and capacity.68% similarUnverifiedHSTU uses a linear-complexity attention variant, so processing behavior sequences longer than several thousand does not result in quadratic growth in computational cost.66% similar
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