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Sparse MoE means the feed-forward layers are split into multiple experts, and each token activates only a small subset of them, so although the total parameter count is 32.9B, the number of parameters actually computed per forward pass is far smaller than that of a dense model, but VRAM still needs to hold all expert weights.
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10/16/2026
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UnverifiedThe 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.78% similarVerifiedThe 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.77% similarVerifiedThe Mixture of Experts (MoE) architecture activates only a small number of experts most relevant to the current task during each inference, thereby drastically reducing the actual computation per inference while maintaining the total parameter count.76% similarUnverifiedK2.5的MoE架构单次推理实际激活的参数量可能只有总参数的1/8甚至更少72% similarUnverifiedMoE模型量化面临额外挑战，因不同专家权重分布差异大，统一量化方案易导致部分专家精度严重下降72% similar
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