Unverified60% confidenceFactExact time
The 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.
2
Sources
60%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
Related Claims
VerifiedThe MoE architecture uses a router to dynamically select the Top-K experts for computation for each token, allowing the total parameter count to scale while keeping the number of activated parameters per inference constant.81% similarUnverifiedMoE架构下模型总参数量远大于单次推理实际激活的参数量，Qwen3.8每次推理激活参数可能仅数百亿级别80% similarVerifiedThe MoE architecture uses a gating network to dynamically select the K most relevant experts (typically Top-K, K=2) for each input token, activating only approximately 10%–30% of total parameters during inference.80% similarVerifiedThe MoE architecture does not activate all parameters during inference; instead, a router dynamically selects a small number of expert sub-networks to process each token, with each token typically activating only K=2 or K=8 experts.78% similar
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