Unverified50% confidenceFactExact time
Although MoE models have low computational requirements, the entire model still needs to be loaded into memory, so the total memory requirement is not low; however, memory bandwidth pressure is greatly reduced because only the activated parameters are read each time.
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50%
Confidence
Long-term
Relevance
9/10/2026
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UnverifiedMoE模型采用稀疏激活，Qwen 3.5 122B总参数122B但每次推理仅激活约10B，需将全部参数加载进内存，对内存容量极度敏感72% similarUnverified自回归推理是memory-bound（内存带宽受限）问题，因为batch size通常为1时算术强度极低，GPU大部分时间在等待数据搬运72% similarUnverifiedMoE models read only the activated subset of parameters at each step, making them much faster than dense models of the same parameter count; for example, GLM 5.3 Flash reads only about 30 GB per token.71% similarUnverified模型规模越大，记忆能力反而越强，因为更大的模型拥有更强的参数容量来存储低频样本70% similar
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