Unverified50% confidenceFactExact time
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
1
Sources
50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
UnverifiedModels with hundreds of billions or trillions of parameters can require hundreds of gigabytes or several terabytes of memory when accounting for weights, activations, and optimizer states72% similarUnverifiedThe memory requirements of the Jacobian Lens grow linearly with model scale, roughly 1/8 of the model size.72% similarVerifiedThe speed bottleneck in large model inference is memory bandwidth rather than insufficient computing power, as generating each token requires loading hundreds of GB of model weights from memory.71% similar
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