Examines why activation sparsity arises naturally in ReLU-based neural networks and how much of a model’s computation is spent on values that are simply zero. Surveys the techniques that turn this property into real throughput gains — including Mixture of Experts and sparse attention — and shows how they deliver major speedups without sacrificing model accuracy.

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