Activation Sparsity

TL;DR for operators Prox1 addresses a deployment problem that appears whenever sparsity itself requires computation: how much work should an inference system spend deciding which work to skip? Its answer is unusually specific. Use a cheap, input-sparse INT4 calculation to rank likely-important feed-forward channels, not to approximate their final values. Then recompute only the selected channels with the original model weights. The distinction matters empirically: at 70% effective FFN sparsity, removing the exact recomputation stage drops the aggregate downstream score from 68.6 to 44.3 on Qwen3-8B and from 74.8 to 56.7 on Qwen3-14B. ...

The Experts Are Sparse Inside: Why MoE Cost Cuts Stop at 1.2x Cost has a way of making architecture fashionable. Mixture-of-Experts models became attractive because they promise a pleasant bargain: keep a large total parameter count, but activate only a small part of the model for each token. In business language, that sounds like capacity without the full compute bill. In engineering language, it means routing each token to a few expert feed-forward networks instead of running every expert all the time. ...