English(EN) How NVIDIA Cut DeepSeek Sparse Attention’s Top-K Time

NVIDIA 优化 DeepSeek 稀疏注意力以加快解码速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-09 13:31

NVIDIA 开发了一种方法，可以显著加快 DeepSeek 稀疏注意力模型中使用的 Top-K 采样过程。此优化利用了自回归解码的一个特性来减少计算时间。该技术侧重于降低文本生成相关的延迟，从而提高模型的效率。 AI

影响像这样的优化对于减少推理延迟至关重要，有可能加速大型稀疏注意力模型的部署和可用性。

排序理由文章详细介绍了现有模型推理过程的技术优化，而非新模型发布或基本研究突破。[lever_c_demoted from research: ic=1 ai=0.7]

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Towards AI TIER_1 English(EN) · Gowtham Boyina · 2026-05-09 13:31

英伟达如何大幅缩短DeepSeek Sparse Attention的Top-K时间

<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://pub.towardsai.net/how-nvidia-cut-deepseek-sparse-attentions-top-k-time-8044db298334?source=rss----98111c9905da---4"><img src="https://cdn-images-1.medium.com/max/2483/1*q7egz-LJl-LK-KaptjTPKA.png" width="…