English(EN) Slim attention: cut your context memory in half without loss -- K-cache is all you need for MHA

Slim attention 将 Transformer 上下文内存减半且无精度损失

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种名为“slim attention”的新技术，可以在不影响精度的前提下将 Transformer 模型上下文内存大小减半。该方法在数学上与标准注意力机制相同，可将大上下文窗口的推理速度提高高达2倍。对于像Whisper这样的特定模型，slim attention 可将上下文内存减少8倍，从而在特定批处理大小下将 token 生成速度提高5倍。该技术还为T5-11B等模型提供了更大的内存缩减，实现了32倍的降低。 AI

影响可能显著提高大型语言模型的推理效率并降低内存需求。

排序理由详细介绍 Transformer 模型新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Slim attention 将 Transformer 上下文内存减半且无精度损失

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arXiv cs.LG TIER_1 English(EN) · Nils Graef, Kayvan Zahiri, Andrew Wasielewski · 2026-11-02 05:00

Slim attention: cut your context memory in half without loss -- K-cache is all you need for MHA

arXiv:2503.05840v3 Announce Type: replace Abstract: Slim attention shrinks the context memory size by 2x for transformer models with MHA (multi-head attention), which can speed up inference by up to 2x for large context windows. Slim attention is an exact, mathematically identica…

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Slim attention: cut your context memory in half without loss -- K-cache is all you need for MHA

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