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新方法有望通过稀疏注意力和权重近似加速大语言模型解码

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文提出了加速大语言模型（LLMs）解码过程的方法。第一篇论文介绍了稀疏非对称分组查询注意力（SAGA），它减少了键头的数量，同时保留了更多的价值头以提高效率，实现了2倍以上的加速，且质量损失极小。第二篇论文SpAx通过结合激活稀疏性和权重近似来解决在内存有限的硬件上部署LLMs的挑战，当权重被卸载到CPU内存或闪存时，可以显著加速。 AI

影响 这些技术可以显著降低LLM推理的计算成本和延迟，从而在资源受限的硬件上实现更广泛的部署。

排序理由 两篇在arXiv上发表的学术论文，提出了提高LLM解码效率的新颖方法。

在 arXiv cs.CL 阅读 →

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新方法有望通过稀疏注意力和权重近似加速大语言模型解码

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两篇在arXiv上发表的学术论文，提出了提高LLM解码效率的新颖方法。
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报道来源 [3]

  1. arXiv cs.CL TIER_1 English(EN) · Jorge L. Ruiz Williams ·

    用于稀疏解码的注意力-质量凝聚

    arXiv:2602.06317v3 Announce Type: replace-cross Abstract: Attention-mass concentration creates an opportunity for sparse decoding, but retained mass alone does not guarantee a stable greedy decision: retrieval error, omitted value directions, and recursive decoding all matter. We…

  2. arXiv cs.AI TIER_1 English(EN) · Noam Elata, Itay Lamprecht, Mikey Shechter, Daniel Ohayon, Itay Hubara, Daniel Soudry ·

    每键更多价值：非对称稀疏注意力加速大语言模型解码

    arXiv:2610.04753v2 Announce Type: replace-cross Abstract: Autoregressive generation in Large Language Models (LLMs) is constrained by the memory and computational demands of attention mechanisms. Sparse attention methods mitigate this cost by selecting only high-probability entri…

  3. arXiv cs.LG TIER_1 English(EN) · JuneHyung Kim, Sankeerth Durvasula, Nandita Vijaykumar ·

    通过权重近似实现激活稀疏化，加速LLM在卸载权重上的解码

    arXiv:2610.02598v1 Announce Type: new Abstract: Deploying LLMs on consumer-grade GPUs with insufficient memory to hold their weights can result in prohibitively slow inference, because decoding repeatedly transfers offloaded weights from system RAM or flash storage into GPU at mu…

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