English(EN) ReplaySSM: Cache SSM Inputs, Not State

ReplaySSM 优化状态空间模型，提升解码速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员推出 ReplaySSM，一种旨在优化 AI 应用中状态空间模型 (SSM) 性能的新方法。该技术通过将状态视为检查点和近期输入的缓冲区来解决混合模型中频繁的状态输入/输出操作造成的瓶颈。通过定期更新检查点，ReplaySSM 显著减少了内存流量并提高了推测解码效率。在 vLLM 中对 Mamba-2 和 Gated DeltaNet 等模型的实现显示，其速度比标准解码快 1.47 倍，在大模型上的吞吐量接近现有推测解码方法的两倍。 AI

影响提高状态空间模型的效率和速度，可能加速 AI 推理和部署。

排序理由介绍一种优化 AI 模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ze-Wei Liou, Harris Nover, Amey Naik, Andrii Skliar, Igor Shovkun, Ido Shahaf, Tri Dao · 2026-10-12 04:00

ReplaySSM：缓存SSM输入，而非状态

arXiv:2610.13778v1 Announce Type: cross Abstract: State-space models (SSMs) maintain a fixed-size recurrent state, but reading and writing it at every decoding step can bottleneck hybrid models. This eager state management causes large state I/O with low arithmetic intensity, lea…

本文如何被排名

报道来源 [1]

ReplaySSM：缓存SSM输入，而非状态

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