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English(EN) WorldPager: Zero-Stall Tiered Memory for Long-Horizon World Models on a Single Consumer GPU

WorldPager系统可在单GPU上实现长视界世界模型

研究人员开发了WorldPager,这是一种新颖的内存系统,旨在使长视界世界模型能够在单台消费级GPU上运行。该系统通过利用包括NVMe存储的分层内存结构,解决了现有交互式世界模型的容量和位置视界限制。WorldPager实现了零停顿预取和连续生成扩展视频会话,而不会损害主机内存,并且其存储方法没有显示出明显的质量损失。 AI

影响 使在可访问的硬件上运行更复杂、更长时间的AI模拟成为可能。

排序理由 该项目是一篇学术论文,详细介绍了AI模型内存管理的新技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

WorldPager系统可在单GPU上实现长视界世界模型

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该项目是一篇学术论文,详细介绍了AI模型内存管理的新技术方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Bingnan Liu, Ruini Xue ·

    WorldPager:单消费级GPU上的长视界世界模型的零停顿分层内存

    arXiv:2610.35529v1 Announce Type: cross Abstract: Interactive world models keep the KV of past chunks as retrieval memory so that revisited places stay consistent. On a single consumer machine (one RTX 4090, 32 GiB of host memory, one NVMe SSD), long sessions with such memory hit…