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English(EN) TACT-KV: Tri-Axis Cosine Transform for Compressing Volumetric KV Caches in Medical VLMs

新的TACT-KV方法将3D医学VLM缓存压缩,批处理大小提升8倍

研究人员开发了TACT-KV,一种用于压缩处理3D医学成像数据的视觉语言模型(VLM)中键值(KV)缓存的新颖方法。与仅考虑1D或2D变换的先前方法不同,TACT-KV利用CT和MRI扫描等体积数据固有的三轴空间结构。通过应用可分离的3D离散余弦变换,TACT-KV集中了缓存的能量,从而实现了更有效的比特分配和量化。这种方法显著减少了内存使用,在单个GPU上可将解码批处理大小提高高达8倍,同时在各种医学和通用VLM中保持高精度。 AI

影响 使得3D视觉语言模型能够处理更大的批处理大小和更高效的处理,尤其是在内存受限的环境中。

排序理由 详细介绍AI模型优化新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的TACT-KV方法将3D医学VLM缓存压缩,批处理大小提升8倍

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chongyu Qu, Ritchie Zhao, Yufan He, Dong Yang, Zhengyi Lu, Junchao Zhu, Tianyuan Yao, Juming Xiong, Junlin Guo, Yanfan Zhu, Yuechen Yang, Daguang Xu, Bennett Allan Landman, Yucheng Tang, Yuankai Huo ·

    TACT-KV:用于医学视觉语言模型中体积KV缓存压缩的三轴余弦变换

    arXiv:2611.07593v1 Announce Type: cross Abstract: Vision-language models (VLMs) applied to 3D medical scans face a structural memory wall. A single CT or MRI volume produces tens of thousands of vision tokens, and the resulting key-value (KV) cache scales linearly with both batch…