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arXiv · 2604.17104

TStore: Rethinking AI Model Hub with Tensor-Centric Compression

Tingfeng Lan·Zirui Wang·Yunjia Zheng·Zhaoyuan Su·Juncheng Yang·Yue Cheng
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Abstract

Modern AI models are growing rapidly in size and redundancy, leading to significant storage and distribution challenges in model hubs. We present TStore, a tensor-centric system for reducing storage overhead through fine-grained deduplication and compression. TStore leverages tensor-level fingerprinting and clustering to identify redundancy across models without requiring annotations. Our design enables efficient storage reduction while preserving model usability and performance. Experiments on real-world model repositories demonstrate substantial storage savings with minimal overhead.

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Tingfeng Lan, Zirui Wang, Yunjia Zheng, Zhaoyuan Su, Juncheng Yang, Yue Cheng. 2026-04-18. TStore: Rethinking AI Model Hub with Tensor-Centric Compression. https://arxiv.org/abs/2604.17104

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