TStore: Rethinking AI Model Hub with Tensor-Centric Compression
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909038230372352 |
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| author | Lan, Tingfeng Wang, Zirui Zheng, Yunjia Su, Zhaoyuan Yang, Juncheng Cheng, Yue |
| author_facet | Lan, Tingfeng Wang, Zirui Zheng, Yunjia Su, Zhaoyuan Yang, Juncheng Cheng, Yue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17104 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TStore: Rethinking AI Model Hub with Tensor-Centric Compression Lan, Tingfeng Wang, Zirui Zheng, Yunjia Su, Zhaoyuan Yang, Juncheng Cheng, Yue Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning D.4.2; E.5; I.2.6 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. |
| title | TStore: Rethinking AI Model Hub with Tensor-Centric Compression |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning D.4.2; E.5; I.2.6 |
| url | https://arxiv.org/abs/2604.17104 |