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Everything You Always Wanted to Know About Storage Compressibility of Pre-Trained ML Models but Were Afraid to Ask

Summary: Exhaustive analysis of pre-trained model file compressibility across granularity levels, showing general-purpose compressors fail to exploit PTM-specific patterns. Propose Elf, an error-bounded float transform that removes shared exponents, and Elves framework; achieves 1.52× compression (~1.3× vs zstd/SZ3/quant) with negligible accuracy loss. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13625
Venue
VLDB
Year
2024
Pagerank
5.3960216e-05
Overall Rank
8,634 | 40.77%
DOI
10.14778/3659437.3659456

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{su_vldb24,
        title = {{Everything You Always Wanted to Know About Storage Compressibility of Pre-Trained ML Models but Were Afraid to Ask}},
        author = {Su, Zhaoyuan and Ahmed, Ammar and Wang, Zirui and Anwar, Ali and Cheng, Yue},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {8},
        pages = {2036--2049},
        doi = {10.14778/3659437.3659456},
        url = {https://doi.org/10.14778/3659437.3659456},
        year = {2024}
}

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