[2603.28845] OneComp: One-Line Revolution for Generative AI Model Compression

Computer Science > Machine Learning arXiv:2603.28845 (cs) [Submitted on 30 Mar 2026] Title:OneComp: One-Line Revolution for Generative AI Model Compression Authors:Yuma Ichikawa, Keiji Kimura, Akihiro Yoshida, Yudai Fujimoto, Hiroki Tokura, Yamato Arai, Yoshiyuki Ishii, Yusei Kawakami, Genki Shikada, Achille Jacquemond, Yoshihiko Fujisawa, Katsuki Fujisawa, Takumi Honda, Akira Sakai View a PDF of the paper titled OneComp: One-Line Revolution for Generative AI Model Compression, by Yuma Ichikawa and 13 other authors View PDF HTML (experimental) Abstract:Deploying foundation models is increasingly constrained by memory footprint, latency, and hardware costs. Post-training compression can mitigate these bottlenecks by reducing the precision of model parameters without significantly degrading performance; however, its practical implementation remains challenging as practitioners navigate a fragmented landscape of quantization algorithms, precision budgets, data-driven calibration strategies, and hardware-dependent execution regimes. We present OneComp, an open-source compression framework that transforms this expert workflow into a reproducible, resource-adaptive pipeline. Given a model identifier and available hardware, OneComp automatically inspects the model, plans mixed-precision assignments, and executes progressive quantization stages, ranging from layer-wise compression to block-wise refinement and global refinement. A key architectural choice is treating the first quan...