OneComp: One-Line Revolution for Generative AI Model Compression

• URL: http://arxiv.org/abs/2603.28845v1
• Date: Mon, 30 Mar 2026 17:43:32 GMT
• Title: OneComp: One-Line Revolution for Generative AI Model Compression
• Abstract summary: Post-training compression can reduce the precision of model parameters without significantly degrading performance.<n>We present OneComp, an open-source compression framework that transforms this expert workflow into a resource-adaptive pipeline.<n>A key architectural choice is treating the first quantized checkpoint as a deployable pivot, ensuring that each subsequent stage improves the same model.
• Score: 5.503278667006273
• License: http://creativecommons.org/licenses/by/4.0/
• 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 quantized checkpoint as a deployable pivot, ensuring that each subsequent stage improves the same model and that quality increases as more compute is invested. By converting state-of-the-art compression research into an extensible, open-source, hardware-aware pipeline, OneComp bridges the gap between algorithmic innovation and production-grade model deployment.

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