A unified, open-source framework that converts complex post-training quantization workflows into a single-line, hardware-aware pipeline.
April 1, 2026
Original Paper
OneComp: One-Line Revolution for Generative AI Model Compression
arXiv · 2603.28845
The Takeaway
It democratizes state-of-the-art model compression by automating the search for precision budgets and mixed-precision assignments. This bridges the gap between algorithmic research and production-grade deployment for resource-constrained environments.
From the 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 One