PowerQuant: Architecture-Agnostic GPU Power Estimation via Quantile Regression

Accurate prediction of NVIDIA GPU power consumption remains challenging due to rapid architectural evolution. Existing machine-learning–based power models are tightly coupled to specific GPU architectures and degrade sharply on unseen platforms, requiring retraining and extensive power measurements, which hinder scalability. This paper presents a quantile-regression–based GPU power prediction framework that enables architecture-agnostic power estimation using static analysis-based compile-time CUDA kernel features. The key insight is that architectural changes primarily induce systematic shifts in power scale, while the relative ordering of kernel power demands remains preserved. By learning power quantiles that capture this ordering and mapping them to new GPUs through one-time calibration, the proposed approach mitigates cross-architecture distribution shift. Extensive evaluation across multiple NVIDIA GPU generations shows that, on unseen architectures, the proposed method improves prediction accuracy by up to 30–50% over existing regression models, while maintaining comparable accuracy in in-distribution settings. The resulting low-overhead, generalizable power estimates make the approach practical for power-aware scheduling, energy budgeting, and sustainability-oriented resource management in large HPC systems.