Breaks the massive compute barrier for medium-range weather forecasting, training on a single consumer-grade GPU.
March 24, 2026
Original Paper
Sonny: Breaking the Compute Wall in Medium-Range Weather Forecasting
arXiv · 2603.21284
The Takeaway
By using a two-stage StepsNet design, the authors achieve results competitive with massive numerical systems while training in just 5.5 days on a single A40. This democratizes high-fidelity meteorological modeling for researchers without access to industrial-scale compute clusters.
From the abstract
Weather forecasting is a fundamental problem for protecting lives and infrastructure from high-impact atmospheric events. Recently, data-driven weather forecasting methods based on deep learning have demonstrated strong performance, often reaching accuracy levels competitive with operational numerical systems. However, many existing models rely on large-scale training regimes and compute-intensive architectures, which raises the practical barrier for academic groups with limited compute resource