Public articles linked to the same research event.
arXiv The study applies post-training quantization (PTQ) algorithms to two pre-trained global weather forecasting models, Deep Learning Weather Prediction (DLWP) and FourCastNet (FCN), systematically examines the effect of quantization on autoregressive inference, and reports that evaluation with simulated quantization indicates qualitatively meaningful forecasts over short-range horizons, providing a first benchmark of PTQ for autoregressive weather emulators.
The study applies post-training quantization (PTQ) algorithms to two pre-trained global weather forecasting models, Deep Learning Weather Prediction (DLWP) and FourCastNet (FCN), systematically examines the effect of quantization on autoregressive inference, and reports that evaluation with simulated quantization indicates qualitatively meaningful forecasts over short-range horizons, providing a first benchmark of PTQ for autoregressive weather emulators.
The study applies post-training quantization (PTQ) algorithms to two pre-trained global weather forecasting models, Deep Learning Weather Prediction (DLWP) and FourCastNet (FCN), systematically examines the effect of quantization on autoregressive inference, and reports that evaluation with simulated quantization indicates qualitatively meaningful forecasts over short-range horizons, providing a first benchmark of PTQ for autoregressive weather emulators.
The study applies post-training quantization (PTQ) algorithms to two pre-trained global weather forecasting models, Deep Learning Weather Prediction (DLWP) and FourCastNet (FCN), systematically examines the effect of quantization on autoregressive inference, and reports that evaluation with simulated quantization indicates qualitatively meaningful forecasts over short-range horizons, providing a first benchmark of PTQ for autoregressive weather emulators.