Training-time compute

Topic

Training-time compute refers to the total amount of computational resources, typically measured in floating-point operations (FLOPs), expended to train a machine learning model. It is a primary driver of model capability under neural scaling laws, determining the optimal balance between model parameter size and training dataset volume. In AI governance and research, training-time compute serves as a key metric for tracking technological progress and establishing regulatory thresholds.