How Scaling Laws Drive Smarter, More Powerful AI
NVIDIA News, Wednesday, February 12th, 2025
Scaling laws describe how the performance of AI systems improves as the size of the training data, model parameters or computational resources increases.
Just as there are widely understood empirical laws of nature - for example, what goes up must come down, or every action has an equal and opposite reaction - the field of AI was long defined by a single idea: that more compute, more training data and more parameters makes a better AI model.
However, AI has since grown to need three distinct laws that describe how applying compute resources in different ways impacts model performance. Together, these AI scaling laws - pretraining scaling, post-training scaling and test-time scaling, also called long thinking - reflect how the field has evolved with techniques to use additional compute in a wide variety of increasingly complex AI use cases.