Scaling Laws Pinpoint Optimal Compute Split Between Model Size and Data
Training loss drops predictably as model size, dataset tokens, and compute increase, following a power‑law. By fitting this relationship on a few small experiments, you can extrapolate the exact compute and data requirements for much larger models, letting teams allocate resources efficiently and avoid costly over‑ or under‑scaling.