Define what “training” means
Pretraining a model, adapting an existing model, and improving a prompt are different projects. Before planning infrastructure, specify the behavior you want to change and the examples available to evaluate it. A larger training run cannot repair an unclear objective.
Budget beyond the weights
Training also uses memory for gradients, optimizer state, intermediate activations, and temporary operations. Batch size and sequence length affect the memory required. The exact footprint depends on implementation and numerical precision; see Hugging Face’s training-memory documentation.
Measure a representative pilot
Run a small experiment with realistic sequence lengths. Record peak memory, tokens processed per second, checkpoint size, and validation behavior. Estimate the larger run from observed throughput, allowing for evaluation, restarts, and data loading rather than assuming ideal utilization.
Account for the full experiment
Data inspection, duplicate removal, dataset versioning, and comparison with the unchanged model all take time. Keep a separate evaluation set and an experiment log. Include unsuccessful runs in the budget, and define a stopping rule before repeatedly changing settings.
Try this: Write a one-page training plan containing the target behavior, baseline, data split, pilot measurements, and conditions under which you would stop.