The process of adjusting a model's parameters over many iterations on a large dataset to minimize prediction error — the most compute-intensive phase of the AI pipeline.
Training a large model from scratch requires tightly coupled high-bandwidth GPU clusters, making it harder for decentralized networks to compete with centralized hyperscalers for this workload. DePIN compute networks primarily target fine-tuning and inference, though distributed training research is active.