Rather than adjusting billions of weights, LoRA inserts small low-rank matrices and trains only those. The result is dramatically cheaper to train and store, and adapters can be swapped per task.
It is the reason fine-tuning is accessible at all outside large labs, and why serving many task-specific variants from one base model is practical.