A very small, highly specialized model can use negligible resources (CPU, energy) to accomplish the same task.

For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.

The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.

Think of it as distillation, but focused on a specific task.