Large models are capable but expensive to run. Distillation trains a smaller model to reproduce the outputs of a larger one, transferring much of the behaviour into a form that is cheaper and faster to deploy.
The small model rarely matches the original on the hardest cases, but for many everyday tasks it comes close at a fraction of the cost. It is one of the main reasons capable assistants can run on modest hardware rather than only in large data centres.