Model distillation is a way to compress a large, capable model into a smaller one. A big teacher model generates outputs, and a smaller student model is trained to reproduce them. The result is a leaner model that captures much of the teacher's behavior at a fraction of the size and cost.

It matters because the largest models are often too slow or expensive to run at scale. Distillation lets teams deploy a model that is cheaper to operate and faster to respond while preserving most of the accuracy on the specific tasks that matter, which is ideal for high-volume or latency-sensitive applications.

At arosplatforms we consider distillation when a client has a well-defined, high-volume task where a frontier model would be overkill. By distilling down to a focused, smaller model, we cut inference cost and latency substantially without giving up the quality the use case needs.