How Engineers Shrink AI Models Without Sacrificing Their Capabilities

Building smaller AI models is not simply a matter of deleting parameters from larger ones, as doing so destroys learned capabilities rather than preserving them. Engineers use a combination of techniques — including knowledge distillation, quantization, pruning, and fine-tuning — each targeting different aspects of model efficiency. Knowledge distillation, a concept predating modern large language models, trains a smaller 'student' model by having it learn from the behavior of a larger 'teacher' model, not just from raw training labels. This allows the student to capture subtle patterns in the teacher's outputs, such as probability distributions across possible answers, rather than just correct answers. These techniques are often combined in a pipeline — distilling capability, then fine-tuning for a specific domain, then quantizing for efficient deployment.