Model Compression

In machine learning interviews, especially for roles involving model optimization, a common question you might encounter is “What is knowledge distillation and why is it useful?” This topic frequently arises because modern AI models are growing enormously; for example, Llama 4 reportedly has a staggering 2 trillion parameters in its largest version, yet practical deployments demand smaller, efficient models.

Interviewers ask about knowledge distillation to gauge whether you understand how we can compress these gigantic models into more manageable sizes without losing too much performance. They want to see that you understand the definition of knowledge distillation, its motivation, and its real-world relevance. For instance, although Llama 4’s “Behemoth” 2-trillion-parameter model exists, Meta only released the much smaller Llama 4 Scout (109B) and Maverick (400B) models—and they achieved this by using the giant model as a teacher to train the smaller ones (i.e., by distilling knowledge from the 2T model)​. This example highlights why knowledge distillation is so important: it allows the AI community to share and deploy the expertise of a massive model in the form of a lightweight model that more people can use.