Model Compression
Learn how to compress large AI models into smaller, faster, and deployable versions using knowledge distillation—without sacrificing accuracy.
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.
When interviewers pose this question, they try to assess a few things.
First, do you understand the core concept of knowledge distillation? Can you explain it clearly in your own words?
Second, do you appreciate why it’s useful—e.g., how it addresses the challenges of deploying large models (speed, memory, cost) while retaining accuracy?
They may also be probing your knowledge of related concepts: perhaps expecting you to mention how distillation differs from other model compression techniques like pruning and quantization.
A strong answer will cover knowledge distillation, how it works at a high level, and why one would use it (with examples).
What is knowledge distillation?
Knowledge distillation is a model compression technique in which a large, high-capacity model (often called the “teacher” model) transfers its knowledge to a smaller “student” model. In simple terms, the idea is to train the smaller student model to mimic the behavior of the big teacher model. By doing so, the student model can achieve nearly the teacher’s performance while being much more efficient regarding parameters and computation. A good way to think about it is that knowledge distillation refers to transferring knowledge from a large, unwieldy model or set of models to a single, smaller model that can be practically deployed under real-world constraints. Essentially, the complex knowledge encapsulated by a huge model (or even an ensemble of models) is “distilled” into a lightweight model.
We can also say that we are dealing with a mentor and it’s apprentice: the teacher (mentor) is an expert with a wealth of knowledge but perhaps too slow or costly to use in practice; the student (apprentice) is less powerful but if properly trained by the mentor, can learn to perform the task almost as well. The ...