Model Compression and Knowledge Distillation: Making Large Models Practical

This article discusses two key techniques, model compression and knowledge distillation, that are used to make large AI models more practical for real-world deployment.

Details

Large AI models, especially large language models (LLMs), often contain billions of parameters, making them computationally expensive to run. This creates real-world challenges like high latency, high cloud costs, limited edge deployment, and environmental concerns. To address these issues, two key techniques are used in practice: model compression and knowledge distillation. Model compression refers to techniques like parameter pruning, quantization, and low-rank factorization that reduce model size and computational cost without significant accuracy loss. Knowledge distillation is a specific form of model compression that transfers knowledge from a large, accurate 'teacher' model to a smaller, faster 'student' model. The student learns not just from ground-truth labels, but from the teacher's 'dark knowledge' like soft probabilities and relative confidence, making it more robust and efficient. In practice, these techniques are widely used to deploy LLMs on edge devices, reduce inference latency, and serve high traffic at lower cost. Many commercial 'small' models today are actually distilled and quantized versions of larger foundation models.