Model distillation (knowledge distillation) is a technique that transfers knowledge from a large, accurate model (teacher) to a small, efficient model (student). It is key to deploying AI on resource-limited devices.
What is distillation?
In distillation, the teacher model (e.g., GPT-5, Llama 4 405B) generates predictions that serve as "soft labels" to train the student model (e.g., a small 1-3B parameter model).
The student learns not only correct answers but also the teacher's probability distribution, capturing nuances that hard labels do not convey.
Main techniques
Logit distillation: The student learns to match the teacher's output probabilities. The simplest and most effective technique.
Feature distillation: The student learns to match the teacher's internal representations (hidden layers).
Self-distillation: The same model serves as both teacher and student. Surprisingly effective for improving performance.
Progressive distillation: Multiple distillation rounds gradually reducing size.
Tools
Hugging Face Transformers: Supports distillation with specialized Trainer classes.
TensorFlow Model Optimization: Distillation and quantization tools.
Microsoft Olive: Model optimization framework including distillation.
Intel Neural Compressor: Optimization with distillation, pruning, and quantization.
When to use distillation
Large inference volumes where API or compute costs are significant, deployment on Edge or mobile devices, latency-critical applications (<100ms), and when you need privacy (local model) but cannot sacrifice much accuracy.
Typical results
A 3B student model can maintain 95-98% of a 70B+ teacher's accuracy on general tasks, while being 10-20x faster and 20-50x smaller.
On very specific tasks, the difference can be even smaller.
Distillation vs fine-tuning
Fine-tuning large models and then distilling to a small model usually yields better results than directly fine-tuning the small model.
Distillation enables bringing high-level AI to any device. At Vynta we apply distillation to create efficient models that maintain high accuracy. Contact us if you need fast, lightweight AI models for your application.