May 08, 2026
Using a flagship model like GPT-4o for every request is expensive and often unnecessary. Model distillation is a technique that allows you to capture the "intelligence" of a large model and put it into a smaller, cheaper one.
In distillation, you use a "Teacher" model (e.g., GPT-4) to generate high-quality labels or explanations for your dataset. You then train a "Student" model (e.g., Llama 3 8B) on this synthetic data. The student model learns to mimic the teacher's reasoning and output style, often matching its performance on specific tasks while being 10x faster and 100x cheaper to run.
Distillation is the key to creating "vertical" AI. Instead of a general-purpose giant, you end up with a highly specialized small model that excels at *your* specific task—whether that's legal summarization, medical classification, or code generation. This specialized approach is the most sustainable way to scale AI intelligence across an organization.