Inference
Distillation
A training technique where a smaller "student" model learns to replicate the behavior of a larger "teacher" model.
In depth
A training technique where a smaller "student" model learns to replicate the behavior of a larger "teacher" model. The student is trained on the teacher's outputs rather than raw data, inheriting much of the larger model's capability at a fraction of the size and inference cost. Distillation is how many fast, lightweight models are created from frontier models.
Example
In practice, developers reach for Distillation when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
What is Distillation?
A training technique where a smaller "student" model learns to replicate the behavior of a larger "teacher" model.
Why does Distillation matter for AI developers?
Distillation sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Distillation?
Developers Digest publishes tutorials and videos that cover Inference topics including Distillation. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
Inference
Mixture of Experts (MoE)
A model architecture that routes each input to a small subset of specialized sub-networks ("experts") rather than activating the entire model.
Inference
Unsupervised Learning
A category of machine learning where models learn patterns from data without labeled examples or explicit correct answers.
Inference
GGUF
A binary file format for storing quantized language models, designed for efficient local inference with llama.cpp and tools built on it.
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