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.
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FAQ
Common questions
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
Related terms
Inference
Diffusion Language Model
A language model that writes text by starting from a fully masked sequence and filling in many positions in parallel over several denoising steps, instead of producing one token at a time.
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
Multi-Token Prediction (MTP)
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk.
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