# Model Distillation **Entity class:** Machine-learning technique **Domain:** Artificial intelligence / Model compression **Maturity:** Developed ## Definition Model distillation trains a smaller or cheaper model to reproduce selected behavior of a larger teacher model or ensemble. ## Mechanism and significance Distillation can move capability toward the edge and reduce cost, but it may compress errors, lose rare skills, or obscure the sources of the teacher's behavior. ## Relationships - **Research dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] - **Ontology route:** [[ASI and RSI Timeline Ontology#Model Architecture and Compute Economics|Model Architecture and Compute Economics]] - **Primary fields:** [[Machine Intelligence]] · [[AI Infrastructure]] · [[Compute Race]] - **Adjacent concepts:** [[NanoGPT]] · [[World Knowledge Database]] · [[Open-Weight Chinese Model]] · [[Frontier-Model Specialization]] ## Sources and provenance - [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] — immediate source for this node's role in the broadcast research map. ## Evidence boundary The research dossier establishes why this entity or concept belongs in the Moonshots ontology. Time-sensitive organizational, product, policy, and performance claims should be checked against the linked primary source or a current authoritative source before reuse as settled fact.