What Model distillation means in practice
Distillation trains a small model to imitate the behaviour of a larger one by minimising the divergence between their output distributions on a shared corpus. The practical effect is a 5–20× reduction in inference cost at a single-digit-percent quality loss on the distilled task. For enterprises, distillation is the path from "we proved this works with a 70B model" to "we can afford to serve this at production volume on an 8B model". MindMap routinely distils a customer's domain-specific 70B prototype into an 8B production model once the eval suite confirms behavioural parity within the customer's acceptance threshold.
Related terms
Small Language Model (SLM) →
A purpose-built or distilled model in the 1–8 billion parameter range, optimised for a specific domain or task, typically served on commodity GPUs.
Fine-tuning →
Adapting a pretrained model to a specific domain or task by continuing training on a smaller, curated dataset.
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The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.