What Small Language Model (SLM) means in practice
A Small Language Model is a 1–8B parameter LLM that has either been pretrained on a narrow domain (clinical notes, financial filings, code) or distilled from a larger model. SLMs trade a small amount of general capability for a large reduction in inference cost and a meaningful improvement in domain-specific accuracy. In sovereign enterprise deployments SLMs are the workhorse for high-volume routing, classification, and document-extraction workloads, with the larger 70B-class model reserved for the long-tail complex queries. A well-tuned 8B model on a single A100 routinely outperforms a 70B model on the customer's narrow benchmark while costing 10× less to serve.
Related terms
Fine-tuning →
Adapting a pretrained model to a specific domain or task by continuing training on a smaller, curated dataset.
Large Language Model (LLM) →
A transformer-architecture neural network trained on very large text corpora to predict the next token in a sequence, producing fluent natural-language output across a wide range of tasks.
Model distillation →
Training a smaller "student" model to imitate the input-output behaviour of a larger "teacher" model.
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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.