Domain-Specific Small Language Models

What we keep being asked

“Can a model that knows our domain run inside our walls?” Regulated buyers want translation and document intelligence that never leaves the building — and they want it without the cost curve of a general-purpose API.

Fig. 2Domain documents distilled into one compact model, served from a desk-scale box, not a data center.

What it is

A small language model — roughly 1B to 14B parameters — fine-tuned on domain text so it speaks the customer’s language: terminology, document formats, and translation conventions. A retrieval layer supplies current knowledge at answer time, so the model does not need retraining when the documents change. Typical first workloads are domain-specific translation (Korean-English technical and regulatory documents), report drafting, and question answering over private archives.

Why now

NVIDIA Research argues that small models are sufficient, more suitable, and cheaper for most agentic workloads^[1]. Gartner expects task-specific small models to be adopted three times more often than general LLMs by 2027^[4]. The prevailing enterprise pattern is exactly this split: fine-tuning for behavior, retrieval for knowledge^[2][3]. Privacy and API cost push in the same direction — regulated customers want models that never send text outside their walls.

What UXF brings

Quantization and memory-bound inference are chip-architecture problems, and that is our home ground. We co-design the model and the deployment target together: pick the smallest model that clears your quality bar, quantize it, and report measured latency and energy per token on the hardware you will actually run — an on-premise server today, our silicon roadmap tomorrow.

Early stage: co-design engagements

How we engage

Send us your domain corpus, your quality bar, and your deployment constraint, and we return measured numbers on a candidate model. First engagements are model-and-deployment co-design projects rather than a shrink-wrapped product. Request a briefing to scope one.