Small language models (SLMs) are AI models trained on a smaller volume of parameters than large language models, typically optimised for a narrower set of tasks or a specific domain. They generally require less compute, are cheaper to run and easier to deploy on private infrastructure, which makes them well-suited to regulated environments where data sovereignty and cost control matter. SLMs trade some general capability for greater efficiency, speed and specialism.
