An Overview of Small Language Models (SLMs) and their Applications

As organizations increase AI adoption across various business units, small language models are gaining prominence for delivering resource-efficient intelligence that can run on mobile devices, edge platforms, and constrained environments. Deploying and scaling large language model-based applications can be cost-intensive, depending on acquiring training datasets, model type and size, customization, compute infrastructure, scalability, fine-tuning, etc. Projected to cost hundreds of millions of dollars for training a cutting-edge large language model.

Large Language Models (LLMs) can perform completely different tasks, such as answering questions, coding, summarizing documents, translating languages, and generating content, etc. However, the next phase in AI would be efficiency with lightweight, purpose-built models delivering domain-specific intelligence augmented with intelligent routing to deliver optimal results.

What are Small Language Models (SLMs)?

SLMs are essentially smaller versions of LLMs trained on specific knowledge pertaining to a domain with significantly fewer parameters, usually ranging from a few million to a few ...