Small Language Models as an Efficient Alternative to Large Language Models in Domain-Specific AI Systems

Abstract

Large language models (LLMs) have demonstrated strong performance across a wide range of natural language processing tasks; however, their deployment in real-world, domain-specific systems is often constrained by high computational cost, latency, energy consumption, and data governance requirements. In parallel, small language models (SLMs) have emerged as a practical alternative, offering significantly lower resource demands while maintaining acceptable performance when applied to narrowly defined tasks. This paper investigates the feasibility of using SLMs as an efficient alternative to LLMs in domain-specific AI systems. We conduct a comparative analysis of representative SLM and LLM configurations across selected applied scenarios, focusing on task accuracy, inference latency, memory consumption, and deployment constraints. The results demonstrate that, for domain-bounded applications with well-defined context, SLM-based solutions can achieve performance comparable to LLMs while providing substantial advantages in efficiency, cost, and on-premise deployability. Based on the experimental findings, practical recommendations are proposed to guide the selection of language model architectures in applied engineering contexts.

Keywords: Small Language Models; Large Language Models; OCR Post-Processing; Edge AI; Document Processing; Model Efficiency; Embedded Systems