In recent years, large language models (LLMs) have become prominent for their extensive capabilities attributed to their vast number of parameters. However, significant issues such as operational costs, response latency, and security concerns have emerged as major obstacles to implementing LLMs in business contexts. Small Language Models (SLM), designed with fewer parameters, emerge as practical alternatives, retaining efficiency while ensuring low operational costs and quicker response times. By integrating optimization technologies, SLMs can operate efficiently on less powerful hardware without compromising security. In many business scenarios, the specific tasks do not necessitate the extensive capabilities of LLMs, making SLMs more suitable for targeted, context-specific applications. Thus, SLMs address the industry’s pace toward incorporating AI solutions while maintaining necessary quality and cost-effectiveness.
👉 Pročitaj original: CIO Magazine
