Understanding Small Language Models (SLMs)
Small Language Models (SLMs) are compact versions of large language models (LLMs), ranging from 100 to 1,000 times smaller. They utilize smaller datasets, requiring less training time and reducing operational costs. SLMs operate efficiently offline, enhancing security by processing sensitive data locally, making them ideal for compliance with data privacy regulations. Their swift inferencing capabilities allow them to run on edge devices like smartphones and tablets.
SLMs are typically tailored for specific tasks, such as creating summaries, translating requests, or categorizing help-desk tickets. In contrast, LLMs, with hundreds of billions of parameters, demand substantial computational resources, making SLMs a more feasible solution for specialized applications. Their focused training results in faster, less expensive deployment and streamlined management. By maintaining compliance and security while achieving efficiency, SLMs represent a valuable innovation in the AI landscape, catering to specific needs without the complexities of LLMs.
