Gartner's report suggests small language models (SLMs) are advantageous for generative AI due to their ease of fine-tuning, efficiency in serving, and manageability. The report details the evolution of model sizes, indicating a growing disparity in parameter counts—large models like GPT-4 and Gemini 1.5 have hundreds of billions to several trillion parameters, while SLMs have under 10 billion. This size difference affects training costs and deployment, impacting decision-makers considering AI solutions. However, SLMs may have knowledge gaps due to their reduced training datasets.
"Small language models (SLMs) offer a potentially cost-effective alternative for generative artificial intelligence (GenAI) development and deployment because they are easier to fine-tune and serve."
"Gartner notes that estimates for the largest models have parameters in the range of half a trillion to two trillion, while smaller models fall under 10 billion parameters."
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