Intelligence Update
Advances in fine-tuning reduce total volume of AI training
In the early days of generative AI, much attention was focused on the process of training new models from zero. As the technology matures, organizations are increasingly looking for ways to adapt existing large language models (LLMs) to their specific requirements. Customizing existing models avoids the need to source training data, employ machine learning experts and endure long and expensive training runs.
A recent Uptime Intelligence report explored the options available to organizations that want to either customize existing models or train their own (see How AI training choices affect infrastructure costs). It demonstrates how using modern fine-tuning techniques — that further train an LLM on a smaller, specialized dataset to adapt it for specific tasks — consumes orders of magnitude fewer IT and facility resources than training a model from scratch, resulting in much lower costs.
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