The evolution of large model architectures from RNNs to GPT represents a fundamental shift in AI capabilities. This overview highlights key milestones such as the transition from recurrent networks to transformers, the scaling laws that enabled GPT's success, and the architectural innovations that followed. Understanding this trajectory helps engineers appreciate why current models behave as they do and anticipate future directions. While the article covers familiar ground, it serves as a concise reference for those new to the field or needing a refresher on the lineage of modern AI.
This article traces the evolution from RNNs to GPT, covering key milestones in large model architecture. It provides a useful historical context for understanding current AI trends, though the content is not groundbreaking.