While large language models (LLMs) dominate the AI landscape, Small-scale Large Language Models (SLMs) are gaining traction as cost-effective and efficient alternatives for various applications. Despite their growing relevance, there is a significant gap in understanding the training behavior and computational requirements of SLMs, especially in contrast to their larger counterparts. In a new paper Computational Bottlenecks of Training Small-scale Large La