5 Misunderstandings About Enterprise AI Training Infrastructure

This post addresses common misunderstandings in enterprise AI training infrastructure, emphasizing that "useful work per GPU hour" is a more critical KPI than simply finishing a job. It highlights the importance of Model FLOPs Utilization (MFU) over raw speed, the scaling laws of bottlenecks in general-purpose infrastructure, and that cost overruns often stem from factors beyond just GPU line items, such as retries and data movement. The post advocates for purpose-built AI clouds that offer observability, transparency in cost, and reliable execution, contrasting this with the limitations of general-purpose clouds. It also stresses the importance of expert support for minimizing downtime and rework in large-scale training.