칼럼 | GPU 사용률이 낮다고 낭비일까? 보안 AI 학습에서 핀옵스가 놓치는 함정

Low GPU utilization in privacy-preserving AI workloads does not necessarily indicate waste, as FinOps-driven cost optimization may overlook critical performance bottlenecks such as memory constraints and security requirements that naturally limit hardware efficiency. CIOs must diagnose actual infrastructure bottlenecks before accepting automated rightsizing recommendations, as premature cost-cutting could compromise both AI model performance and security compliance. This requires a balanced approach where IT organizations understand the trade-offs between cost optimization and the legitimate infrastructure needs of secure AI deployments.