This post explores the use of efficient GPU orchestration for distributed training in MLOps, highlighting how GPUs can significantly boost performance at scale. It delves into key technical considerations such as system setup, orchestration strategies, and performance optimization for scaling modern machine learning workloads. Additionally, it discusses the challenges and benefits of enabling GPU support in Kubernetes for large-scale AI and ML operations, emphasizing the importance of optimizing GPU utilization and performance tuning for cost-effective infrastructure use.