The series explores the under-utilization of GPU clusters, offering insights into measuring and improving utilization, with particular focus on optimizing different types of machine learning workloads. Training workloads benefit from checkpoint/restore strategies that enable the use of cost-effective compute options like spot instances, while real-time inference workloads require right-sizing strategies to balance resource efficiency and performance, such as memory-based right-sizing and replica optimization. Advanced resource-sharing strategies, such as Multi-Instance GPU (MIG) technology, allow multiple workloads to share a single GPU with improved utilization and security. Ancillary workload optimization addresses the often overlooked CPU-intensive preprocessing, network data transfer, and sidecar container functions that can bottleneck GPU efficiency, suggesting strategies like strategic CPU allocation, network and storage optimization, and sidecar resource management to enhance overall efficiency.