Explore mechanisms for efficient GPU resource sharing in High Energy Physics and machine learning environments. Learn about GPU scheduling in Kubernetes, time-sharing techniques, and Nvidia Multi-Instance-GPU (MIG) for physical partitioning. Examine benchmark results to understand optimal workload assignment and discover strategies for centralized GPU management to ensure optimal resource utilization in continuous integration, machine learning, and batch services. This conference talk provides insights into improving overall GPU resource usage, addressing limitations in traditional deployments, and maximizing the potential of scarce and expensive accelerators.