This series explores the under-utilization of GPU clusters, offering insights into measuring and enhancing usage, with a particular focus on security and isolation. It highlights the importance of effective GPU resource management, which provides security benefits crucial for organizations deploying GPU workloads across teams and projects. Multi-Instance GPU (MIG) technology is discussed as a key solution, providing hardware-level isolation and enabling secure multi-tenancy by creating isolated GPU instances with dedicated memory and compute resources. The series outlines various multi-tenancy patterns based on organizational needs, such as department-level isolation, team-level sharing, and project-level optimization, to prevent resource conflicts and ensure security boundaries. It also emphasizes security considerations for GPU workloads, such as protecting models, data isolation, access controls, and maintaining audit trails for compliance and monitoring. Additionally, an upcoming workshop with NVIDIA is mentioned, focusing on GPU utilization in Kubernetes environments.