The series of blog posts from July 2025 covers various aspects of optimizing GPU utilization in Kubernetes environments, emphasizing strategies for systematic monitoring, workload prioritization, and governance to achieve significant cost reductions while enhancing AI/ML performance. The content highlights the limitations of traditional tools like NVidia-smi for GPU monitoring and advocates for DCGM combined with Kubernetes for comprehensive utilization analysis. It underscores the problem of underutilized GPU clusters that can cost organizations over $200K annually and suggests solutions to increase efficiency. The posts also explore the benefits of GPU security and isolation using MIG technology for secure multi-tenancy and effective workload management, along with CRIUgpu, a solution for zero-downtime live migration of CUDA workloads, enabling seamless checkpointing and eliminating expensive restarts.