Kubernetes Scheduler Plugins: Optimizing AI/ML Workloads

Picture this: Enterprises burn $400K monthly on GPU clusters humming at 35% capacity while workloads queue endlessly outside. Why? The stock scheduler thinks GPUs are interchangeable, counting tokens — oblivious to silicon geography, workload personality, or the thundering cost-per-second of idle accelerators. What follows dissects how purpose-built scheduler plugins flip that equation. We're talking technical guts: architectural decisions, deployment mechanics, working code that actually ships. No hand-waving. Just the machinery needed to make GPUs earn their keep.