GPU Autoscaling on Kubernetes — Karpenter, KAI Scheduler, Gang Scheduling

Three layers, not one. Karpenter provisions nodes dynamically (under one minute, 40% faster than Cluster Autoscaler). KAI Scheduler handles gang scheduling, topology awareness, and hierarchical queues — it prevents the 7-of-8 partial allocation trap where seven nodes wait and burn on one missing GPU. Application-level autoscalers (NVIDIA Dynamo Planner, llm-d Workload Variant Autoscaler) scale on inference-specific signals — queue depth, KV cache utilization — not CPU/DCGM duty cycle. The classic HPA trap is that `DCGM_FI_DEV_GPU_UTIL` is a duty-cycle measurement: 100% could be 10 requests or 100. vLLM pre-allocates KV cache memory, so memory never triggers scale-down. This lesson teaches you to compose the three layers and avoid the default Karpenter `WhenEmptyOrUnderutilized` policy that terminates running GPU jobs mid-inference.

Prerequisites: Phase 17 · 02 (Inference Platform Economics), Phase 17 · 04 (vLLM Serving Internals)