You’ve built your model, your training code is containerized, and you’re ready to scale up on Google Kubernetes Engine (GKE). You go to provision your nvidia-h100-80gb node pool and... QUOTA_EXCEEDED.
It’s one of the most common (and frustrating) roadblocks in modern AI development. High-end accelerators like H100s, A100s, and TPUs are in massive demand, and securing permanent, on-demand quota for them can be difficult. But a lack of on-demand quota doesn't mean you're out of options.
GKE provides two powerful, cost-effective strategies for acquiring these scarce resources when you can't get standard, on-demand instances: Spot VMs and the Dynamic Workload Scheduler (DWS).
Let's break down what they are, when to use each, and how to implement them.
Strategy 1: Spot VMs
