GPU hardware is scarce, expensive, and hostile to fast CI/CD. Yet every component in the NVIDIA Kubernetes GPU stack, device plugins, DRA drivers, GPU Operator, depends on NVML, the user-space C library that talks to the driver. What if you could swap it for a drop-in mock that makes nvidia-smi, and any Go binary linked against go-nvml, believe it runs on a DGX A100, on your laptop, in a kind cluster, or in a GitHub Actions runner with zero GPUs?
This talk introduces nvml-mock, an open-source, YAML-configurable mock of libnvidia-ml.so implementing 89 NVML entry points via an auto-generated CGo bridge. We walk the architecture, C shim, a singleton Go engine with handle tables and reference-counted Init/Shutdown, and YAML device profiles for A100, GB200, and custom topologies, then show the same production binary tested end-to-end against simulated multi-GPU nodes. Finally, we show how to build your own GPU-aware CI pipelines without provisioning a single GPU.