Build an NVIDIA k8s Lab on an Old Gaming Laptop
๐๐๐ถ๐น๐ฑ ๐ฎ๐ป ๐ก๐ฉ๐๐๐๐ ๐ธ๐ด๐ ๐๐ฎ๐ฏ ๐ผ๐ป ๐ฎ๐ป ๐ข๐น๐ฑ ๐๐ฎ๐บ๐ถ๐ป๐ด ๐๐ฎ๐ฝ๐๐ผ๐ฝ ๐ฅ๏ธ
The guide takes you from WSL2 and Docker through Kind (K8s) and the NVIDIA GPU Operator. In the final step, you run a small LLM on the NVIDIA GPU (Ollama with a 3B model) to confirm the full stack works for real AI workloads.
๐ช๐ต๐ ๐ ๐๐๐ถ๐น๐ ๐ง๐ต๐ถ๐ ๐๐ฎ๐ฏ
If you work in an industry that is moving toward AI workloads, knowing the NVIDIA AI infrastructure stack helps. That is why I decided to study it.
Earlier in my career, I was a Red Hat Certified Instructor (RHCI), a Red Hat Certified Examiner (RHCX), and a Microsoft Certified Trainer (MCT). From that work, I learned one thing: the best way to learn a vendor’s technology is to study for and pass the certification exam.
The Associate exam โ NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) โ is not a hands-on lab. It is a timed multiple-choice test about how the parts fit together. You can pass it using slides and documentation alone, but…
๐ง๐ต๐ฒ ๐ฅ๐ผ๐น๐ฒ ๐ผ๐ณ ๐ง๐ต๐ถ๐ ๐๐ฎ๐ฏ
This lab is supplemental exam preparation, not a replacement. You still need the official study guide, documentation, and practice questions. What the lab adds is hands-on practice with NVIDIA tools โ without access to expensive data-center hardware. An old gaming laptop is enough.