Keeping GPUs busy is the problem of our time.

Every idle GPU minute is wasted opportunity: training delays, inference latency, and budget burn. Modern AI infrastructure demands engineers who can align compute, network, storage, and orchestration to keep systems stable and utilized.

AI infrastructure spending is accelerating toward $500B+ per year across hyperscalers, enterprises, and sovereign initiatives. The limiting factor is a shortage of engineers who can operate NVIDIA-centric platforms end-to-end.

• Make GPUs busy (utilization, scheduling, throughput)
• Keep clusters healthy (runbooks, observability, incident response)
• Make networking fast (RDMA, InfiniBand/Ethernet tuning, NCCL)
• Make systems predictable (capacity planning, failure modes, automation)

AI infrastructure is now executive-visible. Outcomes are measurable. Downtime is expensive. The engineers who can deliver here will define standards, lead teams, and build reputations fast. History is being written, quietly, in clusters.

Subject-matter experts in NVIDIA AI Infrastructure, focused on the real day-to-day skills required to excel on the job, clear certifications, and communicate clearly in interviews.

• Practical operations and troubleshooting
• Certification readiness (without memorization)
• Interview vocabulary and system thinking
• Production patterns teams actually use

A common place to track trends and discuss what's breaking in the field: top recurring issues, failure patterns, best practices, and guided self-study topics. Bring your questions; we'll build the agenda together.