NVIDIA GPU Troubleshooting: 4 Diagnostic Gates | NCP-AII Study

A GPU can appear healthy on the host and still be unavailable to a Kubernetes workload. This video explains a four-gate approach to NVIDIA GPU troubleshooting, with NCP-AII certification preparation in mind. Follow the diagnostic path from hardware discovery to application execution, separating GPU driver issues, resource allocation problems, container runtime configuration, and CUDA dependencies. The video covers four diagnostic gates: 1. Host and driver: Check PCIe device discovery with lspci and driver communication with nvidia-smi. 2. Scheduler allocation: Examine Kubernetes device plugins, GPU resource requests, and Multi-Instance GPU (MIG) configuration. 3. Runtime integration: Verify NVIDIA Container Toolkit integration, device visibility, and container access. 4. Application execution: Investigate CUDA dependencies, container images, and application compatibility. We also discuss collecting diagnostic evidence before rebooting, correlating NVIDIA DCGM telemetry with BMC events, and examining GPU behavior under load. The central principle: hardware discovery, scheduler allocation, container access, and successful CUDA execution each require separate validation. Passing one gate does not prove the entire stack is working. CHAPTERS 00:00 Introduction: GPU troubleshooting gates 01:16 The GPU troubleshooting trap 02:19 Gate 1: Host and driver 03:19 Gate 2: Scheduler allocation 04:20 Gate 3: Runtime integration 05:39 Gate 4: Application execution 06:37 Automating GPU observability For learners preparing for NVIDIA-Certified Professional: AI Infrastructure (NCP-AII), this explainer connects GPU infrastructure concepts to practical troubleshooting scenarios. Subscribe to AI Infra Bites for more NVIDIA infrastructure explainers and NCP-AII study content. Independent educational content. The four-gate framework is a teaching aid. Not affiliated with or endorsed by NVIDIA. #NCPAII #GPUTroubleshooting #NVIDIA #Kubernetes #AIInfrastructure