NVIDIA NCP-AII case studies
NVIDIA NCP-AII · Put your knowledge to work in context.
The NVIDIA Certified Professional - AI Infrastructure (NCP-AII) is a professional credential aimed at engineers who build, operate, and troubleshoot GPU-accelerated data-center environments. It suits data-center and HPC engineers, AI platform and MLOps infrastructure staff, systems administrators moving into accelerated computing, and field or support engineers who deploy NVIDIA-based clusters for internal or customer workloads. The NVIDIA NCP-AII case studies are 5 cases and about 2,561 words.
1Eight-GPU Node Fails Enumeration After Rack Integration
You are integrating four HGX H100 8-GPU servers into a single 42U rack. Each node has 8 SXM5 H100 GPUs, two Xeon Platinum CPUs, 8 NVMe drives, and four 3000 W PSUs; measured maximum node draw is 10.2 kW.
575 words, about 3 min2MIG Layout Cannot Serve a Mixed Inference and Training Workload
You run a shared 8-GPU A100 80GB SXM node with NVLink. The inference team wants seven isolated 10 GB MIG slices per GPU for small models, and has already enabled MIG with the 1g.10gb profile on all eight GPUs.
507 words, about 3 min3NCCL Throughput Collapses on a Rail-Optimized InfiniBand Fabric
You operate a 32-node cluster, each node with 8 H100 GPUs and 8 NDR InfiniBand compute NICs in a rail-optimized fat tree: GPU i uses mlx5 i on rail i. Two additional 400G NICs per node serve storage.
521 words, about 3 min4Checkpoint Stalls on an NFS-Backed Training Cluster
You support a 64-node, 512-H100 training cluster running a 175B-parameter model. Every 30 minutes the job writes a 2.4 TB checkpoint and must finish in under 5 minutes to avoid stalling the optimizer.
483 words, about 2 min5GPU Operator Rollout Breaks MIG Scheduling
You manage a 16-node Kubernetes cluster with NVIDIA GPU Operator 23.9, driver 535, CUDA 12.2, and containerd. Eight nodes are A100 80GB systems with MIG enabled and advertise nvidia.com/mig-1g.10gb.
475 words, about 2 min