NVIDIA NCP-AIO case studies
NVIDIA NCP-AIO · Put your knowledge to work in context.
The NVIDIA Certified Professional – AI Operations (NCP-AIO) is a professional credential for engineers who build, operate, and keep AI infrastructure healthy - the people running GPU clusters, AI factories, and the software stacks on top of them. This independent study page is not affiliated with, endorsed by, or sponsored by NVIDIA; it is written for candidates who want a clear, honest map of the exam before they book it. The NVIDIA NCP-AIO case studies are 5 cases and about 3,440 words.
1Thermal-Induced XID 79 and NCCL Timeouts on an HGX H100 Cluster
You operate 32 HGX H100 nodes (8 GPUs each) across four racks, connected by NDR InfiniBand, running a 512-GPU PyTorch training job. After a facility change raised the chilled-water supply setpoint from 12 °C to 15 °C, the job aborts after about six hours with an NCCL timeout on rank 187.
736 words, about 4 min2Fabric and Scheduler Choices for a 512-GPU AI Factory Build
You are deploying a 64-node AI factory: 64 servers with 8 H100 GPUs each, for 512 GPUs, on an NDR 400 Gb/s InfiniBand compute fabric with a 25 GbE management network. The deadline is eight weeks.
699 words, about 3 min3OOM and Low Utilization in a 13B Fine-Tuning Job
You run a 13B-parameter LLM fine-tuning job on eight A100 80GB GPUs using PyTorch FSDP, bf16, sequence length 4096, micro-batch size 2, and gradient accumulation 16. Activation checkpointing is disabled.
669 words, about 3 min4NCCL All-Reduce at 40% of Peak on an InfiniBand Fabric
You operate a 128-node, 1,024-GPU training cluster with NDR 400 Gb/s InfiniBand in a rail-optimized leaf-spine fabric. Each node has eight GPUs and eight HCAs, one per rail.
658 words, about 3 min5Containerized Training Fails Above 8 Nodes with NCCL Unhandled System Error
You run a 16-node A100 cluster with 8 GPUs per node. Hosts use NVIDIA driver 535.104.05, CUDA 12.2, MLNX OFED 5.8, and Ubuntu 22.04.
678 words, about 3 min