We've deployed GPU clusters for LLM training, inference serving, and scientific computing. The teams that struggle most aren't the ones who bought cheap GPUs — they're the ones who didn't answer the infrastructure questions before placing the order.
1. Fabric topology first. H100 SXM5 nodes have 8 GPUs each. For all-to-all communication (essential for training), you need InfiniBand NDR or RoCE v2 — not plain Ethernet. Map your fat-tree topology before anything else. This high-throughput interconnect setup is a cornerstone of our AI Infrastructure & HPC design protocols.
3. Power budget. An 8-GPU H100 node can draw 10.2kW under full load. A 16-node cluster needs ~163kW of PDU capacity. Is your data center provisioned for this? What's your PUE? 3. Cooling. Air cooling for 10kW+ nodes is possible but requires careful hot-aisle containment. Direct liquid cooling (DLC) is becoming the norm. Know which your facility supports. These requirements are standard in modern Data Center Services.
4. Storage fabric. Model checkpoints and training datasets are large. You need high-throughput shared storage — NVMe-oF over RDMA or parallel filesystems (GPFS, Lustre, VAST). Plan for 200GB/s aggregate read throughput minimum.
5. Network segmentation. Keep GPU interconnect traffic (InfiniBand or RoCE) physically separate from management and storage networks. Mixing them destroys MFU (model FLOP utilization).
6. MLOps toolchain. Kubeflow? MLflow? Ray? Decide before deployment — the toolchain affects how you schedule jobs, manage experiments, and serve models. Retrofitting is painful.
7. Monitoring. GPU utilization, temperature, MFU, job queue depth, fabric BER — you need all of these. DCGM + Prometheus + Grafana is the standard stack.
8. Licensing. NVIDIA DGX OS and certain frameworks have licensing implications. Check before you build.
9. Security. GPU clusters often run on isolated networks. That's not a substitute for endpoint protection and access controls. Jupyter notebooks with open ports are a common attack surface. Implement custom safeguards detailed in our Cybersecurity Services to audit notebook permissions.
10. DR. What happens if a GPU node fails mid-training run? Do you have a checkpoint recovery policy? A spare node on standby? 11. Cost modelling. H100 rental vs. purchase break-even is typically 18–24 months for sustained workloads. Build a 3-year TCO model before committing. 12. Vendor support. NVIDIA Enterprise Support, or hyperscaler support? Know your escalation path before you need it at 2 AM.