By Elizabeth Goodman
Publication Date: 2026-09-22 17:16:00
AI factories are power-limited systems that deliver maximum value when fully optimized. GPU workload placement is a key optimization. Poor workload placement fragments topology domains and forces traffic across shared links, reducing throughput, raising job costs, and leaving GPUs consuming provisioned power while waiting on data without advancing the workload.
GPUs exchange data continuously during training and inference, so distributed workloads benefit from communication locality. NVIDIA NVLink and NVLink Switch provide high-bandwidth, all-to-all scale-up connectivity within rack-scale GPU domains, while NVIDIA Spectrum-X Ethernet provides predictable, low-latency scale-out networking across systems and racks.
A scheduler can place workloads efficiently only with a current, accurate view of those GPU and fabric relationships, and keeping that view current as the cluster changes is where placement breaks down in practice.
NVIDIA Topograph solves that problem. It discovers cluster topology from cloud APIs or on-premises fabric systems, normalizes it into a common model, and publishes it in the format each workload manager expects: Kubernetes node labels, Slurm topology configuration, or Slinky ConfigMaps. Within the NVIDIA DSX OS cluster orchestration layer, Topograph works alongside Dynamic Resource Allocation (DRA) and KAI Scheduler to enable topology-aware gang scheduling across AI factory infrastructure.
This post walks through deploying Topograph and…