Nvidia Exemplar Cloud: Lessons for Unlocking Performance on AI Infrastructure
AI-generated content may summarize information incompletely. Learn more Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We routinely see 8% to 12% gaps between partner deployments and the corresponding NVIDIA reference architecture (RA) on the same workload, same model, same global batch size.
- ▪AI-generated content may summarize information incompletely.
- ▪Learn more Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput.
- ▪We routinely see 8% to 12% gaps between partner deployments and the corresponding NVIDIA reference architecture (RA) on the same workload, same model, same global batch size.
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| Original publisher | NVIDIA Technical Blog |
| Canonical URL | https://developer.nvidia.com/blog/nvidia-exemplar-cloud-lessons-for-unlocking-full-performance-on-ai-infrastructure/ |
| Publication time | Wed, 12 Aug 2026 13:19:35 +0000 |
| Retrieval time | 2026-08-12T13:21:36.000Z |
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Data Center / Cloud English한국어中文 NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure Jul 30, 2026 By Emily Potyraj, Pavan Sridhar, Sriharsha Niverty, Suryakant Patidar and Charlie Huang Like Discuss (0) L T F R E AI-Generated Summary Like Dislike Material differences in training throughput across clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems result primarily from compounded configuration gaps at the kernel, hypervisor, BIOS, and NVIDIA Collective Communications Library (NCCL) levels, frequently causing deployments to miss the 95% threshold for NVIDIA Exemplar Cloud validation.Four real-world case studies highlight recurring sources of performance loss: missing SMMU capabilities and improper virtualization configuration on…
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