Performance gaps of 8–12% between partner AI cluster deployments and NVIDIA reference architectures often stem from compounding configuration issues across multiple stack layers. Four real-world case studies are examined: (1) SMMU serialization overhead in virtualized GB200 NVL72 deployments running MoE workloads, fixed by enabling CMDQV/VCMDQ; (2) H100 clusters losing throughput to CPU C-state misconfiguration and NUMA misbinding, recovered via C-state tuning and cpuset isolation; (3) GB300 NVL72 under-utilizing 1.6 Tbps ConnectX-8 fabric due to low NCCL queue-pair concurrency, fixed by setting NCCL_IB_QPS_PER_CONNECTION=4; and (4) a B200 deployment where NCCL topology files were not propagated into enroot containers, causing 13–53% slowdowns. A preflight checklist covering GPU health, VM readiness, CPU placement, runtime topology, and fabric collectives is provided to help engineers identify issues before full-scale validation runs.
