As AI models grow in scale and complexity, realizing the full performance of modern accelerated infrastructure depends as much on how workloads are placed as on the hardware itself. NVIDIA GB200 NVL72 delivers exascale compute in a single rack, unlocking real-time trillion-parameter models. Yet capturing that performance in a shared cluster requires schedulers that understand the system architecture and align jobs with its network topology.
This post explains how Slurm topology-aware job scheduling works on NVIDIA GB200 NVL72, and provides scheduling recommendations for optimal GPU occupancy.
How does NVIDIA GB200 NVL72 deliver exascale compute?
NVIDIA GB200 NVL72 is an exascale computer in a single rack. With 72 NVIDIA Blackwell GPUs interconnected by the largest production scale-up compute fabric, NVIDIA NVLink provides 130 terabytes per second (TB/s) of low-latency GPU communication bandwidth for AI and high-performance computing (HPC) workloads. Multiple GB200 NVL72 systems combined in a cluster create hierarchical network topology with large domains of very high networking bandwidth.
An AI training job can greatly benefit from the abundant networking bandwidth offered by GB200 NVL72, when scheduled to maximize the use of NVLink fabrics. Recent results show that GB200 NVL72 delivers significant improvement in performance for all AI workloads, including training (>2.6x with recent MLPerf training), across different inference use cases (real-time inference for trillion-parameter models, >1.5 million tokens/second for the OAI gpt-oss model, state-of-art disaggregate serving), as well as reasoning.
