Optimizing AI Compute: How to Scale Infrastructure on NVIDIA
NVIDIA has published a method for getting the most out of the GB200 NVL72: with topology-aware scheduling in Slurm, a single rack reaches exascale compute for trillion-parameter models. Performance depends not only on the hardware, but also on how jobs are distributed across the cluster.
AI-processed from NVIDIA Developer Blog; edited by Hamidun News
When AI is deployed at scale, infrastructure efficiency depends not only on the hardware but also on where workloads are placed. NVIDIA has released a guide to using the GB200 NVL72 with the Slurm scheduler, which takes network topology into account to distribute compute optimally across distributed clusters.
How do you optimize compute when scaling AI?
Use a job scheduler that accounts for network topology to distribute compute optimally, as shown in NVIDIA's guide for the GB200 NVL72.
What performance does the NVIDIA GB200 deliver?
The GB200 NVL72 packs exascale compute (10^18 operations per second) into a single rack, making it possible to run trillion-parameter models.
Key Questions Answered
How do you optimize compute when scaling AI?
Use a job scheduler that accounts for network topology to distribute compute optimally, as shown in NVIDIA's guide for the GB200 NVL72.
What performance does the NVIDIA GB200 deliver?
The GB200 NVL72 packs exascale compute (10^18 operations per second) into a single rack, making it possible to run trillion-parameter models.
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