Impactful scheduling for GPU clusters
On the AI Infrastructure team at Ai2, we’re responsible for providing the institute’s GPU compute capacity, specifically targeting large, distributed training workloads.
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Key points
- Next is impact: how often the most valuable workloads are chosen to receive resources.
- The capstone of the pyramid is utilization: the fraction of GPU capacity used over the lifetime of a workload.
- At Ai2, we manage thousands of NVIDIA H100, B200, and B300 GPUs arranged in clusters ranging in size from 88 to 1024 GPUs.
- One way to think about this is that every available GPU hour on our cluster has 2-3 different research workloads competing for it.
Sources (1)
- [1]Impactful scheduling for GPU clustersHugging Face Blog · Oct 9, 03:20 PM
On the AI Infrastructure team at Ai2, we’re responsible for providing the institute’s GPU compute capacity, specifically targeting large, distributed training workloads.
Next is impact: how often the most valuable workloads are chosen to receive resources.
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