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Tools · Updated 9 Oct, 08:50 pm IST

AI2 replaces priority scheduler with budgeted fair-share GPU scheduling

Image: Hugging Face Blog

Why it matters for readers: It explains how a research lab decides which AI projects get expensive GPU time so more important work gets done.

  • AI2 moved from a priority-based scheduler to a system using GPU time budgets, hierarchical fair-share allocation, and a time-slicing contract.1
  • The new approach aims to increase the 'impact' metric by choosing the most valuable workloads for resources rather than deciding case-by-case.1
  • AI2 operates clusters of thousands of NVIDIA H100, B200, and B300 GPUs in sizes from 88 to 1,024 GPUs to support large distributed training.1
  • The scheduling change reframes allocation debates as an administrative budgeting process instead of operational ad-hoc decisions.1
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