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slug: "impactful-scheduling-for-gpu-clusters-91fb66"
title: "Impactful scheduling for GPU clusters"
description: "Ai2 says it replaced a priority-based GPU scheduler with a system combining GPU-time budgets, hierarchical fair-share allocation and a workload minimum-runtime contract."
retrieval_nugget: "Ai2 says it replaced a priority-based GPU scheduler with a system combining GPU-time budgets, hierarchical fair-share allocation and a workload minimum-runtime contract."
observed_at: "2026-10-10"
record_date: "2026-10-10"
date_kind: "observed_at"
why_it_matters: "Ai2 says it replaced a priority-based GPU scheduler with a system combining GPU-time budgets, hierarchical fair-share allocation and a workload minimum-runtime contract. The institute says its clusters contain thousands of NVIDIA H100, B200 and B300 GPUs and serve about 150 researchers; submitted workloads request two to three times the available capacity. Under the new model, managers allocate ti"
novelty: "notable"
verification_level: "source-linked"
signal_type: "research"
source_platform: "huggingface.co"
topics: []
entities: ["Ai2","NVIDIA H100","NVIDIA B200","NVIDIA B300","Hadoop Fair Scheduler","SLURM","YARN Fair Scheduler"]
related_patterns: []
source_url: "https://huggingface.co/blog/allenai/impactful-scheduling"
source_urls: ["https://huggingface.co/blog/allenai/impactful-scheduling"]
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status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Impactful scheduling for GPU clusters

## Retrieval answer

Ai2 says it replaced a priority-based GPU scheduler with a system combining GPU-time budgets, hierarchical fair-share allocation and a workload minimum-runtime contract.

Ai2 says it replaced a priority-based GPU scheduler with a system combining GPU-time budgets, hierarchical fair-share allocation and a workload minimum-runtime contract. The institute says its clusters contain thousands of NVIDIA H100, B200 and B300 GPUs and serve about 150 researchers; submitted workloads request two to three times the available capacity. Under the new model, managers allocate time budgets through a research hierarchy, and workloads must be funded by a budget to be protected from preemption. The scheduler uses a seven-day default lookback window to compare occupancy with allocations. Jobs may use unallocated capacity, but that work is preemptible. Researchers declare a minimum runtime during which a job is protected; after that, it may be rebalanced and resumable workloads requeued. Ai2 reports that the redesign addresses priority inflation, GPU squatting and operational friction around long-running jobs. One researcher describes the result as feeling like 30% more compute; this is a quoted user assessment, not an independently measured system-wide gain. Source: https://huggingface.co/blog/allenai/impactful-scheduling

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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