# Same Cluster, 33 Points More Utilization: What Changed Was the Order

DevFeed: [Same Cluster, 33 Points More Utilization: What Changed Was the Order](<https://devfeed.tech/articles/same-cluster-33-points-more-utilization-what-changed-was-the-order-6996.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Dharma-AI/gpu-management-pt2>)

Author: Gabriel Pimenta de Freitas Cardoso; Breno de Almeida Beleza; Francisco de Almeida Rocha Alves; Bruno Duarte

Published: 2026-08-17T19:46:21Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [GPU](<https://devfeed.tech/topics/gpu.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [jobs](<https://devfeed.tech/topics/jobs.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>)

Tags: [batch](<https://devfeed.tech/tags/batch.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [comparison](<https://devfeed.tech/tags/comparison.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

The article describes a constraint-aware GPU allocator and compares it with a FIFO scheduler across seven benchmark scenarios. On identical hardware and workloads, the allocator increased GPU utilization by up to 33 percentage points and priority-weighted output by up to 105%. It explains how training, batch inference, quantization, and real-time inference impose different scheduling constraints, especially under contention.

## Source excerpt

We built a constraint-aware GPU allocator and benchmarked it against a FIFO scheduler across seven benchmark scenarios. On identical hardware, running identical workloads, GPU utilization rose by as much as 33 percentage points, and priority-weighted output rose in every one of them, by as much as 105%. Nothing about the hardware changed. What changed was the order in which allocation decisions get made. One note on measurement before the numbers start.