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Paper dissects GPU utilization for LLM inference on Nvidia Hopper

By PulseAugur Editorial · [1 sources] ·

A new paper explores the intricacies of GPU utilization for Large Language Model (LLM) inference on Nvidia Hopper architecture. The research highlights that a single utilization percentage can be misleading, masking inefficiencies during decode operations where small-batch requests only partially fill compute fragments. The study proposes using eight distinct, counter-validated views derived from Nsight Compute reports to map utilization gaps to specific mechanisms like fragment fill, occupancy limits, and kernel selection. AI

IMPACT Provides deeper insights into optimizing LLM inference performance on high-end GPUs, potentially leading to more efficient deployments.

RANK_REASON The cluster contains an academic paper detailing technical research on LLM inference performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Paper dissects GPU utilization for LLM inference on Nvidia Hopper

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33 / 100
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The cluster contains an academic paper detailing technical research on LLM inference performance. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, infra
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammad Siavashi, Gerald Q. Maguire Jr., Dejan Kostic, Marco Chiesa ·

    Dissecting GPU Utilization for LLM Inference on Nvidia Hopper

    arXiv:2609.12923v1 Announce Type: cross Abstract: A single SM utilization percentage can make an LLM inference workload look compute-saturated while hiding how much useful work is being done. The problem is not that the counter is wrong, but that it collapses several different me…

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