SOURCE-LINKED INTELLIGENCE

Dissecting GPU Utilization for LLM Inference on Nvidia Hopper

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

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 mechanisms into one number. This is most severe during decode, where each request contributes only one new token and dense projection GEMMs become small-row matrix multiplications. On Hopper, the bfloat16 GMMA path executes these operations in fixed 64-row matrix fragments, so small-batch decode can fill only a small fraction of each fragment with real token rows. In th

Read original source ↗ Open in workspace

recordType

paper

region

Global