Dissecting GPU Utilization for LLM Inference on Nvidia Hopper
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 this paper, we profile vLLM with FlashAttention-3 and cuBLASLt on an H100 NVL across cold prefill, ...
| Authors | Mohammad Siavashi, Gerald Q. Maguire, Dejan Kostic et al. |
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| Primary category | cs.PF |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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