Artificial Intelligence (AI) workloads drive a rapid expansion of high-performance computing (HPC) infrastructures and increase their power and energy demands towards a critical level. AI benchmarks representing state-of-the-art workloads and their understanding in the context of performance-energy trade-offs are critical to deploy efficient infrastructures and can guide energy efficiency measures, such as power limiting. We introduce a benchmarking framework with popular deep learning applications from computer vision (image classification and generation) and large language models (continued pre-training and inference) implementing modern methods. Our performance analysis focuses on throughput rather than ``time to completion'', which is the standard metric in HPC. We analyse performance and energy efficiency under various power-limit settings on NVIDIA H100, NVIDIA H200, and AMD MI300X GPUs. Our results reveal that no universal optimal power limit exists, as the efficiency peak varies across application types and GPU architectures. Interestingly, the two NVIDIA GPUs which mainly differ in their high-bandwidth memory (HBM) configuration show qualitatively different performance-energy trade-offs. Code is available on Zenodo (https://zenodo.org/records/20083679) and GitHub (https://github.com/RRZE-HPC/hpc-ai-perf-bench).

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