AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models
Code and container definitions for the AI application benchmarking framework presented in "AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models" ( https://arxiv.org/abs/2603.16164 ). The framework measures throughput and energy efficiency of representative computer vision workloads (ResNet-50, ViT-L/16, Stable Diffusion v2) and large language model workloads (LLaMA 3 8B pre-training with LitGPT and inference serving with SGLang) under systematic GPU power-cap sweeps. Includes Apptainer container definitions and configurations used to evaluate NVIDIA H100, NVIDIA H200, and AMD MI300X accelerators on a pe
Resource information
- Format
- zip
- Original repository
- 20083679
- DOI
- 10.5281/zenodo.20083679
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- cc-by-4.0
- Last record update
- Nov 5, 2026
About this resource
Code and container definitions for the AI application benchmarking framework presented in "AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models" ( https://arxiv.org/abs/2603.16164 ). The framework measures throughput and energy efficiency of representative computer vision workloads (ResNet-50, ViT-L/16, Stable Diffusion v2) and large language model workloads (LLaMA 3 8B pre-training with LitGPT and inference serving with SGLang) under systematic GPU power-cap sweeps. Includes Apptainer container definitions and configurations used to evaluate NVIDIA H100, NVIDIA H200, and AMD MI300X accelerators on a per-node basis. An updated version with extended code and additional experiments will be maintained at https://github.com/RRZE-HPC/hpc-ai-perf-bench .