Originally published at: https://developer.nvidia.com/blog/leading-mlperf-inference-v3-1-results-gh200-grace-hopper-superchip-debut/
AI is transforming computing, and inference is how the capabilities of AI are deployed in the world’s applications. Intelligent chatbots, image and video synthesis from simple text prompts, personalized content recommendations, and medical imaging are just a few examples of AI-powered applications. Inference workloads are both computationally demanding and diverse, requiring that platforms be able…
Hi! I’m trying to run MLPerf on my A100 GPU with MIG mode. I follow the document and have some questions…
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I hope to run these benchmarks on MIG slices as described on this page, but I didn’t find scripts/launch_heterogeneous_mig.py in the repo.
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This implementation uses benchmark configuration for each benchmark, for example, the server mode of resnet50. I wonder if you have a document to introduce each field with more detail? It can help me understand the meaning of every field and adjust them for my experiment.