MLPerf Training v3.1 measures the time to train models across nine different use cases, including large language models (LLMs), image generation, computer vision, medical image segmentation, speech recognition, and recommendation.
MLPerf Inference v3.1 measures inference performance using seven different kinds of neural networks, including LLMs, natural language processing, computer vision, and medical image segmentation.
MLPerf HPC v3.0 measures training performance across four different scientific computing use cases, including climate atmospheric river identification, cosmology parameter prediction, quantum molecular modeling, and protein structure prediction.