The article outlines key AI workloads across inference and training tasks, highlighting their significance and practical applications. For AI inference, GPT-2 demonstrates advanced natural language generation by producing human-quality text, tested by timing the creation of 100 stories from a fixed prompt. stable Diffusion showcases cutting-edge image generation from textual descriptions, with performance measured by the time to generate a single complex image. Additionally, Topaz Photo AI exemplifies AI-driven image upscaling, enhancing resolution and clarity, evaluated by upscaling a 1.5-megapixel image to 22 megapixels.

On the AI training side, the article covers natural language processing (NLP) and image classification. NLP training involves teaching models like BERT to understand and generate language, tested by training on movie review datasets for tasks such as sentiment analysis. Image classification training focuses on teaching models to recognize and categorize objects within images, critical for applications like autonomous driving and medical imaging, demonstrated by training on thousands of labeled clothing images. These workloads collectively illustrate the broad capabilities and real-world relevance of AI systems in language and vision domains.