AI inference — GPT2
Artificial intelligence and machine learning have enabled us to create applications with magical abilities. GPT-2 is a powerful language model developed by OpenAI, creators of ChatGPT, that excels at generating human-quality text. This is a critical AI workload because it tests the efficiency and ability of AI systems in handling complex language tasks that are important for many real-world applications. We measure the time it takes to generate 100 stories that begin with the prompt “Once upon a time.”
AI inference — stable adoption
Stable Diffusion stands out as the second Rockstar AI workload. This state-of-the-art image generation model creates high-quality, detailed visuals from text descriptions. Its ability to generate realistic images has wide-ranging implications for industries such as art, design, advertising, and media, enabling innovative content creation and enhanced visual storytelling. We measure the time it takes to generate one image for the prompt “Photo of an astronaut riding a horse on Mars.”
AI/Inference — Image upscaling
Topaz Photo AI is the best tool for AI-driven image processing. Improve image resolution and quality by intelligently enhancing details and reducing noise. This advanced feature increases the sharpness and sharpness of your assets, making it a valuable tool for anyone who needs high-resolution images. In our tests, we report the time it takes to upscale a 1.5 megapixel image to 22 megapixels.
AI Training — Natural Language Processing
Natural language processing (NLP) involves training AI models to understand and generate human language. NLP applies algorithms and machine learning techniques to analyze and interpret textual data, enabling tasks such as translation, sentiment analysis, and text generation. We measure the time it takes to train a BERT language model by collecting reviews from movie critics.
AI training — image classification
Image classification is a critical AI workload because recognizing objects in images is essential for many practical applications such as autonomous driving, facial recognition, medical imaging, and inventory management. This process involves using algorithms to analyze and extract features from images. During training, these methods learn patterns and attributes from labeled datasets. In our tests, we train a model using thousands of images to classify clothing photos into categories such as “T-shirts,” “bags,” and “pullovers.”
