AI Inference — GPT2

Artificial Intelligence and Machine Learning have enabled us to create applications that are almost magical in their abilities. GPT-2 is a powerful language model developed by OpenAI, the makers of ChatGPT, that excels at generating human-quality text. It's a key AI workload because it tests the efficiency and capability of AI systems in handling complex language tasks, which is crucial for many real-world applications. We are measuring the time it takes to generate 100 stories starting with the "Once upon a time, there was a" prompt.

AI Inference — Stable Diffusion

Stable Diffusion stands out as the second rock star AI workload. This cutting-edge image generation model creates high-quality, detailed visuals from textual descriptions. Its ability to produce realistic images has wide-ranging implications for industries such as art, design, advertising, and media, enabling innovative content creation and enhancing visual storytelling. We are timing how long it takes to generate a single image for the prompt "a photo of an astronaut riding a horse on Mars."

AI / Inference — Image Upscaling

Topaz Photo AI is a premier tool for AI-driven image processing. It enhances image resolution and quality by intelligently increasing detail and reducing noise. This advanced capability improves the clarity and sharpness of assets, making it a valuable tool for anyone needing high-resolution images. Our test reports how long 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 in understanding and generating human language. By applying algorithms and machine learning techniques to analyze and interpret text data, NLP enables tasks like translation, sentiment analysis, and text generation. We are measuring how long it takes to train a BERT language model with a collection of movie critic reviews.

AI Training — Image Classification

Image classification is a crucial AI workload because recognizing objects within images is essential for many practical applications, including 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. Our test involves training a model with several thousand images to classify photos of clothing into categories such as "T-Shirt," "Bag," and "Pullover."