👉 Aquarium computing is an innovative approach to artificial intelligence and machine learning that leverages the massive computational resources of large-scale data centers, often referred to as "aquariums," to train and deploy AI models at unprecedented scales and speeds. These data centers, originally built to house massive server farms for cloud computing, are repurposed to support the intensive training of deep learning models, particularly those used in natural language processing, computer vision, and reinforcement learning. By utilizing the parallel processing power of thousands of interconnected servers, aquarium computing enables researchers and developers to train complex models on vast datasets that would be impractical or impossible to handle with traditional computing infrastructure. This approach not only accelerates the development and deployment of AI technologies but also significantly reduces the energy consumption per training iteration, making it a sustainable and efficient solution for advancing AI capabilities.
