Which AI architecture typically requires higher memory bandwidth during its operation?

The choice of training as the option that typically requires higher memory bandwidth is correct because training AI models involves processing large datasets to adjust the model parameters continuously. This process often requires significant amounts of data to be moved in and out of memory quickly. During training, not only is the model itself updated based on the incoming data, but also, many operations such as gradient calculations and backpropagation demand high throughput to handle the extensive matrix multiplications and other computations effectively.

The intensive calculations associated with optimizing weights necessitate a system that can read and write data at high speeds, making memory bandwidth a critical factor. In contrast, inference, data preprocessing, and model deployment involve less frequent updates and adjustments to model parameters, which typically do not require the same level of intensive memory usage as training does. Therefore, training is the architecture where higher memory bandwidth is crucial for efficient operation.