What is the primary difference between infrastructure requirements for training and inference in AI architectures?

In AI architectures, the primary difference between the infrastructure requirements for training and inference is that training typically requires more computational resources and memory bandwidth. This stems from the nature of the training process, which involves fitting a model to a large dataset by performing numerous calculations through multiple iterations. During training, a model's parameters are continuously optimized, which demands significant computational power, often utilizing high-performance GPUs or TPUs to handle the larger matrix operations effectively.

Additionally, training necessitates higher memory bandwidth to accommodate the vast amounts of data being processed and the intermediate results produced at each step of the learning algorithm. In contrast, inference, which is the process of making predictions using a trained model, usually involves simpler calculations and lower demands on computational resources, allowing it to be executed more efficiently, sometimes even on less powerful hardware like edge devices.

This understanding underscores the fundamental resource differences between these two critical phases of AI model development and deployment.