Which characteristic of AI workloads necessitates the use of distributed computing?

AI tasks benefit significantly from parallel processing, which is a fundamental characteristic that drives the need for distributed computing. Many AI workloads, such as training deep learning models, involve large amounts of data and complex computations that can be processed simultaneously. By distributing these tasks across multiple computing resources, such as GPUs or nodes in a cluster, the overall computation time can be drastically reduced. This parallelization allows for handling bigger datasets and more sophisticated models that would be impractical to process on a single machine.

In contrast, the other characteristics do not support the rationale for using distributed computing in AI workloads. Some AI tasks may require substantial memory and computational complexity, which is contrary to the notion that they require less memory or are simpler to compute. Additionally, while specialized hardware can enhance performance, many AI tasks significantly leverage parallel processing capabilities more than the use of specialized hardware itself. Therefore, the emphasis on parallel processing as a cornerstone of AI workloads underlines the necessity for distributed computing in effectively managing and optimizing these tasks.