What is one of the main functions of a GPU in AI?

The main function of a GPU in AI is to accelerate the training of models through parallel processing. GPUs, or Graphics Processing Units, are specifically designed to handle multiple tasks simultaneously, making them exceptionally well-suited for the computational demands of training machine learning and deep learning models.

In AI, especially in deep learning, training involves performing a significant number of mathematical calculations, particularly matrix operations, which can be highly parallelizable. This means that operations can be split and computed simultaneously across many data points. Given the massive datasets often used in AI training, the parallel processing capability of GPUs allows for much faster computation compared to traditional CPUs that process tasks sequentially. This capability drastically reduces training times for complex models, enabling more efficient experimentation and development.

In contrast, the other options do not accurately describe the primary function of a GPU in AI. High-level decision-making is more associated with the algorithms and structures built on top of the GPU’s processing capabilities. Data collection and storage pertain to data management tools and systems rather than the processing hardware itself. Providing high security for data management relates to cybersecurity measures and is outside the scope of GPU functions. Thus, accelerating the training of AI models through parallel processing accurately reflects the critical role of GPUs in this field.