While both AI and QM simulations extensively need the power of modern computing, the nature of computations involved in each field and their respective challenges in reaching exascale performance are fundamentally different. AI, particularly machine learning (ML), has exhibited significant progress in recent years due to its ability to extract patterns and learn complex relationships from data . The success (see Fig. (a)) is mainly due to the availability of large datasets, advancements in algorithm development, and the accessibility of HPC resources, especially those equipped with GPUs and tensor processing units (TPUs). The computations involved in AI training primarily revolve around optimizing the weights and biases of a neural network to minimize a specific loss function, which measures the difference between predicted and actual target values. Such tasks often involve reduced-precision matrix multiplications, convolutions, and activation function evaluations. Optimization techniques like stochastic gradient descent and its variants are also widely employed, involving iterative updates to network parameters based on gradients of the loss function calculated using backpropagation . These computations are inherently parallel, as the gradients for different data points can be computed independently and then averaged. Furthermore, model parallelism, where different parts of a model are handled by different processors, allows for efficient training of complex models with billions of parameters . Figure (a) illustrates the exponential growth in computational requirements for training large AI models, that double every 3.4 months compared to conventional Moore's law . The inherent parallelism in AI training makes it particularly well-suited for GPUs, which excel at executing the same operation on multiple data points simultaneously.