AI models are rapidly increasing in complexity, demanding more powerful computing resources for effective training and inference.
This trend has sparked significant interest in scaling computational capacity for AI, with teams exploring new hardware architectures and distributed computing strategies to extend the boundaries of what AI can achieve. (Forbes)