AI hardware evolution: The roles of CPU, GPU, and TPU in data centers

The landscape of artificial intelligence infrastructure is shifting as data centers face increasing power constraints and evolving hardware requirements. While NVIDIA's GPUs currently dominate the market through high-performance parallel processing, the industry is seeing a diversification of hardware roles.

Central processing units (CPUs) act as the system controllers, handling complex logic and sequential tasks. Graphics processing units (GPUs) excel at the massive parallel computations required for neural networks. Tensor processing units (TPUs), specialized by Google for machine learning, offer high efficiency for matrix operations by minimizing memory access through systolic array architectures.

As AI demand grows, data centers are encountering a “power wall,” where the massive energy required for GPU-heavy computations necessitates new design strategies. This includes locating facilities near abundant energy sources, implementing advanced water-cooling systems, and increasing the space allocated to non-IT infrastructure like power distribution and cooling.