Modern AI relies on specialized hardware beyond traditional CPUs. Training large language models requires trillions of operations while image generation demands billions of calculations in seconds. General-purpose processors can't handle these workloads efficiently, creating demand for architectures optimized for parallel computation and matrix operations.

GPUs revolutionized deep learning by providing thousands of cores for simultaneous processing, reducing training times from months to days. Google's TPUs further advanced AI with hardware designed specifically for tensor operations, offering better energy efficiency and reduced overhead for large-scale machine learning. These specialized processors enabled breakthroughs that would be impossible with conventional computing architectures.

NPUs now bring AI directly to consumer devices, enabling energy-efficient inference on smartphones and edge devices. Modern AI systems combine multiple hardware components—CPUs for orchestration, GPUs for training, TPUs for specialized operations, and NPUs for on-device inference. This specialized approach continues to drive AI advancement as algorithms and hardware evolve together.