Why are GPUs so central to AI development, and what are the credible alternatives emerging?

GPUs are central to AI development primarily because they provide the high-performance computing power essential for training large AI models and handling inference workloads, as seen in the massive investments and commitments from companies like OpenAI to NVIDIA's GPU infrastructure [2][7][8]. This has propelled NVIDIA to become the world's biggest company, with AI infrastructure relying on high-end GPUs as a core resource alongside electricity and cooling systems, driving surges in spending that question whether it's a bubble or sustainable growth [4][5][8]. Enterprises are pouring billions into GPUs, but utilization issues often stem from data delivery bottlenecks rather than the hardware itself [3]. Credible alternatives are emerging as AI infrastructure expands beyond GPUs to include CPUs, storage, and networking, enabling more scalable production environments [1]. CPUs are gaining traction in the booming AI inference market and for AI agents, positioning companies like AMD and Broadcom to compete with NVIDIA [2]. This "sea change" in AI computing, with inference demand growing faster than training, is challenging NVIDIA's dominance through standardized ecosystems and optimizations like open-source models on advanced GPU setups, but also broader hardware-software integrations [6][7][9].