While traditional servers rely mostly on CPUs, AI servers lean heavily on graphics processing units (GPUs) and similar AI accelerators that are purpose-built to handle modern AI models. Modern AI models are data-hungry, computation-heavy beasts that need specialized hardware just to function, let alone perform at their best. That's the job of an AI server—a custom-built system that keeps AI applications fast, scalable, and efficient. An AI server's architecture is all about. AMD continues to challenge Nvidia with its MI400 series chips, powering the upcoming Helios AI servers. These offer high-performance AI computing with open standards for interoperability, reflecting a shift from proprietary technologies toward collaboration. It involves using computer programs to simulate human intelligence, achieving or imitating human thought processes, behavioral patterns. 2 Hyperscalers are spending $380B+ on AI capex in 2025 while simultaneously building custom chips (TPU, Trainium, Maia, MTIA) that offer 40-65% TCO advantages over GPUs. 3 Broadcom and Marvell control ~95% of the custom ASIC co-design market — Google alone spends ~$8B/year with Broadcom on TPU. The AI chips are sort of general-purpose CPUs that provide higher speed and efficiency through the use of smaller, faster transistors. A smaller transistor is quicker and uses less energy. But unlike the CPUs, AI Chips also offer AI-optimized design features. Microsoft, Meta, Baidu, and ByteDance increased orders in 2023 as they launched services based on generative AI, and AI server shipments were expected to grow by 15.