The integration of artificial intelligence is fundamentally transforming data center infrastructure. Key performance metrics have shifted from traditional FLOPS and PUE to AI-specific indicators, such as tokens generated per second, time-to-first-token, and energy efficiency measured in tokens per watt.
The market is seeing a resurgence in processor architecture innovation. While Nvidia remains dominant, alternative solutions are emerging, including Huawei Ascend, Cambricon MLU, Biren, and custom accelerators from hyperscalers like Google, Amazon, Microsoft, and Meta. Companies are also exploring wafer-scale computing.
Data movement has become a critical bottleneck, leading to new hierarchies of memory and interconnects. Technologies like UCIe, HBM, PCIe 6.0/7.0, CXL, and coherent interconnects such as NVLink-C2C and AMD Infinity Fabric are essential for managing data flow between CPUs, GPUs, and memory pools.
Modern AI racks now exceed 100 kW, with projections reaching 1 MW per rack. This density necessitates liquid direct-to-chip cooling and new power standards, such as the 800 VDC scheme being standardized by Google, Microsoft, and Nvidia via the Open Compute Project for 2026.
The definition of a computer is evolving from a single server to a distributed system where entire data centers function as a unified machine. Consequently, infrastructure is increasingly delivered as integrated software-hardware complexes, requiring specialized schedulers to manage AI workloads and data distribution.
