The explosive growth of artificial intelligence demands massive computational infrastructure, with global data center capacity exceeding 50 million servers consuming 1-2% of worldwide electricity. Modern AI data centers employ GPU accelerators (NVIDIA A100, H100) delivering 300-1000 teraflops per chip, arranged in clusters of thousands interconnected through high-bandwidth networks (400-800 Gbps per link) enabling parallel training of billion-parameter models. Advanced cooling systems address heat loads of 30-50 kW per rack, employing liquid cooling solutions circulating dielectric fluids directly to processors or rear-door heat exchangers removing thermal energy before air circulation. Energy efficiency optimization targets Power Usage Effectiveness (PUE) ratios approaching 1.1 through waste heat recovery, free cooling using ambient air or water when temperatures permit, and intelligent workload management shifting computation to times and locations with abundant renewable energy. Purpose-built AI accelerators including Google TPUs and custom chips from Amazon, Microsoft, and startups offer superior performance-per-watt for specific workloads compared to general-purpose GPUs. Distributed training frameworks like PyTorch and TensorFlow scale across thousands of accelerators, with model parallelism dividing large models across devices and data parallelism processing different data batches simultaneously. Edge computing distributes AI inference to local devices - smartphones, autonomous vehicles, IoT sensors - reducing latency, bandwidth requirements, and privacy concerns by processing data near its source. Quantum computing research pursues exponential speedups for specific optimization and simulation problems relevant to AI. Sustainable data center development increasingly collocates facilities near renewable energy sources and implements direct renewable energy procurement, with major technology companies achieving 90-100% renewable energy usage for global operations.