TPU Logic for Architects: When to Choose Accelerated Compute Over Traditional CPUs

The Rack2Cloud AI Infrastructure Series The software world treats AI like just another API call. But beneath the abstraction, AI is the heaviest, most latency-sensitive, and hardware-dependent workload in the modern data center. In this two-part series, we are dropping the marketing hype and looking at the actual physics of AI infrastructure. Part 1: TPU Logic for Architects: When to Choose Accelerated Compute Over Traditional CPUs Part 2: The Disconnected Brain: Why Cloud-Dependent AI is an Architectural Liability For years, cloud architecture treated compute power like a basic utility—run low on performance, just throw in more CPU cores. That got the job done for web apps and batch processing. But then AI came along, especially Large Language Model (LLM) training, and flipped the script. Suddenly, all that expensive silicon sits around doing nothing if your memory, interconnects, or data pipelines can’t keep up. In hyperscale AI, compute isn’t just about raw power—it’s about physics.