Every time you ask ChatGPT a question, your request triggers a data relay race. Information leaves memory, passes through a CPU for preprocessing, travels to a GPU for heavy computation, and then makes its way back and that entire journey repeats for every single word the AI generates.
This framing redefines the AI inference bottleneck as a data movement problem, not a compute problem. Every token generation incurs a full memory-CPU-GPU round trip — a latency and energy tax that scales with usage volume. XCENA's thesis is that eliminating this relay is worth more than faster GPUs.