At enterprise AI scale, training infrastructure risk becomes roadmap risk. Third-party research and production benchmarks reveal measurable gaps between general-purpose and purpose-built AI cloud infrastructure across three dimensions—utilization efficiency, reliability, and training economics.
This research brief examines what those gaps look like in practice. You’ll learn:
- Why GPU count is the wrong procurement metric, and what to measure instead
- How reliability challenges compound at distributed training scale, and why new hardware raises the stakes further
- Why 44–47% lower TCO goes further than a headline rate comparison—and how utilization efficiency drives the difference
