A developer’s guide to architecting reliable GPU infrastructure at scale

This post outlines Google Cloud's strategy for engineering reliable GPU infrastructure at the scale required for modern AI/ML workloads.

• •At scales of hundreds of thousands of GPUs, a 0.01% performance fluctuation can trigger systemic failure, making reliability a primary constraint
• •Key metrics used are MTBI (Mean Time Between Interruption) and Goodput (useful computational work per unit time)
• •Infrastructure instability causes multi-million dollar losses, delayed model releases, and forces companies to over-provision 10-20% extra hardware as a buffer
• •Google Cloud's reliability approach rests on four principles: proactive prevention, continuous monitoring, transparency/control, and minimizing disruptions
• •Rackscale GPU architectures like NVIDIA GB200 NVL72 require coordinated management at the domain level, beyond individual machines