Inference platforms fail in layers. Most organizations focus on model quality while underestimating the systems engineering required to operate production AI workloads safely and reliably at scale.

Before GPU saturation even becomes a problem, teams often expose models directly to ungoverned traffic, lack concurrency controls, fail to measure system behavior, overload memory bandwidth, and eventually destroy latency guarantees and operational stability.

This talk walks through the progressive failure modes of modern AI serving systems and how to architect scalable inference infrastructure that remains observable, resilient, and performant under real production workloads. In this talk, I will walk the attendees through real code paths, production failure scenarios, debugging strategies, and architectural tradeoffs, showing both how these systems fail and how to systematically fix them.