In production AI systems today, 60–70% of inference workloads are tightly coupled to a single GPU vendor or instance type, leading to 30–50% higher infrastructure costs, poor portability, and operational friction when scaling across cloud and on-prem environments. As demand grows, teams face a choice: lock in deeper or redesign for flexibility.

This presentation presents a GPU-agnostic inference architecture built with Ray on Kubernetes, designed to run reliably across heterogeneous accelerator clusters. By decoupling application logic from hardware assumptions and leveraging Ray’s distributed execution with Kubernetes-native scheduling, teams can scale inference without rewriting pipelines for each GPU type.

Using a production-grade reference architecture, we’ll show how inference traffic flows through Ray Serve, how workloads scale across mixed CPU/GPU nodes, and how concurrency, fault tolerance, and autoscaling are handled under real-world load. We’ll also demonstrate how KubeRay reduces operational overhead by managing Ray clusters through Kubernetes-native lifecycle controls.