GPU Offloading in MLOps: Navigating the Multicloud Ecosystem for Flexible AI/ML Deployments

The demand for dedicated computational resources for AI tasks is surging. However, the substantial investment required for high-performance GPUs, coupled with in-creasing market scarcity and extended lead times, poses significant challenges. Simul-taneously, underutilization of these resources is a frequent issue. This paper proposes a solution leveraging cloud-native approaches to utilize unused resources available elsewhere, imposing minimal overhead on the originating cluster. We will present how an “origin” cluster can seamlessly peer with a “donating” cluster, offloading tasks to remote hardware with complete transparency and superior efficiency com-pared to current technologies.