How to reduce AI infrastructure costs with Kubernetes GPU partitioning

Kubernetes' traditional resource management model struggles with efficient GPU allocation, leading to financial waste when lightweight workloads like AI inference use only a fraction of the assigned GPU. Despite NVIDIA’s Multi-Instance GPU (MIG) technology allowing physical partitioning of GPUs into isolated instances, effectively scheduling these partitions in Kubernetes requires complex configurations involving DaemonSets, node labels, taints, and affinity rules. Qovery offers a solution by simplifying this complexity into an intuitive developer interface that automatically generates the necessary Kubernetes configurations. This approach maximizes GPU utilization and return on investment by abstracting intricate scheduling details, allowing developers to focus on building AI applications without needing deep Kubernetes expertise. Despite the hardware capability being ready, the challenge lies in the orchestration layer, where Qovery's platform bridges the gap by translating hardware capabilities into a seamless developer experience, thus facilitating efficient GPU partitioning and management without the typical associated toil.