GPU-as-a-Service for AI at scale: Practical strategies with Red Hat OpenShift AI

GPU-as-a-Service for AI at scale: Practical strategies with Red Hat OpenShift AI The need for GPUaaS on Red Hat OpenShift AI AI workload integration and autoscaling Queue management with Kueue Effective autoscaling with KEDA Observability-driven optimization Conclusion The adaptable enterprise: Why AI readiness is disruption readiness About the authors Ana Biazetti Lindani Phiri More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Stop wasting budget on idle GPUs. Learn how to implement dynamic allocation, multi-tenancy, and effective autoscaling for your AI workloads. For organizations investing heavily in AI, the cost of specialized hardware is a primary concern. GPUs/accelerators are expensive, and if that hardware is unused and sits idle, it leads to significant budget waste, making it more difficult to scale your AI projects. One solution is to adopt GPU-as-a-Service (GPUaaS), an operational model designed to help maximize the return on investment (ROI) of your hardware. Red Hat OpenShift AI is a Kubernetes-based platform that can be used to implement a multi-user GPUaaS solution. While provisioning the hardware is the first step, achieving true GPUaaS requires additional dynamic allocation based on workload demand, so GPUs are more quickly reclaimed to minimize idle time. GPUaaS also necessitates multi-tenancy. This is where advanced queuing tools like Kueue (Kubernetes Elastic Unit Execution) become indispensable. Kueue partitions shared resources and enforces multi-tenancy via quotas, guaranteeing fair, predictable access for multiple teams and projects. Once this governance is in place, the core challenge shifts to creating an autoscaling pipeline for AI workloads. The goal of a GPUaaS platform is to integrate popular AI frameworks and automatically scale resources based on workload demand.