Learn how Dynamic Resource Allocation (DRA) in OpenShift 4.21 fundamentally changes how GPU and accelerator resources are requested, allocated, and shared across your cluster. This post covers what DRA is, why it matters, what's new in OpenShift 4.21, and how to use it with real examples running on an OpenShift 4.21 cluster with NVIDIA A100 GPUs.

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Red Hat Developer

Dynamic Resource Allocation (DRA) reaches General Availability in Red Hat OpenShift 4.21, built on Kubernetes 1.34. DRA replaces the legacy device plug-in model with a structured, CEL expression-driven framework that lets pods request GPUs and accelerators by attributes like model, memory, MIG profile, and compute capability rather than simple counts. Three GA capabilities are covered with live NVIDIA A100 examples: attribute-based GPU allocation (including MIG slice targeting), prioritized device alternatives with automatic fallback, and namespace-controlled admin access to in-use devices for monitoring without disrupting workloads. The post includes full YAML manifests and real cluster output demonstrating each feature.

Dynamic resource allocation goes GA in Red Hat OpenShift 4.21: Smarter GPU scheduling for AI workloads

The problem: Why device plug-ins fall short

2. Prioritized alternatives in device requests