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DRA

Introduction to Dynamic Resource Allocation (DRA) in Kubernetes

In the previous blog, we reviewed the limitations of Kubernetes GPU scheduling. These often result in:

  1. Resource fragmentation – large portions of GPU memory remain idle and unusable.
  2. Topology blindness – multi-GPU workloads may be scheduled suboptimally.
  3. Cost explosion – teams overprovision GPUs to work around scheduling inefficiencies.

In this post, we’ll look at how a new GA feature in Kubernetes v1.34 — Dynamic Resource Allocation (DRA) — aims to solve these problems and transform GPU scheduling in Kubernetes.

Rethinking GPU Allocation in Kubernetes

Kubernetes has cemented its position as the de-facto standard for orchestrating containerized workloads in the enterprise. In recent years, its role has expanded beyond web services and batch processing into one of the most demanding domains of all: AI/ML workloads.

Organizations now run everything from lightweight inference services to massive, distributed training pipelines on Kubernetes clusters, relying heavily on GPU-accelerated infrastructure to fuel innovation.

But there’s a problem. In this blog, we will explore why the current model falls short, what a more advanced GPU allocation approach looks like, and how it can unlock efficiency, performance, and cost savings at scale.