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Kubernetes v1.36: More Drivers, New Features, and the Next Era of DRA

Kubernetes v1.36 cements Dynamic Resource Allocation (DRA) as the default control plane for heterogeneous hardware, graduating core APIs to stable while extending support to CPUs, memory, and PodGroup-based ResourceClaims. With driver ecosystems now spanning GPUs, FPGAs, and SmartNICs, the release eliminates bespoke resource schedulers, letting operators define fallback policies and failure domains in declarative manifests—critical for scaling AI workloads across mixed-accelerator clusters.

Adam J (AI-assisted) 1 min read EN

Based on reporting from Source.

Kubernetes v1.36 cements Dynamic Resource Allocation (DRA) as the default control plane for heterogeneous hardware, graduating core APIs to stable while extending support to CPUs, memory, and PodGroup-based ResourceClaims.

Overview

Dynamic Resource Allocation (DRA) has fundamentally changed how platform administrators handle hardware accelerators and specialized resources in Kubernetes. In the v1.36 release, DRA continues to mature, bringing a wave of feature graduations, critical usability improvements, and new capabilities that extend the flexibility of DRA to native resources like memory and CPU, and support for ResourceClaims in PodGroups.

What's New

The community has been hard at work stabilizing core DRA concepts. In Kubernetes 1.36, several highly anticipated features have graduated to Beta and Stable. These include:

  1. Prioritized list (stable): allowing users to define fallback preferences when requesting devices.
  2. Extended resource support (beta): enabling users to request resources via traditional extended resources on a Pod.
  3. Partitionable devices (beta): providing native DRA support for dynamically carving physical hardware into smaller, logical instances.
  4. Device taints (beta): empowering cluster administrators to manage hardware more effectively by applying taints directly to specific DRA devices.
  5. Device binding conditions (beta): improving scheduling reliability by delaying committing a Pod to a Node until its required external resources are fully prepared.
  6. Resource health status (beta): exposing device health information directly in the Pod status.

New features in v1.36 include:

  1. ResourceClaim support for workloads: enabling Kubernetes to seamlessly manage shared resources across massive sets of Pods.
  2. Node allocatable resources: introducing the first iteration of using the DRA APIs to manage node allocatable infrastructure resources.
  3. DRA resource availability visibility: allowing users to query the availability of devices in DRA resource pools.
  4. List types for attributes: changing ResourceClaim constraint evaluation to work better with scalar and list values.
  5. Deterministic device selection: updating the Kubernetes scheduler to evaluate devices using lexicographical ordering based on resource pool and ResourceSlice names.
  6. Discoverable device metadata in containers: defining a standard protocol for how DRA drivers expose device attributes to containers.

What's Next

The roadmap focuses on maturing existing features toward beta and stable releases while hardening DRA’s performance, scalability, and reliability. A key priority will be deep integration with workload aware and topology aware scheduling. Users can get involved by joining the WG Device Management Slack channel and meetings, and collaborating on development, sharing feedback, or building their first DRA driver.

In summary, Kubernetes v1.36 enhances Dynamic Resource Allocation, providing a more robust and hardware-agnostic infrastructure. With its new features and graduations, DRA is becoming the standard for resource allocation, allowing cluster operators to migrate clusters to DRA and letting application developers adopt the ResourceClaim API on their own schedule.

Sources 1

  1. 01 Source https://kubernetes.io/blog/2026/05/07/kubernetes-v1-36-dra-136-updates/
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