# Spotlight on WG Device Management

DevFeed: [Spotlight on WG Device Management](<https://devfeed.tech/articles/spotlight-on-wg-device-management-4559.md>)

Original publisher: [Read original article](<https://kubernetes.io/blog/2026/06/24/wg-device-management-spotlight-2026/>)

Author: Natalie Fisher

Published: 2026-06-24T18:00:00Z

Content type: article

Language: en

Sources: [Kubernetes Blog](<https://devfeed.tech/sources/kubernetes-blog.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [intel](<https://devfeed.tech/topics/intel.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [intel](<https://devfeed.tech/tags/intel.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [tech-lead](<https://devfeed.tech/tags/tech-lead.md>)

## AI overview

This Kubernetes spotlight examines the Device Management Working Group and its Dynamic Resource Allocation project, which has graduated to general availability. It explains how Kubernetes is evolving beyond CPU and memory allocation to manage GPUs, TPUs, network interfaces, and other specialized hardware for AI, edge, and telecommunications workloads, including dynamic allocation and time-sharing.

## Source excerpt

The rising popularity of AI, Edge, and Telecommunications workloads on Kubernetes has led to new requirements for hardware management. We now need hardware specification beyond CPU time and memory allocations. This includes allocating GPUs, TPUs, network interfaces, and other hardware, sometimes after pod start and occasionally through time-sharing. Efficiently managing this specialized hardware is the mission of the Device Management Working Group. Their cornerstone project, Dynamic Resource Allocation (DRA), recently graduated to GA, marking a fundamental shift in how the project handles hardware-intensive workloads at scale. In this spotlight, we sit down with working group chairs Kevin Klues, Patrick Ohly, and John Belamaric to discuss the limitations of the legacy device model, the NP-hard challenges of scheduling, and how they're building a more programmable, hardware-aware future for Kubernetes. Introducing Device Management Natalie Fisher: Can you introduce yourself, your role, and how you got involved in the Device Management Working Group? Kevin Klues: My name is Kevin Klues. I am a Distinguished Engineer at NVIDIA. I have been a co-chair of the device management working group since its inception at Kubecon EU 2024. I have also been involved with DRA (the working group's primary deliverable) since its inception in 2019 / 2020. I have also been a kubelet maintainer since 2019, with a focus on its device manager, CPU manager, and topology manager subcomponents. The challenges we saw with using these components for workloads that relied on external accelerators (e.g., GPUs) are what triggered us to start working on DRA in the first place. Patrick Ohly: I am a Principal Engineer at Intel. In Kubernetes, I am a Tech Lead for SIG Testing and SIG Instrumentation and co-chair of the Device Management WG. I was co-chair of the WG Structured Logging and a member of the Steering Committee. Some of my early contributions to Kubernetes include ephemeral CSI volumes and