--- title: "Kubernetes GPU orchestration — NVIDIA device plugin | Aguasto" description: "Technical reference for Kubernetes GPU orchestration — NVIDIA device plugin: Expose NVIDIA GPU devices as Kubernetes resources and allocate them to container workloads. Hardware assumptions and operating trade-offs are documented." canonical: "https://www.aguasto.com/inference-stack/kubernetes" last-updated: "2026-10-12" --- # Kubernetes GPU orchestration — NVIDIA device plugin | Aguasto Canonical HTML: [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) > Technical reference for Kubernetes GPU orchestration — NVIDIA device plugin: Expose NVIDIA GPU devices as Kubernetes resources and allocate them to container workloads. Hardware assumptions and operating trade-offs are documented. [Aguasto](https://www.aguasto.com/) [Inference Stack](https://www.aguasto.com/inference-stack) Kubernetes GPU orchestration — NVIDIA device plugin [Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools) GPU resource orchestration ## Kubernetes GPU orchestration — NVIDIA device plugin Expose NVIDIA GPU devices as Kubernetes resources and allocate them to container workloads. Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark. Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services. ### When it is relevant - Schedule GPU-backed containers on an existing Kubernetes cluster - Study device allocation, GPU feature labels or documented sharing modes [NVIDIA Kubernetes device plugin official repository (external source)](https://github.com/NVIDIA/k8s-device-plugin) · [Kubernetes official GPU scheduling guide (external source)](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/) ### Hardware and software assumptions Documented ecosystem: NVIDIA. Platform support is not a guarantee that every checkpoint, optimization or feature works on every device. - Install compatible NVIDIA drivers, container runtime tooling and the device plugin on GPU nodes - Check cluster-version and plugin-version compatibility - Supply a separate model-serving engine and operational monitoring - No Aguasto Kubernetes cluster or GPU inventory is asserted [NVIDIA Kubernetes device plugin official repository (external source)](https://github.com/NVIDIA/k8s-device-plugin) · [Kubernetes official GPU scheduling guide (external source)](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/) ### Operating trade-offs - This precise plugin profile is NVIDIA-specific; other vendors require their own plugins - Device allocation does not tune inference performance or provide a complete GPU operations system - Time-slicing shares GPU memory and fault domains and is not equivalent to isolated hardware partitions [NVIDIA Kubernetes device plugin official repository (external source)](https://github.com/NVIDIA/k8s-device-plugin) · [Kubernetes official GPU scheduling guide (external source)](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/) ### Continue the research [Choosing an inference framework](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) [Prefill, decoding and disaggregation](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) [Benchmark methodology](https://www.aguasto.com/research/inference-benchmark-methodology) [Model references](https://www.aguasto.com/models) [Infrastructure collaboration](https://www.aguasto.com/partners) ### Sources & verification Reference revision 1 · Reviewed October 12, 2026. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production. - [NVIDIA Kubernetes device plugin official repository ↗](https://github.com/NVIDIA/k8s-device-plugin) NVIDIA device plugin and GPU feature discovery; driver and runtime prerequisites; time-slicing memory and fault-domain limits; distinct sharing modes. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [Kubernetes official GPU scheduling guide ↗](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/) vendor drivers and device plugins; GPU extended resources such as nvidia.com/gpu; resource requests / limits rules; node-selection constraints. Last modified: 2024-09-20. 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Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.