Short answer: choose the NVIDIA GPU Operator’s containerized driver when you want Kubernetes to deploy and upgrade the driver alongside the rest of your GPU stack. Pre-install the driver on each worker when your node-management system must own driver updates, when GPU nodes use different operating-system versions, or when your Kubernetes platform requires a provider-specific installer. You can also combine pre-installed host drivers with the GPU Operator for the device plugin, monitoring and other components.
The important boundary is lifecycle ownership: NVIDIA says the GPU Operator manages containerized driver lifecycles, not drivers installed directly on the host. That distinction determines who performs upgrades, how nodes are drained, and which operating-system combinations are supported.
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What the two installation models actually do
GPU Operator-managed, containerized driver
With a default installation, the GPU Operator runs the NVIDIA driver and supporting GPU software as Kubernetes-managed workloads on GPU worker nodes. NVIDIA’s current installation guide identifies v26.7.0 as the current patch release in its search listing, although the exact patch and chart options can change. Follow the guide for the release you are deploying: Installing the NVIDIA GPU Operator.
The default policy can deploy these components:
- NVIDIA GPU driver
- NVIDIA Container Toolkit
- NVIDIA Device Plugin
- DCGM Exporter
- MIG Manager
- Node Feature Discovery (NFD), unless an existing NFD installation is detected and deployment is disabled
They run as pods or other Kubernetes resources on the GPU nodes, and individual components can be disabled when your platform already supplies them. This is more than a driver installer: it is a way to make the GPU software stack part of the cluster’s declarative configuration.
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Host-pre-installed NVIDIA driver
In the manual model, an administrator or node-image pipeline installs the NVIDIA driver directly into the operating system before the node joins the cluster. The host’s package manager, image builder or configuration-management system then owns that driver and its kernel modules.
“Manual drivers” does not mean that you must abandon the GPU Operator. NVIDIA documents a Pre-Installed NVIDIA GPU Drivers configuration in which the Operator continues to manage other GPU components while its driver deployment is disabled. The operator’s driver setting is configured with driver.enabled=false. If both the driver and NVIDIA Container Toolkit are already installed on the host, the documented configuration also disables toolkit deployment and sets the container runtime’s default runtime to nvidia. Use the chart values and runtime instructions in the current installation guide rather than copying values from an older article.
Who owns the driver lifecycle?
What the Operator controls
NVIDIA’s upgrade documentation is explicit: “The GPU Operator only manages the lifecycle of containerized drivers.” The Operator can therefore coordinate installation, replacement and removal of the driver container through Kubernetes resources. It does not take ownership of a driver that was installed into the host filesystem by an image or package-management process.
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What a host-management system controls
With pre-installed drivers, updates belong to the process that builds or patches nodes. That might be a golden image, an operating-system update pipeline or a cloud-provider mechanism. The GPU Operator will not upgrade those host packages for you. Your process must select a driver compatible with the node kernel, reboot or otherwise reload modules as required, and verify that every node in the target pool reaches the same intended state.
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This separation can be an advantage if your organization already has strict host-patching controls. It can also create two independent change schedules: one for the host driver and another for the Operator-managed device plugin, toolkit, exporter or MIG components. Document that boundary so an incident responder knows which team can change the driver.
How driver upgrades affect running workloads
Containerized-driver upgrade sequence
Updating a loaded NVIDIA driver is not a simple pod replacement. NVIDIA explains that the kernel modules must be unloaded and loaded again when the driver container restarts. The supported sequence is:
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- Unload the currently loaded NVIDIA kernel modules.
- Start the updated driver pod.
- Install the updated driver and load its new modules.
- Enable GPU clients again.
Clients can include application pods, monitoring processes and other software that has opened the device files. Attempting to replace modules while they are in use can leave the node unable to initialize the new driver.
NVIDIA recommends its driver upgrade controller, enabled by default, to automate this workflow. The controller coordinates the operation and emits metrics and Kubernetes events that can be used by monitoring and incident tools. Read the complete procedure in GPU Driver Upgrades and test the sequence with the workload-disruption policy used by your cluster.
Pre-installed-driver upgrade sequence
For a host driver, the same kernel-module reality still applies, but the Operator is not the orchestrator. Your node-maintenance system must cordon or otherwise remove the node from service, stop or drain GPU clients, apply the driver update, unload and reload modules or reboot as required by that driver package, and then return the node to the scheduling pool after health checks pass.
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Plan the operation as a node-level change, not merely a package installation. A successful package transaction does not prove that the running kernel is using the new module. Include checks for the driver version reported by the host, device visibility from a test workload, and the readiness of the device plugin before allowing production jobs back onto the node.
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ClusterPolicy-managed driver containers
NVIDIA’s installation guide states that GPU worker nodes or node groups using the driver container through ClusterPolicy must run the same operating-system version. This requirement applies to that configuration, not automatically to every possible GPU Operator design. If a node pool mixes operating-system versions, do not assume a single ClusterPolicy driver container is supported; verify the matrix for your release and platform.
Pre-installed drivers and mixed versions
The same guide says that pre-installing drivers permits different operating systems on GPU nodes. In this model, each image or host-management profile can install the package appropriate to its own OS while the cluster continues to use the Operator for other components.
NVIDIA also documents the NVIDIA Driver Custom Resource as a route for mixed operating-system versions on GPU nodes. That is a separate configuration choice with its own support conditions. Confirm the exact combination of Operator release, driver source, kernel and node operating systems before standardizing on it; “mixed OS supported” is not a blanket guarantee for every chart setting.
Prerequisites that apply before either choice
Both approaches still require a functioning Kubernetes GPU-node design. The current installation guide lists these prerequisites and dependencies:
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- A client machine with
kubectland Helm. - A container engine such as CRI-O or containerd on the nodes.
- Node Feature Discovery. The Operator deploys NFD by default, but you must disable that deployment if the cluster already runs NFD.
- GPU worker nodes that are prepared for the selected driver and runtime arrangement.
Pod Security Admission can block the privileged workloads needed by the Operator. Where PSA restrictions apply, NVIDIA instructs administrators to label the Operator namespace for privileged enforcement. Apply the label required by the current guide before deploying the chart; otherwise pods can remain rejected even though the Helm installation itself succeeds.
These are cluster-level prerequisites, not reasons to prefer one driver owner. A pre-installed driver still needs the container runtime and device-plugin path to expose GPUs to pods; an Operator-managed driver still depends on a compatible host kernel and runtime.
Comparison at a glance
| Decision axis | Operator-managed containerized driver | Host-pre-installed driver |
|---|---|---|
| Driver installation location | Driver is delivered and run through Operator-managed Kubernetes resources. | Driver is installed in the node operating system before or during node provisioning. |
| Upgrade owner | Cluster team using the Operator’s upgrade workflow; the upgrade controller is enabled by default. | Host-image, package or node-maintenance team; the Operator does not manage it. |
| Default scope | Driver plus toolkit, device plugin, DCGM Exporter, MIG Manager and NFD unless components are disabled. | Only the components you install on the host are host-managed; the Operator can still manage the remaining GPU stack. |
| Operating-system constraint | ClusterPolicy driver-container workers or node groups must use the same OS version according to the guide. | The guide says pre-installation permits different OS versions; verify support for each image and configuration. |
| Kernel-module maintenance | Requires coordinated client shutdown and module reload; the upgrade controller can automate it. | Requires the host-maintenance process to coordinate clients, modules and node availability. |
| Best fit | Teams seeking Kubernetes-native, declarative lifecycle management. | Teams with established node-image or OS-patching ownership, or supported mixed-OS pools. |
How to deploy the Operator-managed model
The exact Helm command, chart values and namespace differ by release and platform, so use the current NVIDIA guide as the authoritative procedure. The following sequence shows the decisions you need to make without hard-coding a command that may age out.
- Inventory the nodes. Record GPU model, kernel, operating-system version, container engine and Kubernetes distribution for every GPU worker or node group. Resolve OS differences before selecting a ClusterPolicy driver-container design.
- Prepare the client. Install versions of
kubectland Helm supported by the target cluster, and ensure your identity can create the Operator’s cluster-scoped resources and privileged workloads. - Check NFD. If NFD is absent, leave the Operator’s default deployment enabled. If the cluster already runs NFD, configure the Operator not to deploy a second instance.
- Prepare Pod Security Admission. Label the Operator namespace for privileged enforcement where PSA would otherwise reject the driver, toolkit or device-plugin pods.
- Install the chart and create the policy. Follow the Helm installation and ClusterPolicy steps in the release guide. Keep the driver component enabled and select only the other components that your environment does not already provide.
- Watch reconciliation. Check that the driver, toolkit, device plugin, exporter, MIG Manager and NFD resources reach their expected ready state on every GPU node. A Helm release marked successful is not a substitute for checking pod readiness and node health.
- Run a GPU smoke test. Schedule a minimal CUDA workload or your platform’s supported GPU test, confirm that the container sees the expected device, and verify that monitoring and scheduling metadata are present before admitting production jobs.
Keep the chart values and resulting ClusterPolicy in version control. That record is the operational contract for future upgrades and makes it clear which components the Operator is expected to own.
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How to use pre-installed drivers with the GPU Operator
- Build or select a node image. Install the NVIDIA driver directly on each GPU node’s operating system using the package and kernel procedure supported for that distribution. Ensure the image’s driver matches its kernel and GPU hardware.
- Verify the host before joining it. Confirm that the host can load the NVIDIA kernel modules and that the driver reports the expected devices. Do this before adding the node to a production scheduling pool.
- Install the container runtime integration. If the host image also includes the NVIDIA Container Toolkit, configure the runtime as documented for your engine and set the default runtime to
nvidia, as specified in NVIDIA’s pre-installed-driver instructions. - Disable Operator driver deployment. Set
driver.enabled=falsein the Operator configuration. If the toolkit is also pre-installed, disable toolkit deployment as well; otherwise two systems may attempt to provide the same component. - Retain the components you want the Operator to manage. The device plugin, DCGM Exporter, MIG Manager and NFD can remain Operator-managed when they are not supplied elsewhere. “Pre-installed driver” is a selective ownership choice, not an all-or-nothing installation mode.
- Validate scheduling. After the Operator reconciles, run a GPU workload and inspect the device-plugin and node status. Confirm that the runtime, host driver and Kubernetes resources agree on the visible GPU count.
- Document the boundary. Record the image version, driver package, kernel compatibility, runtime configuration and the team responsible for future host upgrades.
Platform-specific considerations: Google GKE
Driver ownership is partly a Kubernetes-distribution decision. NVIDIA’s GKE documentation describes both Google’s driver installer and GPU Operator driver management for supported Ubuntu nodes in GKE Standard. Choose one supported mechanism for a node pool and follow the GKE-specific prerequisites rather than combining installers casually.
NVIDIA states that GPU Operator is unsupported for GKE Autopilot Pods. That is a platform limitation, not evidence that either installation model is universally better. Read NVIDIA GPU Operator with Google GKE for the supported Standard configuration and its restrictions.
For other managed Kubernetes services, check the provider’s GPU-driver lifecycle, node-image policy and runtime defaults before installing the chart. A provider may already inject a driver or prohibit privileged workloads, changing which Operator components can run.
Upgrade planning beyond the driver
Changing the Operator version is separate from changing the NVIDIA driver. NVIDIA’s upgrade guide warns that Helm does not automatically upgrade existing CustomResourceDefinitions. Your runbook must therefore include the documented CRD upgrade procedure or explicitly enable the Helm hook described by NVIDIA.
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Common failure modes and recovery paths
| Symptom | Likely issue | What to check or change |
|---|---|---|
| Driver pods are rejected by admission control. | The Operator namespace is not permitted to run privileged workloads. | Apply the privileged namespace labeling required by the current NVIDIA guide, then inspect pod events and retry reconciliation. |
| Two NFD installations appear. | The cluster already had NFD, but the Operator’s default deployment remained enabled. | Disable Operator-managed NFD and keep one authoritative NFD installation. |
| GPU nodes never become ready after a driver update. | GPU clients still held the modules, or the new modules were not loaded for the running kernel. | Remove the node from service, stop clients, follow the unload/load sequence, inspect driver-pod events and only then re-enable workloads. |
| Pods cannot access a GPU with a pre-installed driver. | The runtime toolkit or default runtime is not configured consistently with the host driver. | Check the container engine configuration, the documented nvidia default-runtime setting and device-plugin readiness. |
| Some GPU nodes reconcile while others fail. | Node pools differ in OS, kernel, driver or supported GPU configuration. | Compare node images and labels. Use a supported mixed-OS design, such as pre-installed drivers or the documented Driver Custom Resource path, only after confirming release support. |
| Helm reports an upgrade but custom resources behave unexpectedly. | Existing CRDs were not upgraded automatically. | Follow NVIDIA’s CRD upgrade instructions or use the documented Helm hook option, then verify the CRD versions and Operator events. |
Which approach should your team choose?
Choose the Operator-managed driver when
- Your GPU worker nodes can meet the same-OS requirement for the selected ClusterPolicy driver-container configuration.
- You want driver deployment represented in Kubernetes and prefer the documented upgrade controller.
- Your platform permits the privileged pods and kernel-module operations required by the Operator.
- You want one declarative policy to coordinate the driver with the toolkit, device plugin, DCGM Exporter and MIG Manager.
Choose pre-installed drivers when
- Your organization already has a reliable image-building or host-patching pipeline with clear ownership.
- GPU node groups require different operating-system versions and the chosen configuration is supported.
- Your cloud or Kubernetes distribution supplies a supported driver installer that should remain the source of truth.
- You need to control driver rollout independently from the Operator’s release cadence.
Use a hybrid ownership model when
- The host image must provide the driver and toolkit, but you still want the Operator’s device plugin, telemetry or MIG management.
- You have different node pools with different driver ownership rules, provided the Operator configuration and platform documentation support that arrangement.
- You can make the boundary explicit: host tooling updates the driver; Kubernetes tooling updates the remaining GPU components.
The practical decision is not “automation versus manual work.” It is whether Kubernetes or your node-management system should be accountable for the driver, and whether your operating-system, platform and maintenance constraints support that choice. Select one owner for each component, test the module-reload procedure, and keep the configuration tied to the exact Operator and Kubernetes release you run.
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