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Short answer: Use the NVIDIA GPU Operator when you want Kubernetes to deploy and coordinate the containerized NVIDIA driver and related components. Install the driver on each host yourself when your node-image or operating-system management already owns driver updates, when GPU nodes use different operating-system versions, or when your platform requires a host-managed driver path. You can also combine both approaches: pre-install the host driver, then run the GPU Operator with driver deployment disabled so it manages the remaining GPU components.
The important boundary is lifecycle ownership. NVIDIA states that the GPU Operator manages containerized drivers, not drivers installed directly on the host. Choose the owner before production rollout, because mixing two owners on the same nodes creates upgrade and troubleshooting conflicts.
What the NVIDIA GPU Operator installs
A default GPU Operator deployment places several NVIDIA components on GPU worker nodes as Kubernetes resources. The current installation guide lists v26.7.0 as the current patch release in its search result; release details and chart values can change, so use the current NVIDIA installation guide for the release you are deploying.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Component | Default role | Can it be disabled? |
|---|---|---|
| NVIDIA GPU driver | Provides the kernel modules and user-space libraries needed by workloads | Yes. Set driver.enabled=false when the driver is already installed on the host |
| NVIDIA Container Toolkit | Integrates NVIDIA GPUs with the container runtime | Yes, when your platform already supplies and configures it |
| NVIDIA Device Plugin | Advertises GPUs as schedulable Kubernetes resources | Configurable |
| DCGM Exporter | Exports NVIDIA GPU telemetry for monitoring systems | Configurable |
| MIG Manager | Manages supported Multi-Instance GPU partitioning | Configurable |
| Node Feature Discovery (NFD) | Labels nodes according to hardware and software features | Deployed by default; disable the Operator’s copy if NFD already exists |
This broad scope is why “Operator versus manual drivers” is not always an all-or-nothing choice. You can let your node-image pipeline install the driver while the Operator supplies the device plugin, metrics exporter, MIG management and other components.
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How host-installed drivers differ
With the manual approach, an administrator or node-image system installs the NVIDIA driver directly on every GPU worker node. The host package, kernel modules, reboot policy and security updates remain the responsibility of that system. The GPU Operator does not take over those host drivers later.
In NVIDIA’s documented pre-installed-driver scenario, install the host driver before configuring the Operator and disable its driver component with driver.enabled=false. If the NVIDIA Container Toolkit is also installed and configured by your host process, disable toolkit deployment as well. NVIDIA’s guide says the container runtime’s default runtime should be set to nvidia for this arrangement. The exact runtime file and restart procedure depend on whether the node uses containerd, CRI-O or another supported engine.
“Manual” therefore describes who owns the host driver, not whether Kubernetes is absent. A cluster can use host-installed drivers and still use the GPU Operator for scheduling, monitoring and MIG operations.
GPU Operator versus manual installation: decision table
| Decision point | GPU Operator-managed driver | Host-pre-installed driver |
|---|---|---|
| Lifecycle owner | The Operator manages the containerized driver’s deployment and documented upgrade workflow. | Your image, configuration-management or platform team installs and upgrades the host driver. The Operator does not manage it. |
| Where the driver runs | As a driver container coordinated through the Operator’s ClusterPolicy. | Directly on the node operating system, with kernel modules loaded by the host. |
| Operating-system consistency | For driver containers through ClusterPolicy, GPU worker nodes or node groups must run the same operating-system version. | The installation guide says pre-installed drivers permit different operating systems; verify the exact supported combination for your cluster. |
| Upgrade coordination | The Operator’s upgrade controller is enabled by default and can automate the required client shutdown and module reload sequence. | You must coordinate workload interruption, package installation, kernel-module reloads and node reboots or restarts. |
| Initial configuration | One Helm deployment can install the driver and related GPU components, subject to platform prerequisites. | Each node image or provisioning workflow must contain a compatible driver and, where needed, the toolkit and runtime configuration. |
| Mixed GPU node groups | Possible, but the ClusterPolicy driver-container OS requirement can be a constraint. NVIDIA also documents Driver Custom Resources for mixed OS versions. | Often a better fit when different node groups intentionally use different operating-system versions, provided each image is supported. |
| Failure domain | A bad chart value, image or Operator upgrade can affect many nodes; Kubernetes events and resources make state visible. | A bad image or package update can affect every node built from that image; host tooling and node replacement are the recovery mechanisms. |
When the GPU Operator is the better choice
Choose it for Kubernetes-centered lifecycle management
The Operator is appropriate when the team wants driver deployment represented in Kubernetes resources, versioned with Helm and upgraded using NVIDIA’s documented process. It avoids writing a separate host-driver installer for every node image and keeps the device plugin, exporter and MIG components in one supported deployment model.
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Choose it when nodes are intentionally uniform
Driver containers are simplest when all GPU workers in the relevant node group use the same operating-system version and compatible kernels. Uniform node pools also make draining and validating an upgrade predictable.
Choose it when you need a complete GPU software stack
The default deployment includes more than the driver. If you need device discovery, GPU metrics or MIG management and do not already operate those components, the Operator can provide them from the same release. Individual components remain configurable.
When host pre-installation is the better choice
Your organization already owns node images
Use host installation when a hardened image pipeline, immutable-node workflow or enterprise patch process already validates NVIDIA packages against your kernel. The same process can roll back an image or replace a node without making the driver a Kubernetes workload.
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NVIDIA’s guide states that pre-installing drivers permits different operating-system versions, while ClusterPolicy-managed driver containers require the worker nodes or node groups to run the same OS version. This is configuration-specific: check the supported driver, kernel and Kubernetes combinations before relying on mixed-OS support. NVIDIA also points to Driver Custom Resources as a documented option for mixed operating-system versions on GPU nodes.
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Your cloud platform prescribes a host installer
Support varies by distribution. For GKE Standard, NVIDIA documents both Google’s driver installer and GPU Operator driver management on supported Ubuntu nodes. GPU Operator is not supported for GKE Autopilot Pods. Follow the platform-specific instructions in NVIDIA’s GKE guidance rather than applying a generic node procedure.
Prerequisites that apply to either design
- GPU worker nodes with a supported NVIDIA GPU, kernel and operating-system combination.
- A supported container engine such as containerd or CRI-O.
- A client machine with
kubectland Helm for an Operator deployment. - Node Feature Discovery (NFD), either supplied by the Operator or already installed and deliberately selected as the single NFD owner.
- Permission to label namespaces and create the Operator’s cluster-scoped resources.
- A maintenance plan for GPU workloads whenever kernel modules or the host driver change.
If Pod Security Admission blocks the Operator’s privileged pods, label its namespace for privileged enforcement as described in the installation guide. A typical label command is:
kubectl label --overwrite namespace gpu-operator pod-security.kubernetes.io/enforce=privilegedUse the namespace name chosen for your installation and apply the policy required by your cluster’s security model.
Installing with an Operator-managed driver
- Inventory the cluster. Confirm the Kubernetes distribution, node operating-system versions, kernel versions, container runtime and GPU models. Check whether NFD, a device plugin, a metrics exporter or MIG tooling is already installed.
- Prepare access. Install compatible
kubectland Helm on the administration machine. Ensure the account can create namespaces, custom resources, daemonsets and privileged pods. - Decide which existing components remain. If NFD is already present, configure the chart not to deploy another NFD instance. Apply the same principle to any component your platform already manages; do not run competing device plugins or toolkit installations.
- Create or select the namespace. Apply the required Pod Security Admission label if your policy would otherwise reject privileged GPU pods.
- Install the chart using the current guide. Use the chart version and Helm values documented for your Kubernetes distribution. Leave the driver enabled unless you have deliberately chosen the pre-installed-driver path. Pin a tested chart version rather than allowing an unattended change.
- Wait for node components. Check the Operator’s pods and daemonsets, then inspect events on a GPU node. The driver, toolkit, device plugin and other enabled components should become ready without repeated crash loops.
- Verify scheduling. Confirm that nodes advertise NVIDIA GPU resources and run a small CUDA workload appropriate to your image. Also verify that the container runtime can create an NVIDIA-enabled container.
- Record ownership. Document that the Operator owns the containerized driver and list every component intentionally disabled because another system owns it.
Do not copy an installation command from an old blog post. Chart values, supported distributions and patch releases change; use the release-specific command and values in the official guide.
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Installing with pre-installed NVIDIA drivers
- Build or prepare the node image. Install a driver version supported by the GPU models, kernel and Kubernetes distribution. Reboot or load the modules as required by the host installer, then verify the driver with the host’s NVIDIA diagnostic command before joining the node to the cluster.
- Configure the container runtime. Install and configure the NVIDIA Container Toolkit if it is part of your host process. NVIDIA’s documented scenario says to set the default runtime to
nvidiawhen both the driver and toolkit are pre-installed. Restart the runtime and test a container before adding production workloads. - Join and label the node. Add the node to the cluster only after the host driver and runtime are healthy. Use taints, labels or node pools if only a subset of nodes has GPUs.
- Install the Operator with driver deployment disabled. Set
driver.enabled=false. If the host process also owns the toolkit, disable that component too. Keep the device plugin, DCGM Exporter, MIG Manager and NFD enabled only when you want the Operator to own them. - Check for duplicate ownership. Ensure that no second toolkit, device plugin or NFD deployment is competing with the host or platform installation.
- Validate from Kubernetes. Confirm that the device plugin advertises the expected GPU resources and that a test pod can access a GPU. A successful host-level
nvidia-smialone does not prove that the container runtime is configured correctly. - Put upgrades in the host change process. Record the driver branch, kernel dependency, reboot requirement, rollback image and node-drain procedure. The GPU Operator will not upgrade this driver for you.
Driver upgrades and planned maintenance
A driver upgrade is disruptive because active processes hold the NVIDIA kernel modules. NVIDIA’s GPU Driver Upgrades documentation explains that modules must be unloaded and loaded again when the driver container restarts.
Operator-managed driver sequence
- Stop, drain or otherwise disable GPU-driver clients on the target node.
- Unload the currently loaded NVIDIA modules.
- Start the updated driver pod.
- Install the updated driver and load its modules.
- Re-enable clients and return the node to service.
NVIDIA recommends the upgrade controller, which is enabled by default, to automate this process and emit metrics and events. Treat those events as part of your change verification: a pod being Running is not enough if modules failed to unload or workloads were not quiesced.
Host-installed driver sequence
- Cordon and drain the node, or use your scheduler’s equivalent maintenance mechanism, so no GPU process remains.
- Stop services or workloads that keep NVIDIA devices open.
- Apply the tested host package or replace the node with an image containing the new driver.
- Reboot or unload and reload modules according to the driver package’s requirements.
- Verify the host driver, container runtime and Kubernetes device-plugin registration.
- Uncordon the node only after a GPU test pod succeeds.
The Operator cannot perform these host-driver steps on your behalf. If your organization cannot reliably drain GPU workloads and control reboots, the Operator’s documented upgrade controller may reduce operational work, but it does not eliminate the need for a maintenance window.
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Helm and CRD upgrade planning
Upgrading the Operator is more than changing a Helm release version. NVIDIA’s upgrade guide notes that Helm does not automatically upgrade existing CustomResourceDefinitions. Plan either a manual CRD upgrade or the documented Helm-hook method, and test the resulting ClusterPolicy against the new Operator before changing production nodes.
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- Back up the current Helm values and GPU Operator custom resources.
- Read the target release notes and supported Kubernetes, OS and driver matrix.
- Upgrade CRDs using one documented method; do not assume a chart upgrade changed them.
- Upgrade the Operator during a controlled window and watch events, daemonsets and node conditions.
- Validate one canary GPU node before proceeding to the remaining pool.
Common failure modes and fixes
| Symptom | Likely cause | Action |
|---|---|---|
| Driver pods are pending or repeatedly restarting | Unsupported OS or kernel, missing privileges, incompatible image, or blocked Pod Security Admission | Inspect pod events and node labels, verify the support matrix, and apply the required privileged namespace policy. |
| Two NFD deployments are present | The cluster had NFD before the Operator installation | Disable the Operator’s NFD deployment and keep one owner. |
| GPU is visible on the host but not schedulable | Toolkit, runtime or device plugin is missing or duplicated | Check runtime configuration, device-plugin logs and advertised node resources; remove competing installations. |
| Upgrade stalls while unloading modules | A GPU process still has a device open | Find and stop GPU clients, drain the node and rerun the documented upgrade sequence. |
| New Operator features are absent after a Helm upgrade | Existing CRDs were not upgraded | Upgrade CRDs manually or enable the documented Helm hook, then reconcile the custom resources. |
| One node group works and another does not | Different OS, kernel, GPU or runtime configuration | Compare node images and labels. Use a supported Driver Custom Resource or host-installed drivers for mixed-OS designs when appropriate. |
| GKE Autopilot deployment is rejected | GPU Operator is unsupported for GKE Autopilot Pods | Use a supported GKE Standard approach and follow the platform-specific NVIDIA guidance. |
Security, reliability and ownership checklist
- Use one authoritative owner for the driver on each node: the Operator’s driver container or the host image, never both.
- Pin and test chart, driver and toolkit versions together; record the kernel and container-runtime versions that passed validation.
- Apply least-privilege namespace policies while allowing the privileged operations required by GPU driver components.
- Separate GPU node pools when OS versions, GPU generations or maintenance windows differ.
- Use canary nodes and explicit cordon/drain procedures before broad upgrades.
- Monitor Operator events, driver-module state, device-plugin registration and DCGM metrics, not just pod readiness.
- Keep a rollback path: a previous chart and CRD-compatible configuration for Operator-managed drivers, or a previous node image for host-managed drivers.
Practical decision framework
Select the GPU Operator driver
Pick this path if your GPU nodes are uniform, your team prefers Kubernetes-native lifecycle management, and you want NVIDIA’s upgrade controller and integrated components to coordinate changes. Budget for privileged pods, node maintenance windows and strict compatibility testing.
Select host-installed drivers with a reduced Operator
Pick this path if node images are already a controlled product, different GPU pools require different operating systems, or your cloud distribution prescribes host-level installation. Set driver.enabled=false, disable toolkit deployment when the host owns it, and make driver upgrades part of image release management.
Use a documented hybrid only
A hybrid is valid when the host owns the driver and the Operator owns other GPU services. It is not valid to let the host and Operator both install the same driver or toolkit. Write the ownership map into the cluster runbook so an upgrade does not silently change responsibility.
Bottom line
The GPU Operator is the more integrated choice for consistent Kubernetes GPU pools: it can deploy the driver, toolkit, device plugin, exporter, MIG Manager and NFD, and its upgrade controller can coordinate disruptive driver changes. Manual installation is the clearer choice when your host-image process must control drivers or when node groups use operating-system combinations that make a shared driver container unsuitable. In either case, verify the exact platform support, assign one lifecycle owner, and test a canary node before production rollout.
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