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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTo run a GPU workload on Kubernetes, make the GPU visible to the cluster through a vendor device plugin, then request its advertised resource in the Pod’s container limits. For NVIDIA clusters, the GPU Operator can automate much of the node software setup. Choose whole-GPU allocation, NVIDIA MIG, or time-slicing according to your isolation and sharing needs: they are different allocation models, not interchangeable ways to divide compute.
How Kubernetes discovers and schedules GPUs
Kubernetes does not schedule a physical GPU just because one is installed in a worker node. The node needs the appropriate vendor driver and a device plugin that registers with kubelet, reports available devices and their health, and makes them available as a schedulable resource. For NVIDIA GPUs, that resource is commonly named nvidia.com/gpu; the exact name depends on the plugin and its configuration. See the Kubernetes guide to scheduling GPUs and the lower-level device plugin mechanism.
Once the plugin advertises the resource, the scheduler can place a Pod on a node with sufficient allocatable GPUs. Request the resource in the container’s limits. If you specify both a request and a limit for a GPU resource, Kubernetes requires the values to match. For example, this container resource block asks for one advertised NVIDIA GPU:
resources:
limits:
nvidia.com/gpu: 1Put the block under the appropriate container in your Pod specification; it is not a complete Pod manifest by itself. If the cluster advertises a different resource name, use that name instead. Extended device resources are integer quantities in the standard device-plugin model, and Kubernetes does not overcommit them. A device reported unhealthy by its plugin is reflected in the node’s allocatable device count.
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Direct workloads to compatible nodes
In a cluster with different GPU types or capabilities, resource availability alone may not express every workload requirement. Use node labels with a selector or node affinity to constrain placement to suitable nodes. Node Feature Discovery can publish labels for detected hardware features; useful GPU-specific attributes may also require vendor-specific discovery. Check the labels actually present in your cluster rather than assuming a universal label name.
What the device plugin does—and does not guarantee
The plugin is the bridge between vendor hardware and Kubernetes allocation: it registers with kubelet, reports devices and health, and participates in allocation. The general extended-resource model does not provide sharing or fractional GPU semantics. Sharing behavior, where available, is provided by vendor-specific mechanisms. Kubernetes also notes that the device-plugin API itself is not stable, even though Device Manager is generally available, so platform teams should account for plugin and Kubernetes compatibility when upgrading.
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What the NVIDIA GPU Operator automates
The NVIDIA GPU Operator manages much of the NVIDIA node software stack through Kubernetes. NVIDIA describes automation for drivers, the Kubernetes device plugin, NVIDIA Container Toolkit, automatic node labeling through GPU Feature Discovery (GFD), and DCGM-based monitoring. Its default installation documentation lists the driver, toolkit, device plugin, DCGM Exporter, and MIG Manager. The exact components and installation behavior depend on the operator configuration and supported platform; consult NVIDIA’s GPU Operator overview and installation guide for the version you plan to deploy.
If drivers are already installed on the host, NVIDIA documents that driver deployment can be disabled. Before installing, verify compatibility among the operator chart, GPU driver, container runtime, Kubernetes version, hardware, and platform. The operator reduces the number of node components you must assemble and manage separately; it does not decide workload sizing or remove the need for cluster-specific runtime and operational policies.
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The operator is an NVIDIA-specific management option, not a Kubernetes prerequisite. Kubernetes’ vendor device-plugin integration works independently of it, so a cluster can use a suitable vendor plugin and node setup without deploying the GPU Operator.
Choose how workloads should share GPU capacity
Start by deciding what a Pod should receive: an entire advertised GPU, a hardware-isolated MIG instance on supported hardware, or shared access through time-slicing. The latter two are NVIDIA-specific approaches, with different isolation and operational consequences.
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| Allocation model | What the workload receives | Isolation and main trade-off | Questions to check |
|---|---|---|---|
| Exclusive device-plugin allocation | A whole advertised GPU resource | The standard integer extended-resource model does not overcommit the device. | Can the workload use a whole GPU, and does the node have enough GPUs for the expected concurrency? |
| NVIDIA MIG | A supported GPU partition exposed as an instance | Instances provide hardware-level memory and fault isolation. Changing MIG configuration can require clearing user workloads from the GPU and, in some environments, rebooting the node. | Is the GPU model supported? Which instance profile fits the workload? What is the reconfiguration impact on this platform? |
| NVIDIA time-slicing | A replica representing shared access to an underlying GPU | Workloads interleave on the GPU. This does not provide MIG-style memory or fault isolation; asking for multiple shared GPUs does not guarantee proportional compute. | Can tenants tolerate contention? Is the trust model appropriate? Can you operate with the available GPU monitoring? |
The standard allocation behavior is described in the Kubernetes device plugin documentation. NVIDIA documents supported configurations and operational details for MIG and time-slicing.
When MIG is a better fit
Choose MIG when the supported GPU hardware and workload profile allow partitioning, and hardware-level memory and fault isolation between instances are important. Confirm supported GPU models and profiles in NVIDIA’s MIG documentation, then assess the consequences of changing the configuration on an active node. MIG is not available on every GPU, and its partitions are not equivalent to arbitrary fractional requests.
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When time-slicing is a better fit
Time-slicing can let multiple workloads take turns using an underlying GPU, which may suit workloads that tolerate contention and do not require MIG’s isolation guarantees. Treat each advertised replica as access to shared hardware, not as a reserved fraction of GPU compute or memory.
There is also a monitoring consequence: NVIDIA documents that DCGM Exporter does not associate metrics to containers when time-slicing is enabled with the NVIDIA Kubernetes Device Plugin. If container-attributed GPU metrics are needed for chargeback, diagnosis, or capacity planning, account for that limitation before choosing this mode.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where Dynamic Resource Allocation fits
Dynamic Resource Allocation (DRA) is separate from the ordinary device-plugin scheduling path described above. Kubernetes v1.37 documentation describes device compatibility groups as an Alpha feature that is disabled by default. A driver can use compatibility information to help the scheduler reject incompatible combinations of device partitions—such as MIG and vGPU modes on one physical GPU—before node-side preparation. This is version-specific context, not a requirement for basic GPU scheduling. Check the target cluster’s feature-gate state and driver support before depending on it. See the Kubernetes DRA feature documentation and the Kubernetes v1.37 DRA update.
Quick Recap
A practical rollout sequence
- Inventory the nodes. Confirm which worker nodes have GPUs, their models, and whether the intended allocation mode is supported.
- Choose the node software path. Install and maintain compatible vendor drivers and a device plugin directly, or use the NVIDIA GPU Operator to manage much of that stack. Check the relevant version and platform support documentation first.
- Verify advertised capacity. Inspect node capacity and allocatable resources to confirm the plugin has registered healthy devices and identify the resource name the cluster exposes.
- Set placement and resource requirements. Add the GPU resource to the workload container’s limits and use labels or affinity when a particular GPU type or feature is required.
- Decide on sharing deliberately. Use whole-device allocation by default unless sharing is needed; select MIG only on supported hardware when its isolation and configuration trade-offs fit; use time-slicing only when contention and its monitoring limitation are acceptable.
- Validate operations as well as scheduling. Confirm the container can use the device, observe the workload with the cluster’s available monitoring, and test node maintenance or reconfiguration procedures before broad rollout.
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