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To schedule a GPU workload on Kubernetes, a GPU node must have the vendor driver and a compatible device plugin. The plugin advertises an integer extended resource such as nvidia.com/gpu or amd.com/gpu. Request that resource in the container’s resources.limits, use a node selector or affinity rule when a particular GPU model is required, and cap namespace consumption with a ResourceQuota key such as requests.nvidia.com/gpu: "4". Kubernetes then admits Pods only when the namespace has quota headroom and schedules them only where the requested GPU, CPU, memory, and placement constraints can all be met.
This guide covers the device-plugin workflow, quota accounting, verification, common failures, and how the newer Dynamic Resource Allocation (DRA) model differs. DRA manifests and feature availability vary by Kubernetes release, so treat it as a version-specific alternative rather than a drop-in replacement for the basic workflow.
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What Kubernetes needs before it can schedule a GPU
Kubernetes does not discover usable GPUs by itself. Each GPU node needs the hardware vendor’s driver and a device plugin running as a DaemonSet or equivalent node-level component. The plugin registers with the kubelet and advertises an extended resource for the devices it can allocate. The resource name is vendor- and plugin-dependent; common examples include nvidia.com/gpu and amd.com/gpu. Read the actual resource name from your cluster rather than assuming one.
The Kubernetes device-plugin documentation describes registration and extended-resource behavior. After installation, confirm that each intended node reports the GPU resource under both capacity and allocatable resources. A node that has a physical card but no advertised extended resource cannot satisfy a GPU request.
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Extended resources are whole, integer devices
Device-plugin GPU resources are integer resources. A request for one GPU means one allocatable device, and the standard extended-resource path does not overcommit or divide that resource between Pods. Basic limits therefore do not provide time-slicing or fractional sharing. If your workload needs a particular sharing model or device property, verify that the vendor integration and your Kubernetes version support it; do not infer those capabilities from an ordinary nvidia.com/gpu request.
The device-plugin scheduling sequence
Use this sequence for a conventional whole-GPU workload.
1. Prepare and inspect GPU nodes
Install the vendor driver and the matching device plugin on every node that should run GPU workloads. Then inspect the node objects:
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In the Capacity and Allocatable sections, look for the extended resource advertised by your plugin. The allocatable value is the amount Kubernetes can assign after system reservations and other node-level accounting. If the resource is absent or allocatable capacity is zero, fix the driver/plugin installation before changing a Pod manifest.
The Schedule GPUs task provides the vendor setup context and the resource declaration expected by Kubernetes. Check every intended GPU node, not just one: a DaemonSet may be healthy on one node while failing registration on another because of a driver mismatch, a taint, or a node-specific runtime problem.
2. Put the GPU in the container’s limits
For the device-plugin workflow, declare the GPU under resources.limits:
resources:
limits:
nvidia.com/gpu: 1Replace nvidia.com/gpu with the exact resource name exposed by your cluster. Kubernetes documentation explicitly allows a GPU limit without a separate request: “You can specify GPU limits without specifying requests, because Kubernetes will use the limit as the request value by default.” If you write both fields, the request and limit must be equal for this extended resource.
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The declaration belongs inside the relevant container, not only at Pod level. A Pod with multiple containers must account for the resource on the container that actually needs the GPU. Keep the count an integer supported by the node; a request of 2 cannot fit on a node with only one allocatable GPU. If two containers each request one GPU, Kubernetes accounts for two devices for that Pod, so place the declaration only where the application really needs it.
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3. Target the right hardware
GPU nodes are not necessarily interchangeable. A model, memory size, architecture, driver capability, or attached accelerator feature may matter to your application. Label nodes with distinctions that are meaningful in your environment, then constrain placement with a selector or affinity rule. Labels are operator-defined; the key and value below are examples and must already exist on the target nodes.
nodeSelector:
gpu.example.com/model: a100For more expressive placement, use required node affinity:
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.example.com/model
operator: In
values:
- a100A selector or required affinity that matches no node makes the Pod unschedulable even when free GPUs exist elsewhere. Keep the label taxonomy consistent and document who maintains it. Kubernetes’ GPU scheduling guidance also notes that Node Feature Discovery can automate hardware discovery and labeling. If you use it, verify the labels it creates before relying on them in production manifests.
Placement constraints do not replace the extended-resource request. A selector can identify an A100 node, but Kubernetes still needs an allocatable nvidia.com/gpu unit there. Conversely, requesting a GPU without a selector allows any node advertising that resource, which may be correct for portable workloads but risky when binaries or memory requirements depend on a specific model.
4. Cap namespace consumption with ResourceQuota
Create a quota in the namespace that owns the workload. For a four-GPU NVIDIA allowance, the documented pattern is:
apiVersion: v1
kind: ResourceQuota
metadata:
name: gpu-quota
spec:
hard:
requests.nvidia.com/gpu: "4"The key uses the requests. prefix because extended resources are accounted as requests for quota purposes. The value is an aggregate ceiling across Pods in that namespace, not a per-Pod limit. Four Pods requesting one GPU each can consume the allowance; a fifth one-GPU Pod is rejected until existing usage falls below the hard limit. A single Pod requesting four GPUs can consume the entire quota.
Use the resource name that your plugin advertises. A quota for requests.nvidia.com/gpu does not limit a separately named resource such as amd.com/gpu. The Resource Quotas documentation covers extended-resource syntax and usage accounting.
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5. Set CPU and memory independently
A GPU count does not allocate CPU or memory. Define CPU and memory requests and limits from the application’s actual needs and the capacity of the target nodes:
resources:
requests:
cpu: "4"
memory: 16Gi
limits:
cpu: "8"
memory: 32Gi
nvidia.com/gpu: 1Kubernetes evaluates these dimensions independently. CPU and memory requests influence which nodes can fit the Pod, while limits define the container’s allowed ceiling. If the namespace has CPU or memory quota keys, admission may require explicit request or limit values on every incoming container. A Pod can therefore have enough GPU quota and still be rejected for missing CPU or memory fields, or remain pending because no node has the requested combination of GPU, CPU, and memory.
Do not use a GPU value as a substitute for either CPU or memory. A training container may need one GPU but 16 GiB of memory; another may need the same GPU count and 64 GiB. Set those fields deliberately, and check that sidecar containers also satisfy any namespace policy requiring resource declarations.
6. Apply and verify admission
Apply the workload and inspect both the Pod and its namespace quota:
kubectl apply -f workload.yaml
kubectl get pod <pod-name> -n <namespace>
kubectl describe pod <pod-name> -n <namespace>
kubectl describe resourcequota gpu-quota -n <namespace>A successfully admitted Pod may still be Pending while the scheduler searches for a fitting node. Pod events usually identify whether the blocker is insufficient GPU capacity, an affinity mismatch, missing CPU or memory, or another constraint. The quota description shows hard limits and current usage, which lets you distinguish a namespace admission failure from a node-placement failure.
Compare the Pod’s declared resource name and count with the node’s Allocatable entry character-for-character. Also check the namespace named in the manifest: a quota in one namespace has no effect on a workload submitted to another. When a deployment creates several replicas, account for the sum of all replicas and their sidecars rather than validating only one template instance.
7. Confirm the runtime sees the device
After scheduling, check the running container’s logs and application-level GPU initialization. Kubernetes scheduling proves that a device was allocated according to the plugin contract; it does not prove that the application selected the expected device, loaded a compatible library, or has sufficient device memory. Those checks belong in the image’s startup diagnostics and operational monitoring.
A successful schedule also does not guarantee that every process in the container uses the device efficiently. Validate the vendor runtime and application framework inside the image, and record failures separately from scheduler failures so that changing quota or affinity does not mask an image or driver problem.
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How requests, limits, and quotas interact
| Control | What it answers | GPU behavior |
|---|---|---|
| Container limit | How many devices this container asks Kubernetes to allocate | Set the extended resource under limits; a limit-only declaration becomes the request. |
| Container request | Minimum resource amount the scheduler must fit on a node | If specified for a GPU, it must equal the GPU limit. |
| Node allocatable | How much capacity remains assignable on a node | Must include enough of the plugin-advertised integer resource. |
| ResourceQuota | How much aggregate capacity a namespace may claim | Use an extended-resource key with the requests. prefix. |
| CPU and memory fields | Whether the Pod fits non-GPU resources and meets admission policy | Configured independently; GPU values do not substitute for them. |
These controls operate at different scopes. A node can have a free GPU while the namespace quota is exhausted; conversely, a namespace can have quota headroom while no node has the requested GPU plus the required CPU, memory, and labels. Diagnose the failing scope instead of increasing every value indiscriminately.
Quota design details and pitfalls
Use a separate quota object or a clearly named section for GPU capacity so operators can inspect it without confusing it with CPU and memory policy. The GPU key must be an extended-resource request key, and its value should be written as a string in YAML, as in "4". Keep the number consistent with the approval boundary for that namespace; quota is an enforcement mechanism, not a recommendation to consume all available cards.
Remember that quota counts admitted resource claims across the namespace. Completed or deleted workloads release their claims, while still-running replicas continue to count. A rollout that temporarily creates old and new ReplicaSets can therefore need more headroom than the steady-state replica count. Check current usage before starting a large job, and avoid assuming that a failed scheduling attempt has consumed a GPU: admission and scheduling are separate stages.
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Choosing between device plugins and DRA
The device-plugin path is the straightforward default when an application needs whole GPU devices exposed as an extended resource. Kubernetes also documents Dynamic Resource Allocation (DRA), which introduces device claims and drivers for more expressive allocation. DRA details, APIs, feature gates, and vendor support depend on the Kubernetes release and cluster configuration.
| Decision point | Device-plugin extended resource | DRA |
|---|---|---|
| Version requirements | Uses the established device-plugin and extended-resource APIs supported by the cluster. | Check the DRA API and feature gates for the exact Kubernetes release. |
| Vendor integration | Requires a compatible device plugin and installed vendor driver. | Requires a compatible DRA driver in addition to the underlying device support. |
| Selection model | Request an integer resource such as nvidia.com/gpu: 1; use labels or affinity for node-level distinctions. |
Use device claims and DRA selectors defined by the driver and release. |
| Sharing and properties | Extended resources are not overcommitted and represent whole integer allocations. | May express richer device properties when the driver and version support them; verify before designing around that capability. |
| Quota planning | Quota keys use the extended resource’s requests. name, such as requests.nvidia.com/gpu. |
Check the release-specific quota and accounting behavior described for the DRA objects you deploy. |
The DRA API objects documentation explains the claim-oriented model. The Assign Extended Resources to a Container task provides additional context for extended resources and DRA support. Keep DRA manifests separate from device-plugin manifests so an upgrade or feature-gate change does not silently alter allocation semantics.
Before choosing DRA, identify the exact Kubernetes release, enabled feature gates, and vendor driver version. Confirm how claims are created, referenced by Pods, and accounted for by quota in that release. A manifest copied from documentation for another release may be syntactically valid yet unsupported by the API server or driver. For a simple whole-device request, the established device-plugin path usually has fewer moving parts.
Troubleshooting by symptom
| Symptom | Likely cause | What to check and change |
|---|---|---|
| Pod rejected with a quota error | The namespace has no remaining GPU quota, or a CPU/memory quota requirement is unmet. | Run kubectl describe resourcequota in the namespace. Compare current usage with hard limits, then wait for or remove completed workloads or revise the approved quota. |
| Pod remains Pending with insufficient GPU capacity | No node has enough allocatable units of the requested extended resource. | Inspect node allocatable values and existing GPU consumers. Reduce the request only if the application can run with fewer devices; otherwise add or free correctly configured capacity. |
| Pod remains Pending after adding a selector | The selector or required affinity matches no GPU node, or labels are misspelled. | List node labels and compare them character-for-character with the manifest. Remove the constraint only when the workload truly supports any GPU type. |
| Physical GPU exists but Kubernetes reports none | The driver is missing, incompatible, unhealthy, or the device plugin is not registered on that node. | Inspect the plugin Pod, kubelet events, and node capacity. Repair the node/plugin path before editing application resources. |
| GPU schedules but the application fails at startup | The container image or libraries do not match the installed driver/runtime, or the application expects capabilities not provided by the allocated device. | Review container logs and the vendor runtime requirements. Kubernetes allocation alone does not validate application compatibility. |
| Two workloads cannot share one GPU | The basic extended-resource workflow treats GPUs as non-overcommittable integer resources. | Do not assign the same whole device through ordinary requests. Investigate a vendor-supported sharing mechanism or a DRA design that your cluster release explicitly supports. |
| One replica schedules while later replicas are rejected | The first replica consumed the remaining namespace quota or node capacity. | Check quota usage and each node’s allocatable value. Calculate total replica demand before scaling the controller. |
| Quota appears available but admission still fails | Another quota dimension, commonly CPU or memory, requires fields that the container omits. | Inspect every quota object in the namespace and add explicit CPU and memory requests or limits where policy requires them. |
Operational checklist
- Record the exact extended-resource name advertised by each GPU plugin.
- Verify driver health, plugin registration, node capacity, and node allocatable values before deploying workloads.
- Use GPU limits in every GPU-consuming container; keep request and limit equal if both are written.
- Label nodes for meaningful hardware differences and test selectors or affinity against real node labels.
- Set CPU and memory requests and limits separately from GPU values.
- Apply a namespace quota with the
requests.<extended-resource>key and an explicitly approved aggregate value. - Check quota usage and Pod events whenever admission or scheduling fails.
- Review the Kubernetes release documentation before enabling DRA or relying on a DRA-specific sharing or property-selection feature.
- Account for all replicas and sidecars when estimating quota consumption.
- Validate application-level GPU initialization after Kubernetes reports the Pod running.
Reference documentation
- Schedule GPUs
- Device Plugins
- Resource Quotas
- DRA API Objects
- Assign Extended Resources to a Container
Frequently Asked Questions
Can I put a GPU only under requests?
For the device-plugin GPU workflow, Kubernetes documentation expects the GPU resource in limits. A limit-only declaration becomes the request automatically. If both fields are present, they must be equal; do not rely on a GPU request that differs from its limit.
How do I limit a namespace to four GPUs?
Create a ResourceQuota in that namespace with hard.requests.<extended-resource>: "4", for example requests.nvidia.com/gpu: "4". This is an aggregate namespace ceiling, not a four-GPU allowance per Pod.
Why is a Pod pending even though the node has a GPU?
The node may lack enough allocatable GPU after existing consumers and system reservations, or the Pod may also require CPU, memory, and labels that no node satisfies. Inspect Pod events and compare the request with each node’s allocatable resources and labels.
Does a GPU quota limit AMD and NVIDIA devices together?
No. Quota keys use the exact extended-resource name. A quota for requests.nvidia.com/gpu does not cap amd.com/gpu or another separately advertised resource.
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Not through the basic device-plugin extended-resource path. Those resources are integer, non-overcommittable devices. Use only a vendor-supported sharing mechanism or a DRA design explicitly supported by your Kubernetes release and driver.
Do I need CPU and memory values when requesting a GPU?
A GPU request does not set CPU or memory. Declare those resources independently so the scheduler can find a node with the full combination. If namespace quotas require CPU or memory requests or limits, omitting them can cause admission to fail even when GPU quota remains.
When should I choose DRA instead of a device plugin?
Use the established device-plugin path for a straightforward whole-GPU allocation. Consider DRA only after checking the exact Kubernetes version, feature gates, vendor DRA driver, claim syntax, device properties, sharing model, and quota-accounting behavior supported by that release.
The Bottom Line
Expose GPUs through a healthy vendor driver and device plugin, request the advertised integer resource under container limits, and constrain nodes with accurate labels when hardware matters. Enforce the namespace-wide ceiling with a ResourceQuota key such as requests.nvidia.com/gpu, while setting CPU and memory independently. If scheduling or admission fails, inspect quota usage, node allocatable capacity, Pod events, and label matches before changing the workload. Treat DRA as a release-specific alternative whose drivers, claims, feature gates, and accounting must be verified for the cluster you operate.
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