Manage hardware devices on Amazon EKS - Amazon EKS
Dynamic Resource Allocation vs device pluginsDRA drivers for Amazon EKSDevice plugins for Amazon EKSConsiderationsUsing DRA with KarpenterDRA ResourceClaim vs ResourceClaimTemplateTopics
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Manage hardware devices on Amazon EKS

Amazon EKS supports two Kubernetes mechanisms for managing specialized hardware devices in EKS clusters: Dynamic Resource Allocation (DRA) and device plugins. Both mechanisms enable workloads to access hardware accelerators such as NVIDIA GPUs and AWS Trainium chips, and high-performance network devices such as Elastic Fabric Adapter (EFA).

We recommend using DRA drivers for new deployments with Kubernetes versions 1.34 and later on Karpenter, EKS managed node groups, or self-managed nodes. Karpenter supports DRA with both dynamic and static capacity provisioning as of Karpenter version 1.15, though dynamic provisioning requires enabling Karpenter feature gates. For more information, see Using DRA with Karpenter. DRA is not currently supported with EKS Auto Mode. DRA provides richer device selection, topology-aware scheduling, and device sharing capabilities that are not possible with device plugins.

Reference the Kubernetes documentation for Dynamic Resource Allocation and device plugins for general information about these two Kubernetes features.

Dynamic Resource Allocation vs device plugins

Kubernetes device plugins have been the primary mechanism for exposing specialized hardware to Kubernetes workloads. Device plugins advertise devices as extended resources (for example, nvidia.com/gpu or aws.amazon.com/neuroncore) that you request in container resource requests and limits. While device plugins are widely supported and used, they have limitations:

  • Devices are requested as opaque integer counts with no attribute-based filtering.

  • No support for device sharing between containers or Pods.

  • No expressive topology-aware allocation across device types.

  • Custom scheduler extensions are often required for intelligent placement.

Dynamic Resource Allocation (DRA) is a Kubernetes feature made generally available in Kubernetes version 1.34 that addresses these limitations. With DRA, device drivers publish rich device attributes to the Kubernetes scheduler through ResourceSlice objects. You request devices using ResourceClaim and ResourceClaimTemplate objects that reference DeviceClass categories.

DRA enables:

  • Attribute-based device selection using Common Expression Language (CEL) expressions.

  • Topology-aware allocation that ensures devices are co-located on the same PCIe switch or NUMA domain.

  • Device sharing between multiple containers or Pods through shared ResourceClaim references.

  • Dynamic partitioning and sharing of NVIDIA GPUs when using MIG or time-slicing

DRA drivers for Amazon EKS

The following DRA drivers are commonly used for managing specialized hardware devices in Amazon EKS clusters.

NVIDIA DRA driver

The NVIDIA DRA driver for GPUs on GitHub enables flexible allocation and dynamic configuration of NVIDIA GPUs. See Use the NVIDIA DRA driver or device plugin on Amazon EKS for information on managing GPUs with the NVIDIA DRA driver and Use P6e-GB200 UltraServers with Amazon EKS for information on using ComputeDomains for Multi-Node NVLink (MNNVL) workloads with EC2 Grace-Blackwell instances.

EFA DRA driver

The EFA DRA driver (DRANET on GitHub) manages Elastic Fabric Adapter (EFA) device allocation with topology-aware scheduling that pairs EFA interfaces with their topologically-local GPUs or Neuron devices, and supports device sharing between Pods. For more information, see Manage EFA devices on Amazon EKS.

Neuron DRA driver

The Neuron DRA driver manages AWS Trainium and AWS Inferentia2 device allocation with topology-aware scheduling, connected device subset allocation, and Logical NeuronCore (LNC) configuration, without requiring custom scheduler extensions. For more information, see Manage Neuron devices on Amazon EKS.

Device plugins for Amazon EKS

The following device plugins are commonly used for managing specialized hardware devices in Amazon EKS clusters.

NVIDIA device plugin

The NVIDIA device plugin on GitHub advertises NVIDIA GPUs as nvidia.com/gpu extended resources and tracks the health of GPUs.

EFA device plugin

The EFA device plugin discovers all available EFA devices on each node and advertises EFA devices as vpc.amazonaws.com/efa extended resources.

Neuron device plugin

The Neuron device plugin exposes Neuron hardware as aws.amazon.com/neuroncore and aws.amazon.com/neuron extended resources. It discovers available Neuron devices on each node, advertises them as allocatable resources, and manages their lifecycle.

Considerations

Before using DRA drivers on Amazon EKS, review the following considerations:

  • DRA is available on Amazon EKS with Kubernetes version 1.33 and above, but it is recommended for Kubernetes versions 1.34 and later because of an upstream Kubernetes issue on GitHub. Your cluster control plane and nodes must be running a Kubernetes version that supports DRA.

  • DRA is not currently compatible with EKS Auto Mode.

  • Karpenter supports DRA with both dynamic and static capacity provisioning as of Karpenter version 1.15. Dynamic provisioning requires enabling Karpenter’s DRA feature gates and has additional limitations. For more information, see Using DRA with Karpenter.

  • DRA drivers and device plugins for the same device type must not run simultaneously on the same node. Uninstall the device plugin before installing the corresponding DRA driver, or deploy them on separate nodes. Running both DRA driver and device plugin for the same device on the same node can cause silent oversubscription of the underlying hardware devices.

  • DRA uses different Kubernetes API resources (ResourceClaim, ResourceClaimTemplate, DeviceClass) than device plugins (resource.limits, resource.requests). You can use DRA to manage the device plugin extended resources without changing your workload specifications. For more information about extended resources in DRA, see the Kubernetes documentation on the Kubernetes website.

  • The DRA drivers for NVIDIA, EFA, and Neuron are compatible with both the EKS-optimized AL2023 AMIs and the Bottlerocket AMIs. If you are using the NVIDIA DRA driver with Bottlerocket, disable the NVIDIA device plugin that is included in the Bottlerocket NVIDIA variants.

  • Device plugins remain fully supported for all Kubernetes versions.

Using DRA with Karpenter

Karpenter must account for the devices a Pod claims in order to decide which instance type to launch. Because DRA requests devices through ResourceClaim objects rather than extended resources, Karpenter needs explicit support to read them.

  • Static capacity provisioning — Works with DRA on any Karpenter version that supports static NodePools. Because a static NodePool maintains a fixed node count through spec.replicas rather than scaling in response to pending Pods, Karpenter never simulates the device claim. The nodes exist first and the DRA driver publishes its ResourceSlice objects from them. For more information, see the Karpenter static NodePool documentation.

  • Dynamic capacity provisioning — Supported as of Karpenter version 1.15, where Karpenter ships device metadata for supported instance types and evaluates pending ResourceClaim objects against it. This means scale-from-zero works for DRA workloads: no node with the device needs to exist first. This support is Alpha and off by default.

To enable dynamic provisioning for DRA workloads, set settings.enableDRA: true on the Karpenter Helm chart. This turns on the DRANVIDIAGPU and DRAEFA AWS feature gates and sets IGNORE_DRA_REQUESTS to false. You can instead enable a single gate through settings.awsFeatureGates.draNVIDIAGPU or settings.awsFeatureGates.draEFA if you run only one of the drivers. Do not combine settings.enableDRA with the individual gate values, because the chart fails to render.

Review the following limitations before enabling dynamic provisioning for DRA workloads:

  • Both drivers assumed — settings.enableDRA assumes that both the NVIDIA and EFA DRA drivers run on your DRA nodes. If one does not, Karpenter launches nodes for claims that can never be satisfied. Enable only the gate for each driver you actually run.

  • Instance type coverage — Karpenter evaluates claims only against instance types it ships metadata for. Support for a new instance type requires a new Karpenter release.

  • EFA network interface configuration — Configuring spec.networkInterfaces on an EC2NodeClass for EFA DRA workloads is currently unsupported and results in undefined behavior. This conflicts with the efa-only interface configuration recommended in EFA and EFA-only interfaces, so use static capacity provisioning when you need both EFA DRA and a custom interface layout.

  • ComputeDomains — Karpenter does not support dynamic provisioning for ComputeDomains, so it does not launch nodes for those claims. Multi-Node NVLink workloads that use ComputeDomains still require static capacity provisioning, EKS managed node groups, or self-managed nodes. For more information, see Use P6e-GB200 UltraServers with Amazon EKS.

  • Extended resource requests are not DRA requests — A Pod that requests nvidia.com/gpu as an extended resource is not treated as a DRA Pod, even if a DRA driver serves that resource.

  • Keep DRA and device plugin nodes separate — Karpenter’s device metadata applies to every NodePool that allows a supported instance type. Use labels and node selectors to keep DRA Pods on DRA nodes, so that a DRA driver and a device plugin never manage the same device on the same node.

For the current gate names, defaults, and supported instance types, see Utilizing GPUs and EFAs with Dynamic Resource Allocation in the Karpenter documentation.

DRA ResourceClaim vs ResourceClaimTemplate

When using DRA, you request devices through ResourceClaim or ResourceClaimTemplate objects. These two resource types serve different purposes and have different lifecycle behaviors.

ResourceClaim

A ResourceClaim is a named Kubernetes object that you create independently of any Pod. You reference it in a Pod specification by name using the resourceClaimName field. A ResourceClaim has the following characteristics:

  • It must exist in the cluster before any Pod that references it is created. If the claim does not exist, the Pod remains in a pending state.

  • It persists until you explicitly delete it, regardless of whether any Pods reference it.

  • Multiple Pods can reference the same ResourceClaim, which enables device sharing. All Pods that reference the same claim share access to the same allocated devices and are scheduled to the same node.

    Use a ResourceClaim when you need multiple Pods to share access to the same devices, or when you need a claim to exist beyond the lifetime of a single Pod.

ResourceClaimTemplate

A ResourceClaimTemplate defines a template that Kubernetes uses to automatically generate a unique ResourceClaim for each Pod. You reference it in a Pod specification using the resourceClaimTemplateName field. The ResourceClaimTemplate itself is not bound to any Pod — it is a reusable template that persists independently. A ResourceClaimTemplate has the following characteristics:

  • Kubernetes creates a new ResourceClaim for each Pod that references the template. Each Pod gets its own separate set of devices.

  • Each generated ResourceClaim is bound to the lifecycle of the Pod that triggered its creation. When the Pod is deleted, the associated generated ResourceClaim is also deleted. The ResourceClaimTemplate itself is not affected and continues to generate new claims for future Pods.

    Use a ResourceClaimTemplate when each Pod in a workload needs its own dedicated devices with similar configurations. For example, use a ResourceClaimTemplate for Pods in a Job that uses parallel execution where each Pod needs its own GPU or EFA devices.

The following table summarizes the differences between ResourceClaim and ResourceClaimTemplate.

Behavior ResourceClaim ResourceClaimTemplate

Creation

You create it manually before Pods reference it

Kubernetes generates a claim automatically per Pod

Lifecycle

Persists until you delete it

The template persists until you delete it. Each generated ResourceClaim is bound to the Pod that triggered its creation.

Device sharing across Pods

Supported. Multiple Pods can reference the same claim.

Not supported. Each Pod gets a separate claim.

Pod specification field

resourceClaimName

resourceClaimTemplateName

For examples of using ResourceClaim objects to share EFA devices between Pods, see Share EFA devices between multiple Pods. For examples of using ResourceClaimTemplate objects with topology-aware allocation, see Topology-aware EFA and GPU/Neuron device allocation.

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