For AI agents: The documentation index is at https://docs.digitalocean.com/llms.txt. Markdown versions of pages use the same URL with index.html.md in place of the HTML page (for example, append index.html.md to the directory path instead of opening the HTML document).

GPU worker nodes are built on GPU Droplets, which are powered by AMD and NVIDIA GPUs. Using GPU worker nodes in your cluster, you can:

• Experiment and develop AI/ML applications in containerized environments
• Run distributed AI workloads on Kubernetes
• Scale AI inference services

Available GPU Node Pools

We offer the following GPU options for creating node pools:

You can use the 8-GPU configuration GPUs in a multi-node set up, where the GPUs are connected via a dedicated high-speed networking fabric. To learn how to configure high-speed networking for multi-node GPUs, see How to Use Multi-Node GPUs.

Runtime, Drivers, and Plugins

To run GPU workloads, you do not need to specify a Runtime Class.

DigitalOcean also installs and manages the required drivers to enable the GPU worker nodes as described below. For the latest versions installed, see the DOKS changelog.

AMD GPUs

For AMD GPUs, DigitalOcean installs the following on worker nodes:

• AMDGPU driver

Starting with DOKS 1.36.0-do.0, AMD ROCm is not installed on AMD GPU worker nodes. Use container images that bundle ROCm when your workload needs it. For AMD GPU worker images on earlier DOKS minor versions, ROCm was installed on the node; see the DOKS changelog for the ROCm versions that applied to each release.

We also recommend:

• ROCm Device Plugin for Kubernetes for GPU discovery, health checks, configuration of GPU-enabled containers, and time slicing.

  We automatically deploy this component when you create or update a cluster. You can turn this option off by setting amd_gpu_device_plugin to false in the request body when creating or updating a cluster using the API. For an opt-in DRA alternative to the device plugin, see Dynamic Resource Allocation.

• AMD Device Metrics Exporter for ingesting GPU metrics into your monitoring system.

  You can install this plugin by setting amd_gpu_device_metrics_exporter_plugin to true in the request body when creating or updating a cluster using the API. The plugin is installed in the kube-system namespace of the Kubernetes cluster.

NVIDIA GPUs

For NVIDIA GPUs, we install the following drivers and plugins:

• NVIDIA CUDA drivers

• NVIDIA CUDA Toolkit

• NVIDIA Container Toolkit

• NVIDIA device plugin for Kubernetes for GPU discovery, health checks, configuration of GPU-enabled containers, and time slicing.

  You can disable it by setting nvidia_gpu_device_plugin to false when creating or updating a cluster using the API or Terraform, or by setting --enable-nvidia-gpu-device-plugin to false using doctl. For an opt-in DRA alternative to the device plugin, see Dynamic Resource Allocation.

We also recommend:

• NVIDIA DCGM Exporter for monitoring your cluster using Prometheus.

Additional Features

Labels and Taints

DigitalOcean applies additional labels and taints to the GPU worker nodes. For more information, see Automatic Application of Labels and Taints to Nodes.

Node Readiness Controller

DOKS also uses a node readiness controller to gate scheduling on GPU nodes until the GPU drivers are healthy. For more information, see How to Use the Node Readiness Controller in DOKS.

Scaling to Zero

You can also use the cluster autoscaler to automatically scale the GPU node pool down to zero, or use the DigitalOcean CLI or API to manually scale the node pool down to 0. Autoscaling is useful when using on-demand and for jobs like training and fine-tuning.

Dynamic Resource Allocation public

Dynamic Resource Allocation (DRA) is an opt-in alternative to the Kubernetes device plugins for allocating GPUs to workloads. Starting with DOKS 1.36.3-do.0, DOKS can deploy DRA drivers for NVIDIA and AMD GPU node pools.

Enable a DRA driver by setting nvidia_gpu_dra_driver or amd_gpu_dra_driver to true when creating or updating a cluster using the API, doctl, or Terraform.

DRA drivers are mutually exclusive with the corresponding device plugins (nvidia_gpu_device_plugin and amd_gpu_device_plugin). You cannot enable both for the same GPU brand on a cluster.

DRA remains compatible with workloads that request GPUs using device-plugin-style extended resources such as nvidia.com/gpu or amd.com/gpu. Kubernetes extended resource allocation by DRA (DRAExtendedResource) creates the corresponding ResourceClaims automatically, so you do not need to update those workloads at the same time you enable the DRA driver.