Syself Autopilot supports Nvidia GPUs in Kubernetes clusters running on bare metal servers from Hetzner. You can run AI, machine learning, inference, and accelerated compute workloads directly inside Kubernetes. Typical workloads that benefit from this feature include:
To use GPUs with Syself Autopilot, first order bare metal servers with Nvidia GPUs from Hetzner.
Register them into your cluster following the bare metal onboarding guide.
After the servers join the cluster, Syself Autopilot automatically detects supported Nvidia GPUs and configures the required drivers and Kubernetes components.
Autopilot automatically installs and manages the required Nvidia components for GPU workloads, including:
This removes the need to manually SSH into nodes and do the configuration by hand.
After GPU nodes are available, applications can request GPU resources directly through standard Kubernetes manifests.
GPU resources are exposed through the nvidia.com/gpu resource type. Kubernetes schedules workloads only onto nodes with available GPU capacity.
Example:
This example requests one Nvidia GPU for the container.
Once the pod is running, Kubernetes automatically schedules it onto a GPU-enabled node.
To verify that GPUs are available in your cluster, inspect the node capacity:
You should see GPU resources exposed under the node capacity and allocatable sections:
You can also verify GPU access from inside a container using:
GPU-enabled Kubernetes clusters can run a wide range of AI and accelerated compute applications, including:
For production GPU workloads, we recommend:
If you need assistance in managing GPU-enabled clusters, we are here to help!