> ## Documentation Index
> Fetch the complete documentation index at: https://docs.porter.run/llms.txt
> Use this file to discover all available pages before exploring further.
# Running GPU workloads
> Create fixed GPU node groups with NVIDIA-enabled instances and deploy GPU-accelerated machine learning or inference workloads on Porter
This guide walks you through setting up GPU infrastructure on Porter, from
creating a GPU node group to deploying your first GPU-accelerated application.
You can deploy GPU-enabled workloads on Porter by creating a fixed node group
and selecting a GPU-enabled instance type. Note that this has to be the
**second** node group in your cluster as the default node group is reserved for
CPU workloads.
GPUs are only supported on fixed node groups because cost optimization does not currently support GPU instances. This means you select a specific GPU instance type and Porter scales by adding or removing instances of that exact type.
## Creating a GPU Node Group
From your Porter dashboard, click on the **Infrastructure** tab in the left sidebar.
Click on **Cluster** to view your cluster configuration and existing node groups.
Click **Add an additional node group** to open the node group configuration panel.
Configure your GPU node group with the following settings:
| Setting | Description |
| ----------------- | ------------------------------------------------------------------ |
| **Instance type** | Select a GPU-enabled instance type (see table below) |
| **Minimum nodes** | Select minimum number of nodes that will be available at all times |
| **Maximum nodes** | The upper limit for autoscaling based on demand |
GPU instances are significantly more expensive than standard instances.
Click **Save** to create the node group. Porter will provision the GPU nodes in your cluster. This may take a few minutes as GPU nodes often require additional driver installation.
## Deploying a GPU Application
Once your GPU node group is ready, you can deploy applications that use GPU resources.
Navigate to your application in the Porter dashboard, or create a new one if you haven't already.
Click on the **Services** tab to view your application's services.
Click on the service that needs GPU access (e.g., your inference worker or training job).
Under **General**, find the **Node group** selector and choose your GPU node group from the dropdown.
Under **Resources**, configure your GPU requirements:
| Setting | Recommended Value |
| ------- | ------------------------------------------------------------------- |
| **GPU** | Number of GPUs needed (typically 1) |
| **CPU** | Match your workload needs (GPU instances have fixed CPU/RAM ratios) |
| **RAM** | Match your workload needs |
Request only the GPUs you need. Each GPU request reserves an entire GPU.
Save your changes and redeploy the application. Porter will schedule your workload on the GPU node group.
## Troubleshooting
This usually means there are no available GPU nodes. Check:
* The node group has scaled up (check Infrastructure → Cluster)
* Your GPU request doesn't exceed available GPUs on the instance type
* The node group maximum nodes hasn't been reached
GPU memory errors indicate your model or batch size is too large:
* Reduce batch size
* Use a larger GPU instance with more VRAM
GPU nodes take longer to start than CPU nodes due to driver initialization:
* Keep minimum nodes at 1 for latency-sensitive workloads
* Consider keeping a warm pool of nodes during peak hours