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docs: document supported GPU instance types including P5 family #262

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25 changes: 23 additions & 2 deletions other/gpu-workloads.mdx
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Expand Up @@ -35,14 +35,14 @@ CPU workloads.

| Setting | Description |
|---------|-------------|
| **Instance type** | Select a GPU-enabled instance type (see table below) |
| **Instance type** | Select a GPU-enabled instance type (see [Supported GPU instance types](#supported-gpu-instance-types)) |
| **Minimum nodes** | Select minimum number of nodes that will be available at all times |
| **Maximum nodes** | The upper limit for autoscaling based on demand |

![Fixed Node Group Configuration](/images/provisioning-infrastructure/cost-opt-1.png)

<Warning>
GPU instances are significantly more expensive than standard instances.
GPU instances are significantly more expensive than standard instances. Larger P-family instances (such as `p5.48xlarge`, `p5e.48xlarge`, and `p5en.48xlarge`) also require an AWS service quota increase for **Running On-Demand P instances** before they can be provisioned.
</Warning>
</Step>

Expand Down Expand Up @@ -91,6 +91,27 @@ Once your GPU node group is ready, you can deploy applications that use GPU reso
</Step>
</Steps>

## Supported GPU instance types

Porter supports the following NVIDIA-enabled EC2 instance types for fixed GPU node groups on AWS EKS clusters.

| Family | Instance types | Typical use case |
|--------|----------------|------------------|
| **G4dn** (NVIDIA T4) | `g4dn.xlarge`, `g4dn.2xlarge`, `g4dn.4xlarge` | Cost-effective inference, small models, graphics workloads |
| **G5** (NVIDIA A10G) | `g5.xlarge`, `g5.2xlarge`, `g5.4xlarge` | Mid-range inference, fine-tuning, small training jobs |
| **G6** (NVIDIA L4) | `g6.xlarge`, `g6.2xlarge`, `g6.12xlarge` | Inference, video processing, graphics |
| **G6e** (NVIDIA L40S) | `g6e.xlarge`, `g6e.2xlarge`, `g6e.4xlarge`, `g6e.8xlarge`, `g6e.12xlarge` | Generative AI inference, training of small-to-mid models |
| **P4d** (NVIDIA A100 40GB) | `p4d.24xlarge` | Large-scale distributed training |
| **P5** (NVIDIA H100) | `p5.4xlarge`, `p5.48xlarge` | Large model training and high-throughput inference |
| **P5e** (NVIDIA H200) | `p5e.48xlarge` | Frontier model training with expanded GPU memory |
| **P5en** (NVIDIA H200 + EFAv3) | `p5en.48xlarge` | Multi-node training requiring high-bandwidth networking |

<Info>
The smaller `p5.4xlarge` SKU (1× H100) is useful when you need H100-class GPUs for single-node training or inference without the cost of a full `p5.48xlarge` (8× H100). Both `p5e.48xlarge` and `p5en.48xlarge` provide 8× H200 GPUs, with `p5en` adding EFAv3 networking for distributed workloads.
</Info>

When a GPU node group is provisioned, Porter automatically labels the nodes with `porter.run/has-gpu=true` and installs the NVIDIA device plugin so pods can request GPU resources through the standard `nvidia.com/gpu` resource.

## Troubleshooting

<AccordionGroup>
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