diff --git a/other/gpu-workloads.mdx b/other/gpu-workloads.mdx
index 601b865..c46d43c 100644
--- a/other/gpu-workloads.mdx
+++ b/other/gpu-workloads.mdx
@@ -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>
 
@@ -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>