> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/fpt-gpu-cloud/gpu-cluster/managed-k8s-with-gpu-virtual-machine/tutorial/modify-worker-groups.md).

# Modify worker groups

## Requirements:

* CPU, GPU, RAM, Storage, Instance quotas: Must be sufficient for the Worker Group configuration changes.
* The number of GPUs must meet the Min node + 1 requirement so that Worker Nodes can roll out the configuration. If using Autoscale, the number of GPUs must meet the desired Max node requirement.
* 01 Network subnet: Network used for Kubernetes Nodes, the subnet must have a Static IP Pool.

## Step by Step

**Step 1**: Access the FPT Cloud portal [console.fptcloud.com](https://console.fptcloud.com/), select Kubernetes, click on the cluster you want to change, select Node Pools, and click the "Edit Workers" icon.

![](https://content.gitbook.com/content/2FDB5fvsiFxYI972UDTv/blobs/WHV3gTvedcogJhUP0wgv/Unknown%20image)

**Step 2**: In addition to the configuration information for the standard Worker Group, you need to select the configuration for the GPU:

Select instance type: GPU

Select GPU type (A30, A100, H100, H200, etc.)

Select the GPU sharing configuration (None/Single/Mixed) Select the GPU type configuration (CPU/RAM/GPU RAM)

![](https://content.gitbook.com/content/2FDB5fvsiFxYI972UDTv/blobs/ppRPt2ROUcg5Os5R1ctk/Unknown%20image)

**Note**:

* Changing the GPU sharing method will require all GPU-related workloads to be redeployed, so before making changes, users must scale the application down to 0 to avoid errors.
* If GPU sharing was previously set to None or None with Operator, you cannot change GPU sharing to Single or Mixed.
* If you previously selected Single for GPU sharing, you can only change it to the corresponding Single modes.

**Step 3**: Review the initialization information and click Save.

**Step 4**: Monitor the initialization status of adding the Worker Group to the Kubernetes cluster. Once the status shows Successed (Running), proceed to use and deploy the application.


---

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