How to Create AMD GPU Droplets

Validated on 3 Jun 2025 • Last edited on 3 Jun 2025

AMD Developer Cloud gives developers direct access to AMD GPU acceleration built on DigitalOcean infrastructure. Its preconfigured environments and transparent credit system are designed for students, researchers, and AI startups.

AMD GPU Droplets have GPUs in a single or 8 GPU configuration. They also come with two different kinds of storage: a boot disk for persistent data and a scratch disk for non-persistent data. Learn more about GPU Droplet plans and features.

We provide an AI/ML-ready image for GPU Droplets that has drivers and software from AMD preinstalled. You can also create GPU Droplets with existing Droplet images, but you need to manually install drivers and other software to use the GPUs.

Create a GPU Droplet in the Control Panel

After you log in to the control panel, click the green Create button in the top right to open the Create menu, then click GPU Droplets to go to the GPU Droplet creation page.

Choose an image

In the Choose an image section, you choose the image to use for your Droplet:

Initially, you can choose from four categories of images:

  • OS includes basic Unix-like images with no additional software (like Ubuntu, Fedora, and Debian).

  • 1-click images include pre-configured applications, like JupyterLabs.

  • Snapshots are on-demand Droplet and volume images. They’re useful to manually back up or reproducibly create Droplets.

  • Backups are automatic system-level backups taken weekly or daily. You can use backups to rebuild an existing Droplet. We retain weekly backups for four weeks, and daily backups for seven days.

  • Custom images are Unix-like images that you create and upload to your DigitalOcean team.

Once you start using Droplets, you can make backups and take snapshots of them. On the Droplet creation page, tabs for Backups and Snapshots list the backups and snapshots you’ve taken, if any. You can choose those images as the foundation to create new Droplets.

  • Choose an image. We provide an AI/ML-ready image for GPU Droplets which is based on Ubuntu 22.04 and has AMD drivers and software pre-installed. You can also use 1-Click Models which let you deploy popular third-party models directly to GPU Droplets.

    GPU Droplets also work with existing Droplet images, but you need to manually install GPU drivers.

Choose a GPU Plan

We offer a single GPU plan and an 8 GPU plan. GPU Droplets also come with a scratch disk, which is a non-persistent disk to store data for staging purposes.

Add an SSH for Authentication

More Options

In the Show more options drop-down menu, you can configure the following options:

  • Enabled automated backups(/products/backups/) allows you to schedule regular backups of your Droplet. This adds a cost to the Droplet’s price depending on the frequency of the backups.

  • Add additional storage allows you to creates and attach additional block storage volumes to your Droplet. When you click Add Volume, you can specify the details of the volume you want to create.

  • Improved metrics and monitoring.

  • Enable IPv6 (free) enables IPv6 access for your Droplet.

  • Startup scripts(/products/droplets/how-to/provide-user-data/) (free) is arbitrary data, most often a cloud-config file or a Bash script, that you can supply to a Droplet during creation. Droplets running distributions with cloud-init can consume and execute user data, which is a common way to initially configure the Droplet on first boot.

  • If you want to enable jumbo frames, you can do so with user data during creation.

Once you finish selecting your options, click Create GPU Droplet. GPU Droplets generally take longer than non-GPU Droplets to initialize, particularly for 8 GPU Droplets, so allow some time for this process to finish.

Create a GPU Droplet Using the CLI

When creating a GPU Droplet using the CLI, you need to provide values for the --region, --size, and --image flags, which specify the desired datacenter, Droplet plan, and base image for your new Droplet.

The single GPU Droplet size slug is gpu-mi300x1-192gb. The 8 GPU Droplet size slug is gpu-mi300x8-1536gb.

How to Create a New Droplet Using the DigitalOcean CLI
  1. Install doctl, the official DigitalOcean CLI.
  2. Create a personal access token and save it for use with doctl.
  3. Use the token to grant doctl access to your DigitalOcean account. 
    doctl auth init
  4. Finally, run doctl compute droplet create. Basic usage looks like this, but you can read the usage docs for more details: 
    doctl compute droplet create <droplet-name>... [flags]
    The following example creates a Droplet named example-droplet with a two vCPUs, two GiB of RAM, and 20 GBs of disk space. The Droplet is created in the nyc1 region and is based on the ubuntu-20-04-x64 image. Additionally, the command uses the --user-data flag to run a Bash script the first time the Droplet boots up: 
    doctl compute droplet create example-droplet --size s-2vcpu-2gb --image ubuntu-20-04-x64 --region nyc1 --user-data $'#!/bin/bash\n touch /root/example.txt; sudo apt update;sudo snap install doctl'

Create a Droplet Using the API

When creating a GPU Droplet using the API, you need to provide values for region, size, and image, which specify the desired datacenter, Droplet plan, and base image for your new Droplet.

The single GPU Droplet size slug is gpu-mi300x1-192gb. The 8 GPU Droplet size slug is gpu-mi300x8-1536gb.

How to Create a New Droplet Using the DigitalOcean API
  1. Create a personal access token and save it for use with the API.
  2. Send a POST request to https://api.digitalocean.com/v2/droplets.

cURL

Using cURL:

curl -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DIGITALOCEAN_TOKEN" \
  -d '{"name":"example.com","region":"nyc3","size":"s-1vcpu-1gb","image":"ubuntu-20-04-x64","ssh_keys":[289794,"3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"],"backups":true,"ipv6":true,"monitoring":true,"tags":["env:prod","web"],"user_data":"#cloud-config\nruncmd:\n  - touch /test.txt\n","vpc_uuid":"760e09ef-dc84-11e8-981e-3cfdfeaae000"}' \
  "https://api.digitalocean.com/v2/droplets"

Go

Using Godo, the official DigitalOcean API client for Go:

import (
    "context"
    "os"

    "github.com/digitalocean/godo"
)

func main() {
    token := os.Getenv("DIGITALOCEAN_TOKEN")

    client := godo.NewFromToken(token)
    ctx := context.TODO()

    createRequest := &godo.DropletCreateRequest{
        Name:   "example.com",
        Region: "nyc3",
        Size:   "s-1vcpu-1gb",
        Image: godo.DropletCreateImage{
            Slug: "ubuntu-20-04-x64",
        },
        SSHKeys: []godo.DropletCreateSSHKey{
            godo.DropletCreateSSHKey{ID: 289794},
            godo.DropletCreateSSHKey{Fingerprint: "3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"}
        },
        Backups: true,
        IPv6: true,
        Monitoring: true,
        Tags: []string{"env:prod","web"},
        UserData: "#cloud-config\nruncmd:\n  - touch /test.txt\n",
        VPCUUID: "760e09ef-dc84-11e8-981e-3cfdfeaae000",
    }

Ruby

Using DropletKit, the official DigitalOcean API client for Ruby:

require 'droplet_kit'
token = ENV['DIGITALOCEAN_TOKEN']
client = DropletKit::Client.new(access_token: token)

droplet = DropletKit::Droplet.new(
  name: 'example.com',
  region: 'nyc3',
  size: 's-1vcpu-1gb',
  image: 'ubuntu-20-04-x64',
  ssh_keys: [289794,"3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"],
  backups: true,
  ipv6: true,
  monitoring: true,
  tags: ["env:prod","web"],
  user_data: "#cloud-config\nruncmd:\n  - touch /test.txt\n",
  vpc_uuid: "760e09ef-dc84-11e8-981e-3cfdfeaae000",
)
client.droplets.create(droplet)

Python

Using PyDo, the official DigitalOcean API client for Python:

import os
from pydo import Client

client = Client(token=os.environ.get("DIGITALOCEAN_TOKEN"))

req = {
  "name": "example.com",
  "region": "nyc3",
  "size": "s-1vcpu-1gb",
  "image": "ubuntu-20-04-x64",
  "ssh_keys": [
    289794,
    "3b:16:e4:bf:8b:00:8b:b8:59:8c:a9:d3:f0:19:fa:45"
  ],
  "backups": True,
  "ipv6": True,
  "monitoring": True,
  "tags": [
    "env:prod",
    "web"
  ],
  "user_data": "#cloud-config\nruncmd:\n  - touch /test.txt\n",
  "vpc_uuid": "760e09ef-dc84-11e8-981e-3cfdfeaae000"
}

resp = client.droplets.create(body=req)

We can't find any results for your search.

Try using different keywords or simplifying your search terms.