Getting Started

Install Docker Desktop 4.43.0+
Open the terminal and run the following command:

docker offload
Usage:  docker offload COMMAND

Docker Offload

Commands:
  accounts       Prints available accounts
  diagnose       Print diagnostic information
  start          Start a Docker Offload session
  status         Show the status of the Docker Offload connection
  stop           Stop a Docker Offload session
  version        Prints the version

Run 'docker offload COMMAND --help' for more information on a command.

Start a Docker Offload session

docker offload start

Please choose your Hub account and proceed further.

Enable or disable GPU support based on your preference.

!Tip: If you choose to enable GPU support, Docker Offload will run in an instance with an NVIDIA L4 GPU, which is useful for machine learning or compute-intensive workloads. offload1

It starts Docker Offload session through the docker account.

offload2

Verify if a new docker-cloud context is created or not.

docker context ls
NAME             DESCRIPTION                                      DOCKER ENDPOINT                                             ERROR
default          Current DOCKER_HOST based configuration          unix:///var/run/docker.sock                                 
desktop-linux    Docker Desktop                                   unix:///Users/ajeetsraina/.docker/run/docker.sock           
docker-cloud *   docker cloud context created by version v0.4.2   unix:///Users/ajeetsraina/.docker/cloud/docker-cloud.sock   
tcd              Testcontainers Desktop                           tcp://127.0.0.1:49496

By now, you should be able to able to print available accounts through CLI.

docker offload accounts
{
  "user": {
    "id": "15ee357d-XXXX-4d39-87d9-dc3b697b3392",
    "fullName": "XXX",
    "gravatarUrl": "",
    "username": "XXX",
    "state": "READY"
  },
  "orgs": [
    {
      "id": "57b45934-74c0-11e4-XXX-0242ac11001b",
      "fullName": "Docker, Inc.",
      "gravatarUrl": "https://www.gravatar.com/avatar/XXXXXa68e?s=80&r=g&d=mm",
      "orgname": "docker",
      "state": "READY"
    },
    {
      "id": "96a075XXXXXX1a78b",
      "fullName": "",
      "gravatarUrl": "",
      "orgname": "XXXX",
      "state": "READY"
    }
  ]
}

Check the status of Docker Offload.

docker offload status

Check the version

docker offload version
Docker Offload v0.4.2 build at 2025-06-30

Stop the Docker Offload

stop offload3

This command removes docker-cloud instance from your system.

Using Docker Offload using Docker Dashboard

Navigate to Settings > Beta Features to enable Docker Offload.

offload4 - Once enabled, you will need to hit the toggle button to start Docker Offload.

offload5

You can check the status by running the following command:

docker offload status

You’ll notice that Docker Offload appears in multiple places Under Models On the left sidebar of the Docker Dashboard

offload6

Verify if you’re using Cloud instance through Docker Offload.

docker info | grep -E "(Server Version|Operating System)"
 Server Version: 28.0.2
 Operating System: Ubuntu 22.04.5 LTS
ajeetsraina  ~  ♥ 16:54

You can verify the type of GPU that your remote instance is leveraging.

docker run --rm --gpus all nvidia/cuda:12.4.0-runtime-ubuntu22.04 nvidia-smi

Results:

==========
== CUDA ==
==========

CUDA Version 12.4.0

Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.

Fri Jul  4 11:26:11 2025       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.247.01             Driver Version: 535.247.01   CUDA Version: 12.4     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA L4                      Off | 00000000:31:00.0 Off |                    0 |
| N/A   44C    P0              27W /  72W |  20200MiB / 23034MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
+---------------------------------------------------------------------------------------+

It shows NVIDIA L4 GPU with 23GB of memory. You can find further details:

GPU: NVIDIA L4 (great for AI/ML workloads)
Memory: 23GB total, ~20GB already allocated
Driver: 535.247.01 with CUDA 12.4 support
Current Usage: 0% (idle)