Create an N1 VM that has attached GPUs

Linux Windows

This document explains how to create a VM that has attached GPUs and uses an N1 machine family. You can use most N1 machine types except the N1 shared-core.

Before you begin

To review additional prerequisite steps such as selecting an OS image and checking GPU quota, review the overview document.
If you haven't already, then set up authentication. Authentication is the process by which your identity is verified for access to Google Cloud services and APIs. To run code or samples from a local development environment, you can authenticate to Compute Engine by selecting one of the following options:

Select the tab for how you plan to use the samples on this page:
Console

When you use the Google Cloud console to access Google Cloud services and APIs, you don't need to set up authentication.
gcloud
1. Install the Google Cloud CLI, then initialize it by running the following command:
  gcloud init
  Note: If you installed the gcloud CLI previously, make sure you have the latest version by running gcloud components update.
2. Set a default region and zone.
REST

To use the REST API samples on this page in a local development environment, you use the credentials you provide to the gcloud CLI.
For more information, see Authenticate for using REST in the Google Cloud authentication documentation.

Required roles

To get the permissions that you need to create VMs, ask your administrator to grant you the Compute Instance Admin (v1) (roles/compute.instanceAdmin.v1) IAM role on the project. For more information about granting roles, see Manage access to projects, folders, and organizations.

This predefined role contains the permissions required to create VMs. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

The following permissions are required to create VMs:

compute.instances.create on the project
To use a custom image to create the VM: compute.images.useReadOnly on the image
To use a snapshot to create the VM: compute.snapshots.useReadOnly on the snapshot
To use an instance template to create the VM: compute.instanceTemplates.useReadOnly on the instance template
To assign a legacy network to the VM: compute.networks.use on the project
To specify a static IP address for the VM: compute.addresses.use on the project
To assign an external IP address to the VM when using a legacy network: compute.networks.useExternalIp on the project
To specify a subnet for your VM: compute.subnetworks.use on the project or on the chosen subnet
To assign an external IP address to the VM when using a VPC network: compute.subnetworks.useExternalIp on the project or on the chosen subnet
To set VM instance metadata for the VM: compute.instances.setMetadata on the project
To set tags for the VM: compute.instances.setTags on the VM
To set labels for the VM: compute.instances.setLabels on the VM
To set a service account for the VM to use: compute.instances.setServiceAccount on the VM
To create a new disk for the VM: compute.disks.create on the project
To attach an existing disk in read-only or read-write mode: compute.disks.use on the disk
To attach an existing disk in read-only mode: compute.disks.useReadOnly on the disk

You might also be able to get these permissions with custom roles or other predefined roles.

Overview

The following GPU models can be attached to VMs that use N1 machine families.

NVIDIA GPUs:

NVIDIA T4: nvidia-tesla-t4
NVIDIA P4: nvidia-tesla-p4
NVIDIA P100: nvidia-tesla-p100
NVIDIA V100: nvidia-tesla-v100

NVIDIA RTX Virtual Workstation (vWS) (formerly known as NVIDIA GRID):

NVIDIA T4 Virtual Workstation: nvidia-tesla-t4-vws
NVIDIA P4 Virtual Workstation: nvidia-tesla-p4-vws
NVIDIA P100 Virtual Workstation: nvidia-tesla-p100-vws

For these virtual workstations, an NVIDIA RTX Virtual Workstation (vWS) license is automatically added to your VM.

Create a VM that has attached GPUs

You can create an N1 VM that has attached GPUs by using either the Google Cloud console, Google Cloud CLI, or REST.

Console

In the Google Cloud console, go to the Create an instance page.

Go to Create an instance
Specify a Name for your VM. See Resource naming convention.
Select a region and zone where GPUs are available. See the list of available GPU zones.
In the Machine configuration section, select the GPUs machine family, and then do the following:
1. In the GPU type list, select one of the GPU models supported on N1 machines.
2. In the Number of GPUs list, select the number of GPUs.
3. If your GPU model supports NVIDIA RTX Virtual Workstations (vWS) for graphics workloads, and you plan on running graphics-intensive workloads on this VM, select Enable Virtual Workstation (NVIDIA GRID).
4. In the Machine type list, select one of the preset N1 machine types. Alternatively, you can also specify custom machine type settings.
In the Boot disk section, click Change. This opens the Boot disk configuration page.
On the Boot disk configuration page, do the following:
1. On the Public images tab, choose a supported Compute Engine image or Deep Learning VM Images.
2. Specify a boot disk size of at least 40 GB.
3. To confirm your boot disk options, click Select.
Optional: Configure provisioning model. For example, if your workload is fault-tolerant and can withstand possible VM preemption, consider using Spot VMs to reduce the cost of your VMs and the attached GPUs. For more information, see GPUs on Spot VMs. To do this, complete the following steps:
1. In the Availability policies section, select Spot from the VM provisioning model list. This setting disables automatic restart and host maintenance options for the VM.
2. Optional: In the On VM termination list, select what happens when Compute Engine preempts the VM:
  - To stop the VM during preemption, select Stop (default).
  - To delete the VM during preemption, select Delete.
To create and start the VM, click Create.

gcloud

To create and start a VM use the gcloud compute instances create command with the following flags.

If your workload is fault-tolerant and can withstand possible VM preemption, consider using Spot VMs to reduce the cost of your VMs and the attached GPUs. For more information, see GPUs on Spot VMs. The --provisioning-model=SPOT is an optional flag that configures your VMs as Spot VMs. For Spot VMs, the automatic restart and host maintenance options flags are disabled.

gcloud compute instances create VM_NAME \
    --machine-type MACHINE_TYPE \
    --zone ZONE \
    --boot-disk-size DISK_SIZE \
    --accelerator type=ACCELERATOR_TYPE,count=ACCELERATOR_COUNT \
    [--image IMAGE | --image-family IMAGE_FAMILY] \
    --image-project IMAGE_PROJECT \
    --maintenance-policy TERMINATE \
    [--provisioning-model=SPOT]

Replace the following:

VM_NAME: the name for the new VM.
MACHINE_TYPE: the machine type that you selected for your VM.
ZONE: the zone for the VM. This zone must support the GPU type.
DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.
IMAGE or IMAGE_FAMILY that supports GPUs. Specify one of the following:
- IMAGE: the required version of a public image. For example, --image debian-10-buster-v20200309.
- IMAGE_FAMILY: an image family. This creates the VM from the most recent, non-deprecated OS image. For example, if you specify --image-family debian-10, Compute Engine creates a VM from the latest version of the OS image in the Debian 10 image family.
You can also specify a custom image or Deep Learning VM Images.
IMAGE_PROJECT: the Compute Engine image project that the image family belongs to. If using a custom image or Deep Learning VM Images, specify the project that those images belong to.
ACCELERATOR_COUNT: the number of GPUs that you want to add to your VM. See GPUs on Compute Engine for a list of GPU limits based on the machine type of your VM.
ACCELERATOR_TYPE: the GPU model that you want to use. If you plan on running graphics-intensive workloads on this VM, use one of the virtual workstation models.

Choose one of the following values:
- NVIDIA GPUs:
  - NVIDIA T4: nvidia-tesla-t4
  - NVIDIA P4: nvidia-tesla-p4
  - NVIDIA P100: nvidia-tesla-p100
  - NVIDIA V100: nvidia-tesla-v100
- NVIDIA RTX Virtual Workstation (vWS) (formerly known as NVIDIA GRID):
  - NVIDIA T4 Virtual Workstation: nvidia-tesla-t4-vws
  - NVIDIA P4 Virtual Workstation: nvidia-tesla-p4-vws
  - NVIDIA P100 Virtual Workstation: nvidia-tesla-p100-vws
    
    For these virtual workstations, an NVIDIA RTX Virtual Workstation (vWS) license is automatically added to your VM.

Example

For example, you can use the following gcloud command to start an Ubuntu 22.04 VM with 1 NVIDIA T4 GPU and 2 vCPUs in the us-east1-d zone.

gcloud compute instances create gpu-instance-1 \
    --machine-type n1-standard-2 \
    --zone us-east1-d \
    --boot-disk-size 40GB \
    --accelerator type=nvidia-tesla-t4,count=1 \
    --image-family ubuntu-2204-lts \
    --image-project ubuntu-os-cloud \
    --maintenance-policy TERMINATE

REST

Identify the GPU type that you want to add to your VM. Submit a GET request to list the GPU types that are available to your project in a specific zone.

If your workload is fault-tolerant and can withstand possible VM preemption, consider using Spot VMs to reduce the cost of your VMs and the attached GPUs. For more information, see GPUs on Spot VMs. The "provisioningModel": "SPOT" is an optional parameter that configures your VMs as Spot VMs. For Spot VMs, the automatic restart and host maintenance options flags are disabled.

GET https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/zones/ZONE/acceleratorTypes

Replace the following:

PROJECT_ID: project ID.
ZONE: zone from which you want to list the available GPU types.

Send a POST request to the instances.insert method. Include the acceleratorType parameter to specify which GPU type you want to use, and include the acceleratorCount parameter to specify how many GPUs you want to add. Also set the onHostMaintenance parameter to TERMINATE.

POST https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/zones/ZONE/instances
{
  "machineType": "projects/PROJECT_ID/zones/ZONE/machineTypes/MACHINE_TYPE",
  "disks":
  [
    {
      "type": "PERSISTENT",
      "initializeParams":
      {
        "diskSizeGb": "DISK_SIZE",
        "sourceImage": "projects/IMAGE_PROJECT/global/images/family/IMAGE_FAMILY"
      },
      "boot": true
    }
  ],
  "name": "VM_NAME",
  "networkInterfaces":
  [
    {
      "network": "projects/PROJECT_ID/global/networks/NETWORK"
    }
  ],
  "guestAccelerators":
  [
    {
      "acceleratorCount": ACCELERATOR_COUNT,
      "acceleratorType": "projects/PROJECT_ID/zones/ZONE/acceleratorTypes/ACCELERATOR_TYPE"
    }
  ],
  "scheduling":
  {
    ["automaticRestart": true],
    "onHostMaintenance": "TERMINATE",
    ["provisioningModel": "SPOT"]
  },
}

Replace the following:

VM_NAME: the name of the VM.
PROJECT_ID: your project ID.
ZONE: the zone for the VM. This zone must support the GPU type.
MACHINE_TYPE: the machine type that you selected for the VM. See GPUs on Compute Engine to see what machine types are available based on your desired GPU count.
IMAGE or IMAGE_FAMILY: specify one of the following:
- IMAGE: the required version of a public image. For example, "sourceImage": "projects/debian-cloud/global/images/debian-10-buster-v20200309"
- IMAGE_FAMILY: an image family. This creates the VM from the most recent, non-deprecated OS image. For example, if you specify "sourceImage": "projects/debian-cloud/global/images/family/debian-10", Compute Engine creates a VM from the latest version of the OS image in the Debian 10 image family.
You can also specify a custom image or Deep Learning VM Images.
IMAGE_PROJECT: the Compute Engine image project that the image family belongs to. If using a custom image or Deep Learning VM Images, specify the project that those images belong to.
DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.
NETWORK: the VPCnetwork that you want to use for the VM. You can specify default to use your default network.
ACCELERATOR_COUNT: the number of GPUs that you want to add to your VM. See GPUs on Compute Engine for a list of GPU limits based on the machine type of your VM.
ACCELERATOR_TYPE: the GPU model that you want to use. If you plan on running graphics-intensive workloads on this VM, use one of the virtual workstation models.

Choose one of the following values:
- NVIDIA GPUs:
  - NVIDIA T4: nvidia-tesla-t4
  - NVIDIA P4: nvidia-tesla-p4
  - NVIDIA P100: nvidia-tesla-p100
  - NVIDIA V100: nvidia-tesla-v100
- NVIDIA RTX Virtual Workstation (vWS) (formerly known as NVIDIA GRID):
  - NVIDIA T4 Virtual Workstation: nvidia-tesla-t4-vws
  - NVIDIA P4 Virtual Workstation: nvidia-tesla-p4-vws
  - NVIDIA P100 Virtual Workstation: nvidia-tesla-p100-vws
    
    For these virtual workstations, an NVIDIA RTX Virtual Workstation (vWS) license is automatically added to your VM.

Install drivers

To install the drivers, choose one of the following options:

If you plan to run graphics-intensive workloads, such as those for gaming and visualization, install drivers for the NVIDIA RTX Virtual Workstation.
For most workloads, install the GPU drivers.

What's next?

Learn more about GPU platforms.
Add Local SSDs to your instances. Local SSD devices pair well with GPUs when your apps require high-performance storage.
Install the GPU drivers. If you enabled an NVIDIA RTX Virtual Workstation, install a driver for the virtual workstation.
To handle GPU host maintenance, see Handling GPU host maintenance events.