Create an A3 High or A2 instance

This document explains how to create a virtual machine (VM) instance that uses a machine type from the A2 or A3 High accelerator-optimized machine series.

For A3 High machine types, this document only covers machine types that have fewer than 8 GPUs attached. These A3 High machine types with fewer than 8 GPUs can only be created as Spot VMs or Flex-start VMs. To create an A3 instance that has 8 GPUs attached, see Create an A3 Mega, A3 High, or A3 Edge instance with GPUDirect enabled.

To create multiple A3 or A2 VMs, you can also use one of the following options:

Before you begin

  • To review limitations and additional prerequisite steps for creating instances with attached GPUs, such as selecting an OS image and checking GPU quota, see Overview of creating an instance with attached GPUs.
  • If you haven't already, set up authentication. Authentication verifies your identity 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. After installation, initialize the Google Cloud CLI by running the following command: 

      gcloud init

      If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

    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.

      Install the Google Cloud CLI. After installation, initialize the Google Cloud CLI by running the following command: 

      gcloud init

      If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

    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 specify a subnet for your VM: compute.subnetworks.use on the project or on the chosen subnet
  • 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 VPC network: compute.subnetworks.useExternalIp on the project or on the chosen subnet
  • To assign a legacy network to the VM: compute.networks.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 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.

Create a VM that has attached GPUs

You can create an A2 or A3 accelerator-optimized VM by using the Google Cloud console, Google Cloud CLI, or REST.

Console

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

    Go to Create an instance
  2. In the Name field, enter a unique name for your instance. See Resource naming convention.
  3. Select a region and zone where these GPU machine types are available. See GPU regions and zones.
  4. In the machine types section, select GPUs.
    1. In the GPU type list, select the GPU type.
      • For A2 accelerator-optimized VMs, select either NVIDIA A100 40GB or NVIDIA A100 80GB
      • For A3 accelerator-optimized VMs, select NVIDIA H100 80GB
    2. In the Number of GPUs list, select the number of GPUs.
  5. Configure the boot disk as follows:
    1. In the OS and storage section, click Change. This opens the Boot disk configuration page.
    2. 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.
      1. Specify a boot disk size of at least 40 GiB.
      2. To confirm your boot disk options, click Select.
  6. Configure the provisioning model. In the Advanced options section, under VM provisioning model, select one of the following:
    • Standard: for general-purpose workloads.
    • Flex-start: for short-duration workloads that can tolerate a flexible start time. For more information, see About Flex-start VMs.
    • Spot: for fault-tolerant workloads that can be preempted. For more information, see Spot VMs.
  7. Optional: In the On VM termination list, select what happens when Compute Engine preempts the Spot VMs or the Flex-start VMs reaches the end of its run duration:
    • To stop the VM during preemption, select Stop (default).
    • To delete the VM during preemption, select Delete.
  8. 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. VMs with GPUs can't live migrate, so make sure that you set the --maintenance-policy=TERMINATE flag.

The sample command also shows the --provisioning-model flag. This flag sets the provisioning model for the VM. This flag is required when creating A3 machine types with fewer than 8 GPUs and must be set to either SPOT or FLEX_START. For A2 machine types, this flag is optional. If you don't specify a model, then the standard provisioning model is used. For more information, see Compute Engine instances provisioning models.

  gcloud compute instances create VM_NAME \
      --machine-type=MACHINE_TYPE \
      --zone=ZONE \
      --boot-disk-size=DISK_SIZE \
      --image=IMAGE \
      --image-project=IMAGE_PROJECT \
      --maintenance-policy=TERMINATE \
      --provisioning-model=PROVISIONING_MODEL
Replace the following:
  • VM_NAME: the name for the new VM.
  • MACHINE_TYPE: an A2 machine type or an A3 machine type with 1, 2, or 4 GPUs. For A3 machine types, you must specify a provisioning model.
  • ZONE: the zone for the VM. This zone must support your selected GPU model.
  • DISK_SIZE: the size of your boot disk in GiB. Specify a boot disk size of at least 40 GiB.
  • IMAGE: an operating system image that supports GPUs. If you want to use the latest image in an image family, replace the --image flag with the --image-family flag and set its value to an image family that supports GPUs. For example: --image-family=rocky-linux-8-optimized-gcp.
    You can also specify a custom image or Deep Learning VM Images.
  • IMAGE_PROJECT: the Compute Engine image project that the OS image belongs to. If using a custom image or Deep Learning VM Images, specify the project that those images belong to.
  • PROVISIONING_MODEL: the provisioning model to use to create the VM. You can specify either SPOT or FLEX_START. If you remove the --provisioning-model flag from the command, then the standard provisioning model is used. This flag is required when creating A3 VMs with fewer than 8 GPUs.

REST

Send a POST request to the instances.insert method. VMs with GPUs can't live migrate, make sure you 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": "SOURCE_IMAGE_URI"
    },
    "boot": true
  }
],
"name": "VM_NAME",
"networkInterfaces":
[
  {
    "network": "projects/PROJECT_ID/global/networks/NETWORK"
  }
],
"scheduling":
{
  "onHostMaintenance": "terminate",
  "automaticRestart": true
}
}
Replace the following:
  • VM_NAME: the name for the new VM.
  • PROJECT_ID: your Project ID.
  • ZONE: the zone for the VM. This zone must support your selected GPU model.
  • MACHINE_TYPE: an A2 machine type or an A3 machine type with 1, 2, or 4 GPUs. For A3 machine types, you must specify a provisioning model.
  • PROVISIONING_MODEL: The provisioning model for the VM. Specify either SPOT or FLEX_START. This field is required when creating A3 VMs with fewer than 8 GPUs. For A2 VMs, this field is optional; if you don't specify a model, then the standard provisioning model is used. For more information, see Compute Engine instances provisioning models.
  • SOURCE_IMAGE_URI: the URI for the specific image or image family that you want to use. For example:
    • Specific image: "sourceImage": "projects/rocky-linux-cloud/global/images/rocky-linux-10-optimized-gcp-v20251017"
    • Image family: "sourceImage": "projects/rocky-linux-cloud/global/images/family/rocky-linux-10-optimized-gcp"
    When you specify an image family, Compute Engine creates a VM from the most recent, non-deprecated OS image in that family. For more information about when to use image families, see Image families best practices.
  • DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.
  • NETWORK: the VPC network that you want to use for the VM. You can specify `default` to use your default network.
Additional settings:
  • To specify a provisioning model, add the "provisioningModel": "PROVISIONING_MODEL" field to the scheduling object in your request. This is required for A3 machine types with fewer than 8 GPUs. If you specify to create Spot VMs, then the onHostMaintenance and automaticRestart fields are ignored. 
    "scheduling":
  {
    "onHostMaintenance": "terminate",
    "automaticRestart": true,
    "provisioningModel": "PROVISIONING_MODEL"
  }

Install drivers

For the VM to use the GPU, you need to Install the GPU driver on your VM.

Examples

In these examples, most of the VMs are created by using the Google Cloud CLI. However, you can also use either the Google Cloud console or REST to create these VMs.

The following examples show how to create an A3 Spot VM by using a standard OS image, and an A2 VM by using a Deep Learning VM Images image.

Create an A3 Spot VM by using the Debian 13 OS image family

This example creates an A3 (a3-highgpu-1g) Spot VM by using the Debian 13 OS image family.

gcloud compute instances create VM_NAME \
    --project=PROJECT_ID \
    --zone=ZONE \
    --machine-type=a3-highgpu-1g \
    --provisioning-model=SPOT \
    --maintenance-policy=TERMINATE \
    --image-family=debian-13 \
    --image-project=debian-cloud \
    --boot-disk-size=200GB \
    --scopes=https://www.googleapis.com/auth/cloud-platform

Replace the following:

  • VM_NAME: the name of your VM instance
  • PROJECT_ID : your project ID
  • ZONE: the zone for the VM instance

Create an A2 VM with a Vertex AI Workbench user-managed notebooks instance on the VM

This example creates an A2 Standard (a2-highgpu-1g) VM by using the tf2-ent-2-3-cu110 Deep Learning VM Images image. In this example, optional flags such as boot disk size and scope are specified.

Using DLVM images is the easiest way to get started because these images already have the NVIDIA drivers and CUDA libraries pre-installed.

These images also provide performance optimizations.

The following DLVM images are supported for NVIDIA A100:

  • common-cu110: NVIDIA driver and CUDA pre-installed
  • tf-ent-1-15-cu110: NVIDIA driver, CUDA, TensorFlow Enterprise 1.15.3 pre-installed
  • tf2-ent-2-1-cu110: NVIDIA driver, CUDA, TensorFlow Enterprise 2.1.1 pre-installed
  • tf2-ent-2-3-cu110: NVIDIA driver, CUDA, TensorFlow Enterprise 2.3.1 pre-installed
  • pytorch-1-6-cu110: NVIDIA driver, CUDA, Pytorch 1.6

For more information about the DLVM images that are available, and the packages installed on the images, see the Deep Learning VM documentation.

gcloud compute instances create VM_NAME \
    --project=PROJECT_ID \
    --zone=ZONE \
    --machine-type=a2-highgpu-1g \
    --maintenance-policy=TERMINATE \
    --image-family=tf2-ent-2-3-cu110 \
    --image-project=deeplearning-platform-release \
    --boot-disk-size=200GB \
    --metadata="install-nvidia-driver=True,proxy-mode=project_editors" \
    --scopes=https://www.googleapis.com/auth/cloud-platform

Replace the following:

  • VM_NAME: the name of your VM instance
  • PROJECT_ID : your project ID
  • ZONE: the zone for the VM instance

The preceding example command also generates a Vertex AI Workbench user-managed notebooks instance for the VM. To access the notebook, in the Google Cloud console, go to the Vertex AI Workbench > User-managed notebooks page.

Go to the User-managed notebooks page

Multi-Instance GPU

A Multi-Instance GPU partitions a single NVIDIA A100 or NVIDIA H100 GPU within the same VM into as many as seven independent GPU instances. They run simultaneously, each with its own memory, cache and streaming multiprocessors. This setup enables the NVIDIA A100 and H100 GPU to deliver consistent quality-of-service (QoS) at up to 7x higher utilization compared to earlier GPU models.

You can create up to seven Multi-instance GPUs. For A100 40GB GPUs, each Multi-instance GPU is allocated 5 GB of memory. With the A100 80GB GPUs the allocated memory doubles to 10 GB each. With H100 80GB GPUs, each Multi-instance GPU is also allocated 10 GB of memory.

For more information about using Multi-Instance GPUs, see NVIDIA Multi-Instance GPU User Guide.

To create Multi-Instance GPUs, complete the following steps:

  1. Create an A2 (A100) or A3 (H100) accelerator-optimized VM instance.

  2. Connect to the VM instance. For more information, see Connect to Linux VMs or Connect to Windows VMs.

  3. Enable NVIDIA GPU drivers.

  4. Enable Multi-Instance GPUs.

    sudo nvidia-smi -mig 1

  5. Review the Multi-Instance GPU shapes that are available.

    sudo nvidia-smi mig --list-gpu-instance-profiles

    The output is similar to the following:

    +-----------------------------------------------------------------------------+
| GPU instance profiles:                                                      |
| GPU   Name             ID    Instances   Memory     P2P    SM    DEC   ENC  |
|                              Free/Total   GiB              CE    JPEG  OFA  |
|=============================================================================|
|   0  MIG 1g.10gb       19     7/7        9.62       No     16     1     0   |
|                                                             1     1     0   |
+-----------------------------------------------------------------------------+
|   0  MIG 1g.10gb+me    20     1/1        9.62       No     16     1     0   |
|                                                             1     1     1   |
+-----------------------------------------------------------------------------+
|   0  MIG 1g.20gb       15     4/4        19.50      No     26     1     0   |
|                                                             1     1     0   |
+-----------------------------------------------------------------------------+
|   0  MIG 2g.20gb       14     3/3        19.50      No     32     2     0   |
|                                                             2     2     0   |
+-----------------------------------------------------------------------------+
|   0  MIG 3g.40gb        9     2/2        39.25      No     60     3     0   |
|                                                             3     3     0   |
+-----------------------------------------------------------------------------+
.......

  6. Create the Multi-Instance GPU (GI) and associated compute instances (CI) that you want. You can create these instances by specifying either the full or shortened profile name, profile ID, or a combination of both. For more information, see Creating GPU Instances.

    The following example creates two MIG 3g.20gb GPU instances by using the profile ID (9).

    The -C flag is also specified which creates the associated compute instances for the required profile.

    sudo nvidia-smi mig -cgi 9,9 -C

  7. Check that the two Multi-Instance GPUs are created:

    sudo nvidia-smi mig -lgi

  8. Check that both the GIs and corresponding CIs are created.

    sudo nvidia-smi

    The output is similar to the following:

    +-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.125.06   Driver Version: 525.125.06   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| 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 H100 80G...  Off  | 00000000:04:00.0 Off |                   On |
| N/A   33C    P0    70W / 700W |     39MiB / 81559MiB |     N/A      Default |
|                               |                      |              Enabled |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA H100 80G...  Off  | 00000000:05:00.0 Off |                   On |
| N/A   32C    P0    69W / 700W |     39MiB / 81559MiB |     N/A      Default |
|                               |                      |              Enabled |
+-------------------------------+----------------------+----------------------+
......

+-----------------------------------------------------------------------------+
| MIG devices:                                                                |
+------------------+----------------------+-----------+-----------------------+
| GPU  GI  CI  MIG |         Memory-Usage |        Vol|         Shared        |
|      ID  ID  Dev |           BAR1-Usage | SM     Unc| CE  ENC  DEC  OFA  JPG|
|                  |                      |        ECC|                       |
|==================+======================+===========+=======================|
|  0    1   0   0  |     19MiB / 40192MiB | 60      0 |  3   0    3    0    3 |
|                  |      0MiB / 65535MiB |           |                       |
+------------------+----------------------+-----------+-----------------------+
|  0    2   0   1  |     19MiB / 40192MiB | 60      0 |  3   0    3    0    3 |
|                  |      0MiB / 65535MiB |           |                       |
+------------------+----------------------+-----------+-----------------------+
......

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

What's next?