Create an accelerator-optimized VM

This document explains how to create a VM that uses an accelerator-optimized machine family. The accelerator-optimized machine family is available in A3, A2, and G2 machine types.

Each accelerator-optimized machine type has a specific model of NVIDIA GPUs attached.

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
    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, then initialize it by running the following command: 

      gcloud init

    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.

Create a VM that has attached GPUs

You can create an A3, A2, or G2 accelerator-optimized VM by using the Google Cloud console, Google Cloud CLI, or REST.

To make some customizations to your G2 VMs, you might need to use the Google Cloud CLI or REST. See G2 limitations.

Console

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

    Go to Create an instance

  2. Specify a Name for your VM. See Resource naming convention.

  3. Select a region and zone where GPUs are available. See the list of available GPU regions and zones.

  4. In the Machine configuration section, select the GPUs machine family, and then do the following:

    1. In the GPU type list, select your GPU type.

      • For A3 accelerator-optimized VMs, select NVIDIA H100 80GB or NVIDIA H100 80GB MEGA.
      • For A2 accelerator-optimized VMs, select either NVIDIA A100 40GB or NVIDIA A100 80GB.
      • For G2 accelerator-optimized VMs, select NVIDIA L4.

    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).

  5. In the Boot disk section, click Change. This opens the Boot disk configuration page.

  6. 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.

  7. 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.

  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, make sure that you set the --maintenance-policy=TERMINATE flag.

The following optional flags are shown in the sample command:

  • The --provisioning-model=SPOT flag which configures your VMs as Spot VMs. 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. For Spot VMs, the automatic restart and host maintenance options flags are disabled.
  • The --accelerator flag to specify a virtual workstation. NVIDIA RTX Virtual Workstations (vWS) are supported for only G2 VMs.
  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=SPOT] \
      [--accelerator=type=nvidia-l4-vws,count=VWS_ACCELERATOR_COUNT]
Replace the following:
  • VM_NAME: the name for the new VM.
  • MACHINE_TYPE : the machine type that you selected. Choose from one of the following:
    • An A3 machine type.
    • An A2 machine type.
    • A G2 machine type. G2 machine types also support custom memory. Memory must be a multiple of 1024 MB and within the supported memory range. For example, to create a VM with 4 vCPUs and 19 GB of memory specify --machine-type=g2-custom-4-19456.
  • ZONE: the zone for the VM. This zone must support your selected GPU model.
  • DISK_SIZE: the size of your boot disk in GB. Specify a boot disk size of at least 40 GB.
  • 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.
  • VWS_ACCELERATOR_COUNT: the number of virtual GPUs that you need.

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 : the machine type that you selected. Choose from one of the following:
    • An A3 machine type.
    • An A2 machine type.
    • A G2 machine type. G2 machine types also support custom memory. Memory must be a multiple of 1024 MB and within the supported memory range. For example, to create a VM with 4 vCPUs and 19 GB of memory specify --machine-type=g2-custom-4-19456.
    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-8-optimized-gcp-v20220719"
    • Image family: "sourceImage": "projects/rocky-linux-cloud/global/images/family/rocky-linux-8-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 family 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:
  • If your workload is fault-tolerant and can withstand possible VM preemption, consider using Spot VMs to reduce the cost ofyour VMs and the attached GPUs. For more information, see GPUs on Spot VMs. To specify Spot VMs, add the "provisioningModel": "SPOT" option to your request. For Spot VMs, the automatic restart and host maintenance options flags are disabled. 
    "scheduling":
  {
    "provisioningModel": "SPOT"
  }
  • For G2 VMs, NVIDIA RTX Virtual Workstations (vWS) are supported. To specify a virtual workstation, add the `guestAccelerators` option to your request. Replace VWS_ACCELERATOR_COUNT with the number of virtual GPUs that you need. 
    "guestAccelerators":
  [
    {
      "acceleratorCount": VWS_ACCELERATOR_COUNT,
      "acceleratorType": "projects/PROJECT_ID/zones/ZONEacceleratorTypes/nvidia-l4-vws"
    }
  ]

Limitations

A3 VMs

The following limitations apply to VMs that use A3 Edge, A3 High, and A3 Mega machine types:

  • You don't receive sustained use discounts and flexible committed use discounts for VMs that use A3 machine types.
  • You can only use A3 machine types in certain regions and zones.
  • You can't use regional persistent disks on VMs that use A3 machine types.
  • The A3 machine series is only available on the Sapphire Rapids platform.
  • If your VM uses an A3 machine type, you can't change the machine type. If you need to change the machine type, you must create a new VM.
  • You can't change the machine type of a VM to an A3 machine type. If you need a VM that uses an A3 machine type, you must create a new VM.
  • A3 machine types don't support sole-tenancy.
  • You can't run Windows operating systems on A3 machine types.
  • You can reserve A3 machine types only through certain reservations.
  • For a3-highgpu-1g, a3-highgpu-2g, anda3-highgpu-4g machine types, the following limitations apply:
    • For these machine types, you must either use Spot VMs or a feature that uses the Dynamic Workload Scheduler (DWS) such as resize requests in a MIG. For detailed instructions on either of these options, review the following:
    • You can't use Hyperdisk Balanced with these machine types.
    • You can't create reservations.
    If you try to create a VM by using standard provisioning or try to create a reservation for these machine types, you'll receive an error message.

A2 Standard VMs

  • You don't receive sustained use discounts and flexible committed use discounts for VMs that use A2 Standard machine types.
  • You can only use A2 Standard machine types in certain regions and zones.
  • You can't use regional persistent disks on VMs that use A2 Standard machine types.
  • The A2 Standard machine type is only available on the Cascade Lake platform.
  • If your VM uses an A2 Standard machine type, you can only switch from one A2 Standard machine type to another A2 Standard machine type. You can't change to any other machine type. For more information, see Modify accelerator-optmized VMs.
  • You can't use the Windows operating system with A2 Standard machine types. When using Windows operating systems, choose a different A2 Standard machine type.
  • You can't do a quick format of the attached Local SSDs on Windows VMs that use A2 Standard machine types. To format these Local SSDs, you must do a full format by using the diskpart utility and specifying format fs=ntfs label=tmpfs.
  • A2 Standard machine types don't support sole-tenancy.

A2 Ultra VMs

  • You don't receive sustained use discounts and flexible committed use discounts for VMs that use A2 Ultra machine types.
  • You can only use A2 Ultra machine types in certain regions and zones.
  • You can't use regional persistent disks on VMs that use A2 Ultra machine types.
  • The A2 Ultra machine type is only available on the Cascade Lake platform.
  • If your VM uses an A2 Ultra machine type, you can't change the machine type. If you need to use a different A2 Ultra machine type, or any other machine type, you must create a new VM.
  • You can't change any other machine type to an A2 Ultra machine type. If you need a VM that uses an A2 Ultra machine type, you must create a new VM.
  • You can't do a quick format of the attached Local SSDs on Windows VMs that use A2 Ultra machine types. To format these Local SSDs, you must do a full format by using the diskpart utility and specifying format fs=ntfs label=tmpfs.

G2 VMs

  • You don't receive sustained use discounts and flexible committed use discounts for VMs that use G2 machine types.
  • You can only use G2 machine types in certain regions and zones.
  • You can't use regional persistent disks on VMs that use G2 machine types.
  • The G2 machine type is only available on the Cascade Lake platform.
  • Standard persistent disks (pd-standard) are not supported on VMs that use G2 standard machine types. For supported disk types, see Supported disk types for G2.
  • You can't create Multi-Instance GPUs on G2 machine types.
  • If you need to change the machine type of a G2 VM, review Modify accelerator-optmized VMs.
  • You can't use Deep Learning VM Images as boot disks for your VMs that use G2 machine types.
  • The current default driver for Container-Optimized OS doesn't support L4 GPUs running on G2 machine types. Container-Optimized OS also only support a select set of drivers. If you want to use Container-Optimized OS on G2 machine types, review the following notes:
    • Use a Container-Optimized OS version that supports the minimum recommended NVIDIA driver version 525.60.13 or later. For more information, review the Container-Optimized OS release notes.
    • When you install the driver, specify the latest available version that works for the L4 GPUs. For example, sudo cos-extensions install gpu -- -version=525.60.13.
  • You must use the Google Cloud CLI or REST to create G2 VMs for the following scenarios:
    • You want to specify custom memory values.
    • You want to customize the number of visible CPU cores.

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 VMs using the following images:

COS (A3 Edge/High)

You can create either a3-edgegpu-8g or a3-highgpu-8g VMs that have attached H100 GPUs by using Container-optimized (COS) images.

For detailed instructions on how to create these a3-edgegpu-8g or a3-highgpu-8gVMs that use Container-Optimized OS, see Create an A3 VM with GPUDirect-TCPX enabled.

Public OS image (G2)

You can create VMs that have attached GPUs that use either a public image that is available on Compute Engine or a custom image.

To create a VM using the most recent, non-deprecated image from the Rocky Linux 8 optimized for Google Cloud image family that uses the g2-standard-8 machine type and has an NVIDIA RTX Virtual Workstation, complete the following steps:

  1. Create the VM. In this example, optional flags such as boot disk type and size are also specified.

    gcloud compute instances create VM_NAME \
    --project=PROJECT_ID \
    --zone=ZONE \
    --machine-type=g2-standard-8  \
    --maintenance-policy=TERMINATE --restart-on-failure \
    --network-interface=nic-type=GVNIC \
    --accelerator=type=nvidia-l4-vws,count=1 \
    --image-family=rocky-linux-8-optimized-gcp \
    --image-project=rocky-linux-cloud \
    --boot-disk-size=200GB \
    --boot-disk-type=pd-ssd

    Replace the following:

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

  2. Install NVIDIA driver and CUDA. For NVIDIA L4 GPUs, CUDA version XX or higher is required.

DLVM image (A2)

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.

  1. Create a VM using the tf2-ent-2-3-cu110 image and the a2-highgpu-1g machine type. In this example, optional flags such as boot disk size and scope are specified.

    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
    • PROJECT_ID : your project ID.
    • ZONE: the zone for the VM

  2. 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 (A3 and A2 VMs only)

A Multi-Instance GPU partitions a single NVIDIA H100 or A100 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 H100 or A100 GPU to deliver guaranteed 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 and H100 80GB GPUs the allocated memory doubles to 10 GB each.

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 A3 or A2 accelerator-optimized VM.

  2. Enable NVIDIA GPU drivers.

  3. Enable Multi-Instance GPUs..

    sudo nvidia-smi -mig 1

  4. 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   |
+-----------------------------------------------------------------------------+
.......

  5. 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

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

    sudo nvidia-smi mig -lgi

  7. 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?