Available GPUs

GPUs are used to accelerate workloads, and Cloud Workstations supports attaching GPUs to workstations. Cloud Workstations supports many of the various GPU models that are available to attach to Compute Engine VMs. The model and quantity of GPUs to attach to each workstation are specified on the workstation configuration. Cloud Workstations handles attaching the GPUs and installing their device drivers.

Attaching GPUs to workstations will affect costs as described in the Cloud Workstations pricing overview.

Limitations

A workstation configuration may specify GPUs, subject to the following limitations:

• Cloud Workstations only supports GPUs for configurations that specify machine types in the N1 machine series or the A2 machine series. Refer to supported GPU models for details.
• The configuration must specify replica zones where the chosen GPU model is available.
• It is not possible to configure workstations to have more than one model of GPU attached at a time.

Supported GPU models

Cloud Workstations supports many of the GPU models that Compute Engine makes available. The models that are supported depend on the machine series chosen for the workstation configuration, as summarized in the following table.

N1 machine series

The N1 general-purpose machine series supports several GPU models, and workstation configurations that specify any of the N1 machine types can also specify one of the following GPU models. For the chosen GPU model, the configuration may specify a count of how many GPU cards to attach to each workstation.

GPU model	GPU counts
NVIDIA T4 (nvidia-tesla-t4)	1, 2, or 4 GPUs
NVIDIA P4 (nvidia-tesla-p4)	1, 2, or 4 GPUs
NVIDIA V100 (nvidia-tesla-v100)	1, 2, 4, or 8 GPUs
NVIDIA P100 (nvidia-tesla-p100)	1, 2, or 4 GPUs

A2 machine series

The A2 standard accelerator-optimized machine series has a fixed number of NVIDIA A100 GPUs attached, based solely on the chosen machine type.

The following table shows the mapping from machine type to the number of cards that will be attached.

GPU model	Machine type	GPU count
NVIDIA A100 40GB (nvidia-tesla-a100)	a2-highgpu-1g	1 GPU
	a2-highgpu-2g	2 GPUs
	a2-highgpu-4g	4 GPUs
	a2-highgpu-8g	8 GPUs
	a2-megagpu-16g	16 GPUs

Cloud Workstations does not support A2 ultra machine types.

Add GPUs to an Existing Workstation Configuration

To add GPUs to a workstation configuration, complete the steps in one of the following tabs.

Before you begin

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

Install the Google Cloud CLI, then initialize it by running the following command:

gcloud init

Review the Cloud Workstations pricing overview to understand how your costs will be affected by configuring GPUs. Note that GPUs are attached to the pre-started virtual machines (VMs) specified by a configuration's Quick start pool size.

Updating your existing configuration

Console

Configure GPUs on an existing workstation configuration from the Google Cloud console, by doing the following:

1. In the Google Cloud console, go to the Workstation configurations page.

   Go to Workstation configurations

2. In the Workstation configurations list, click the Name of the configuration to add GPUs to.

3. On the Workstation configuration details page, click edit Edit.

4. On the Edit workstation configuration page, click Machine settings in the navigation menu.

5. Click the toggle to select GPUs instead of the General purpose machine family.

   In the GPU type field, select the GPU Model that you want to use.

   In the Number of GPUs field, select the number of GPU cards you want to attach to each workstation.

   In the Machine type field, select the machine type you want to use.

   Note: the options for each field vary depending on the replica zones chosen for the configuration and the GPU and machine type availability in those zones. If you cannot find suitable GPUs in the replica zones for this configuration, you may decide to create a new configuration with GPUs instead.

6. Click Save to update the configuration.

gcloud

Configure GPUs on an existing workstation configuration by running the gcloud workstations configs update command.

First though, collect some information to see which GPU models are available and to choose one for your configuration:

1. Check which replica zones the configuration specifies by running the following gcloud CLI command:

   gcloud workstations configs describe \
     --format="table(name.scope(workstationConfigs),replicaZones.list())" \
     --project=PROJECT_ID \
     --region=LOCATION \
     --cluster=WORKSTATION_CLUSTER_ID \
     WORKSTATION_CONFIG_ID
   

   Replace the following:

   • PROJECT_ID: the ID of the project that contains the workstation configuration.
   • LOCATION: the location of the workstation cluster.
   • WORKSTATION_CLUSTER_ID: the name workstation cluster that contains the workstation configuration.
   • WORKSTATION_CONFIG_ID: the name of the workstation configuration.

2. Choose a supported GPU model that is available in both of the configuration's replica zones by running the gcloud compute accelerator-types list command:

   gcloud compute accelerator-types list \
     --format="table(name:sort=1,zone,description,maximumCardsPerInstance)" \
     --filter='zone.basename()=(ZONES) AND name~"nvidia-tesla-(a100|p100|p4|t4|v100)$"' \
     --project=PROJECT_ID
   

   Replace ZONES with a comma-separated list of the replica zones determined in the previous step (for example, us-central1-a,us-central1-c).

   Choose a GPU model that is listed twice in the table, indicating it is available in both replica zones.

   Take note of the maximum cards you can attach for the chosen GPU model.

   Note: If there isn't suitable GPU Availability in the replica zones for this configuration, you may decide to create a new configuration with GPUs instead.

3. Determine which of the supported machine types are available in both of the configuration's replica zones using the gcloud compute machine-types list command.

   1. If you chose the NVIDIA A100 40GB GPU model in the previous step, your configuration must use the A2 machine series:

      gcloud compute machine-types list \
        --format="table(name,zone,guestCpus:sort=1)" \
        --filter="name:a2-highgpu- OR name:a2-megagpu-" \
        --zones=ZONES \
        --project=PROJECT_ID
      

   2. If you chose any other GPU model in the previous step, your configuration must use the N1 machine series:

      gcloud compute machine-types list \
        --format="table(name,zone,guestCpus:sort=1)" \
        --filter="name:n1-standard-" \
        --zones=ZONES \
        --project=PROJECT_ID
      

   Choose a machine type that is listed twice in the table, indicating it is available in both replica zones.

4. Now that you have selected a GPU model and compatible machine type, update the configuration:

   1. For NVIDIA A100 40GB GPUs, run this command to update your configuration:

      gcloud beta workstations configs update \
        --project=PROJECT_ID \
        --region=LOCATION \
        --cluster=WORKSTATION_CLUSTER_ID \
        WORKSTATION_CONFIG_ID \
        --machine-type=A2_MACHINE_TYPE
      

      Replace A2_MACHINE_TYPE with the chosen A2 machine type determined in the previous step (for example, a2-highgpu-1g).

      Note: for the A2 machine series the model and quantity of GPUs is fixed based on the machine type, so you may omit the --accelerator-type and --accelerator-count flags.

   2. For all other GPU models, run this command:

      gcloud beta workstations configs update \
        --project=PROJECT_ID \
        --region=LOCATION \
        --cluster=WORKSTATION_CLUSTER_ID \
        WORKSTATION_CONFIG_ID \
        --machine-type=N1_MACHINE_TYPE \
        --accelerator-type=ACCELERATOR_TYPE \
        --accelerator-count=ACCELERATOR_COUNT
      

      Replace the following:

      • N1_MACHINE_TYPE: the chosen machine type from the N1 series (for example, n1-standard-2).
      • ACCELERATOR_TYPE: the chosen GPU model's name (for example, nvidia-tesla-t4).
      • ACCELERATOR_COUNT: the number of GPUs to attach to each workstation (for example, 1, 2, 4). Must be a power of two less than the maximum for the GPU model.

Create a new Workstation Configuration with GPUs

To create a new workstation configuration that attaches GPUs to workstations that are based on it, complete the steps in one of the following tabs.

Before you begin

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

Install the Google Cloud CLI, then initialize it by running the following command:

gcloud init

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.

Choose a GPU model and consult the GPU availability table to select a region where the chosen GPU model is available in at least two zones.

If you don't already have a cluster in the chosen region where you can create a new configuration, follow the steps to Create a workstation cluster in the region.

Review the Cloud Workstations pricing overview to understand how your costs will be affected by configuring GPUs. Note that GPUs are attached to the pre-started virtual machines (VMs) specified by a configuration's Quick start pool size.

Creating a new configuration

Console

Create a new workstation configuration with GPUs from the Google Cloud console, by doing the following:

1. In the Google Cloud console, go to the Workstation configurations page.

   Go to Workstation configurations

2. On the Workstation configurations page, click add_box Create.

3. On the Basic information step of the Create workstation configuration page, specify a name for your configuration in the Name field.

   In the Workstation cluster field, select a cluster in the chosen region.

   Tip: GPUs are also attached to Quick start workstations. You can keep costs down by selecting Disabled.

   Click Continue to advance to the Machine settings step.

4. On the Machine settings step of the Create workstation configuration page, begin by clicking the toggle to select GPUs instead of the General purpose machine family.

   1. Then, in the Zones field, select the checkboxes next to two zones where the GPU model you chose is available (see the GPU availability table.

   2. In the GPU type field, select the GPU Model that you want to use.

   3. In the Number of GPUs field, select the number of GPU cards you want to attach to each workstation.

   4. In the Machine type field, select a compatible machine type.

5. Click Continue to configure the Environment settings and IAM policy before clicking Create to provision the new workstation configuration.

gcloud

Create a new workstation configuration with GPUs using the gcloud CLI, by running the gcloud workstations configs create command.

• For NVIDIA A100 40GB GPUs run this command to create your configuration:

  gcloud beta workstations configs create \
    --project=PROJECT_ID \
    --region=LOCATION \
    --cluster=WORKSTATION_CLUSTER_ID \
    WORKSTATION_CONFIG_ID \
    --replica-zones=REPLICA_ZONES \
    --machine-type=A2_MACHINE_TYPE
  

  Replace the following:

  • PROJECT_ID: the ID of the project that will contain the new workstation configuration.
  • LOCATION: the location of the workstation cluster where the configuration will be contained.
  • WORKSTATION_CLUSTER_ID: the name workstation cluster that will contain the new workstation configuration.
  • WORKSTATION_CONFIG_ID: the name of the new workstation configuration.
  • REPLICA_ZONES: exactly two zones in the cluster's region where the chosen GPU model is available (for example, us-central1-a,us-central1-c).
  • A2_MACHINE_TYPE: the chosen A2 series machine type (for example, a2-highgpu-1g).
  Note: for the A2 machine series the model and quantity of GPUs is fixed based on the machine type, so you may omit the --accelerator-type and --accelerator-count flags.

• For all other GPU models, run this command to create your configuration:

  gcloud beta workstations configs create \
    --project=PROJECT_ID \
    --region=LOCATION \
    --cluster=WORKSTATION_CLUSTER_ID \
    WORKSTATION_CONFIG_ID \
    --replica-zones=REPLICA_ZONES \
    --machine-type=N1_MACHINE_TYPE \
    --accelerator-type=ACCELERATOR_TYPE \
    --accelerator-count=ACCELERATOR_COUNT
  

  Replace the following:

  • PROJECT_ID: the ID of the project that will contain the new workstation configuration.
  • LOCATION: the location of the workstation cluster where the configuration will be contained.
  • WORKSTATION_CLUSTER_ID: the name workstation cluster that will contain the new workstation configuration.
  • WORKSTATION_CONFIG_ID: the name of the new workstation configuration.
  • REPLICA_ZONES: exactly two zones in the cluster's region where the chosen GPU model is available (for example, us-central1-a,us-central1-c).
  • N1_MACHINE_TYPE: the chosen N1 series machine type (for example, n1-standard-2).
  • ACCELERATOR_TYPE: the chosen GPU model's name (for example, nvidia-tesla-t4).
  • ACCELERATOR_COUNT: the number of GPUs to attach to each workstation (for example, 1, 2, 4).

NVIDIA GPU device drivers

Cloud Workstations installs the NVIDIA device drivers on workstations' host VMs during VM startup.

To determine which version device driver has been installed on a workstation, run the following command:

nvidia-smi --query-gpu=name,driver_version --format=csv

GPU availability by region and zone

You can search either by location or GPU model, or a combination of both.

APAC North America South America Europe Middle East A100 40GB T4 V100 P100 P4

Clear all

Zones	Location	GPU platforms
asia-east1-a	Changhua County, Taiwan, APAC	T4, P100
asia-east1-b	Changhua County, Taiwan, APAC
asia-east1-c	Changhua County, Taiwan, APAC	T4, V100, P100
asia-east2-a	Hong Kong, APAC	T4
asia-east2-b	Hong Kong, APAC
asia-east2-c	Hong Kong, APAC	T4
asia-northeast1-a	Tokyo, Japan, APAC	A100 40GB, T4
asia-northeast1-b	Tokyo, Japan, APAC
asia-northeast1-c	Tokyo, Japan, APAC	A100 40GB, T4
asia-northeast3-a	Seoul, South Korea, APAC	A100 40GB
asia-northeast3-b	Seoul, South Korea, APAC	A100 40GB, T4
asia-northeast3-c	Seoul, South Korea, APAC	T4
asia-south1-a	Mumbai, India, APAC	T4
asia-south1-b	Mumbai, India, APAC	T4
asia-south1-c	Mumbai, India, APAC	T4
asia-southeast1-a	Jurong West, Singapore, APAC	T4
asia-southeast1-b	Jurong West, Singapore, APAC	A100 40GB, T4, P4
asia-southeast1-c	Jurong West, Singapore, APAC	A100 40GB, T4, P4
australia-southeast1-a	Sydney, Australia, APAC	T4, P4
australia-southeast1-b	Sydney, Australia, APAC	P4
australia-southeast1-c	Sydney, Australia, APAC	T4, P100
europe-north1-a europe-north1-b europe-north1-c	Hamina, Finland, Europe
europe-west1-b	St. Ghislain, Belgium, Europe	T4, P100
europe-west1-c	St. Ghislain, Belgium, Europe	T4
europe-west1-d	St. Ghislain, Belgium, Europe	P100, T4
europe-west2-a europe-west2-b	London, England, Europe	T4
europe-west2-c	London, England, Europe
europe-west3-a	Frankfurt, Germany, Europe
europe-west3-b	Frankfurt, Germany, Europe	T4
europe-west3-c	Frankfurt, Germany, Europe
europe-west4-a	Eemshaven, Netherlands, Europe	A100 40GB, T4, V100, P100
europe-west4-b	Eemshaven, Netherlands, Europe	A100 40GB, T4, P4, V100
europe-west4-c	Eemshaven, Netherlands, Europe	T4, P4, V100
europe-west6-a europe-west6-b europe-west6-c	Zurich, Switzerland, Europe
europe-west8-a europe-west8-b europe-west8-c	Milan, Italy, Europe
europe-west9-a europe-west9-b europe-west9-c	Paris, France, Europe
europe-west12-a europe-west12-b europe-west12-c	Turin, Italy, Europe
europe-southwest1-a europe-southwest1-b europe-southwest1-c	Madrid, Spain, Europe
me-west1-a	Tel Aviv, Israel, Middle East
me-west1-b	Tel Aviv, Israel, Middle East	A100 40GB, T4
me-west1-c	Tel Aviv, Israel, Middle East	A100 40GB, T4
northamerica-northeast1-a	Montréal, Québec, North America	P4
northamerica-northeast1-b	Montréal, Québec, North America	P4
northamerica-northeast1-c	Montréal, Québec, North America	T4, P4
southamerica-east1-a	Osasco, São Paulo, Brazil, South America	T4
southamerica-east1-c	Osasco, São Paulo, Brazil, South America	T4
southamerica-west1-a southamerica-west1-b southamerica-west1-c	Santiago, Chile, South America
us-central1-a	Council Bluffs, Iowa, North America	A100 40GB, T4, P4, V100
us-central1-b	Council Bluffs, Iowa, North America	A100 40GB, T4, V100
us-central1-c	Council Bluffs, Iowa, North America	A100 40GB, T4, P4, V100, P100
us-central1-f	Council Bluffs, Iowa, North America	A100 40GB, T4, V100, P100
us-east1-b	Moncks Corner, South Carolina, North America	A100 40GB, P100
us-east1-c	Moncks Corner, South Carolina, North America	T4, V100, P100
us-east1-d	Moncks Corner, South Carolina, North America	T4
us-east4-a us-east4-b us-east4-c	Ashburn, Virginia, North America	T4, P4
us-east5-a us-east5-b us-east5-c	Columbus, Ohio, North America
us-west1-a	The Dalles, Oregon, North America	T4, V100, P100
us-west1-b	The Dalles, Oregon, North America	A100 40GB, T4, V100, P100
us-west1-c	The Dalles, Oregon, North America
us-west4-a	Las Vegas, Nevada, North America	T4
us-west4-b	Las Vegas, Nevada, North America	A100 40GB, T4
us-west4-c	Las Vegas, Nevada, North America

What's next

• Specify GPUs using the Cloud Workstations API.

• Learn about Running Compute Engine instances with GPU accelerators.

• See the complete list of Compute Engine machine types.