When you need a general-purpose VM with the option to add a GPU for moderate acceleration, you can create compute instances with attached GPUs on Google Cloud Compute Engine's [N1 general-purpose machines](https://docs.cloud.google.com/compute/docs/general-purpose-machines#n1_machines). Attaching earlier-generation GPUs (such as T4, V100, and P4) to N1 VMs provides cost-effective flexibility for smaller-scale or less performance-critical AI tasks, remote virtual workstations, and video transcoding. Most N1 machine types support attached GPUs, with the exception of N1 shared-core machines. If your primary workload is highly GPU-intensive and can benefit from the latest GPU architectures and platform optimizations, choose [accelerator-optimized A-series or G-series machines](https://docs.cloud.google.com/compute/docs/gpus#introduction). ## Before you begin - To review additional prerequisite steps such as selecting an OS image and checking GPU quota, review the [overview](https://docs.cloud.google.com/compute/docs/gpus/create-vm-with-gpus) document. - If you haven't already, set up [authentication](https://docs.cloud.google.com/compute/docs/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](https://docs.cloud.google.com/sdk/docs/install) the Google Cloud CLI. After installation, [initialize](https://docs.cloud.google.com/sdk/docs/initializing) the Google Cloud CLI by running the following command: ```bash gcloud init ``` If you're using an external identity provider (IdP), you must first [sign in to the gcloud CLI with your federated identity](https://docs.cloud.google.com/iam/docs/workforce-log-in-gcloud). > [!NOTE] > **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](https://docs.cloud.google.com/compute/docs/gcloud-compute#set_default_zone_and_region_in_your_local_client). ### 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. 1. [Install](https://docs.cloud.google.com/sdk/docs/install) the Google Cloud CLI. 2. If you're using an external identity provider (IdP), you must first [sign in to the gcloud CLI with your federated identity](https://docs.cloud.google.com/iam/docs/workforce-log-in-gcloud). For more information, see [Authenticate for using REST](https://docs.cloud.google.com/docs/authentication/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)](https://docs.cloud.google.com/iam/docs/roles-permissions/compute#compute.instanceAdmin.v1) (`roles/compute.instanceAdmin.v1`) IAM role on the project. For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access). 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](https://docs.cloud.google.com/vpc/docs/legacy) 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](https://docs.cloud.google.com/iam/docs/creating-custom-roles) or other [predefined roles](https://docs.cloud.google.com/iam/docs/roles-overview#predefined). ## Overview The following GPU models can be attached to VMs that use N1 machine families. > [!CAUTION] > **Caution:** NVIDIA T4 and P4 GPUs reach end of support on August 1, 2027. For more information, see [NVIDIA T4 end of support](https://docs.cloud.google.com/compute/docs/eol/t4-eos) and [NVIDIA P4 end of support](https://docs.cloud.google.com/compute/docs/eol/p4-eos). NVIDIA GPUs:
- NVIDIA T4: `nvidia-tesla-t4` - NVIDIA P4: `nvidia-tesla-p4` - 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` For these virtual workstations, an NVIDIA RTX Virtual Workstation (vWS) license is automatically added to your instance. ## 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 1. In the Google Cloud console, go to the **Create an instance** page. [Go to Create an instance](https://console.cloud.google.com/compute/instancesAdd) 2. Specify a **Name** for your VM. See [Resource naming convention](https://docs.cloud.google.com/compute/docs/naming-resources#resource-name-format). 3. Select a region and zone where GPUs are available. See the list of available [GPU zones](https://docs.cloud.google.com/compute/docs/gpus/gpu-regions-zones). 4. 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](https://docs.cloud.google.com/compute/docs/gpus#gpu-virtual-workstations), 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. 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](https://docs.cloud.google.com/compute/docs/images/os-details#gpu-support) or [Deep Learning VM Images](https://docs.cloud.google.com/ai-platform/deep-learning-vm/docs/images). 2. Specify a boot disk size of at least 40 GB. 3. To confirm your boot disk options, click **Select**. 7. Optional: In the **VM provisioning model** list, select a [provisioning model](https://docs.cloud.google.com/compute/docs/instances/provisioning-models). 8. To create and start the VM, click **Create**. ### gcloud To create and start a VM use the [`gcloud compute instances create`](https://docs.cloud.google.com/sdk/gcloud/reference/compute/instances/create) command with the following flags. If your workload is fault-tolerant or can start at any time, then consider using a different provisioning model to reduce your costs. To change your provisioning model, include the `--provisioning-model=PROVISIONING_MODEL` flag in the command. For more information about the available models, see [Compute Engine instances provisioning models](https://docs.cloud.google.com/compute/docs/instances/provisioning-models). ``` 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=PROVISIONING_MODEL] ``` Replace the following: - `VM_NAME`: the [name](https://docs.cloud.google.com/compute/docs/naming-resources#resource-name-format) for the new VM. - `MACHINE_TYPE`: the [machine type](https://docs.cloud.google.com/compute/docs/machine-types#gpu-support) that you selected for your VM. - `ZONE`: the [zone](https://docs.cloud.google.com/compute/docs/zones) for the VM. This zone must support the [GPU type](https://docs.cloud.google.com/compute/docs/gpus/gpu-regions-zones). - `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](https://docs.cloud.google.com/compute/docs/images/os-details#gpu-support). 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](https://docs.cloud.google.com/compute/docs/images/os-details#general-info). 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](https://docs.cloud.google.com/deep-learning-vm/docs/images). - `IMAGE_PROJECT`: the Compute Engine [image project](https://docs.cloud.google.com/compute/docs/images/os-details#general-info) that the image family 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 for the VM. Specify either `SPOT` or `FLEX_START`. If you don't specify a provisioning model, the standard model is used. This flag is optional. - `ACCELERATOR_COUNT`: the number of GPUs that you want to add to your VM. See [GPUs on Compute Engine](https://docs.cloud.google.com/compute/docs/gpus#introduction) for a list of GPU limits based on the machine type of your VM. - `ACCELERATOR_TYPE`: the [GPU model](https://docs.cloud.google.com/compute/docs/gpus#introduction) that you want to use. If you plan on running graphics-intensive workloads on this VM, use one of the [virtual workstation models](https://docs.cloud.google.com/compute/docs/gpus#gpu-virtual-workstations). Choose one of the following values: - NVIDIA GPUs: - NVIDIA T4: `nvidia-tesla-t4` - NVIDIA P4: `nvidia-tesla-p4` - 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` For these virtual workstations, an NVIDIA RTX Virtual Workstation (vWS) license is automatically added to your instance. **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. To create VMs at a discounted price, you can specify a different provisioning model by adding the `"provisioningModel": "PROVISIONING_MODEL"` field to the `scheduling` object in your request. For more information about the available models, see [Compute Engine instances provisioning models](https://docs.cloud.google.com/compute/docs/instances/provisioning-models). ``` GET https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/zones/ZONE/acceleratorTypes ``` Replace the following: - `PROJECT_ID`: project ID. - `ZONE`: [zone](https://docs.cloud.google.com/compute/docs/zones) from which you want to list the available GPU types. Send a POST request to the [`instances.insert` method](https://docs.cloud.google.com/compute/docs/reference/rest/v1/instances/insert). 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": "PROVISIONING_MODEL" }, } ``` Replace the following: - `VM_NAME`: the [name](https://docs.cloud.google.com/compute/docs/naming-resources#resource-name-format) of the VM. - `PROJECT_ID`: your project ID. - `ZONE`: the [zone](https://docs.cloud.google.com/compute/docs/zones) for the VM. This zone must support the [GPU type](https://docs.cloud.google.com/compute/docs/gpus/gpu-regions-zones). - `MACHINE_TYPE`: the [machine type](https://docs.cloud.google.com/compute/docs/machine-types) that you selected for the VM. See [GPUs on Compute Engine](https://docs.cloud.google.com/compute/docs/gpus#introduction) to see what machine types are available based on your chosen 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](https://docs.cloud.google.com/compute/docs/images/os-details#general-info). 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](https://docs.cloud.google.com/deep-learning-vm/docs/images). - `IMAGE_PROJECT`: the Compute Engine [image project](https://docs.cloud.google.com/compute/docs/images/os-details#general-info) 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](https://docs.cloud.google.com/compute/docs/gpus#introduction) for a list of GPU limits based on the machine type of your VM. - `ACCELERATOR_TYPE`: the [GPU model](https://docs.cloud.google.com/compute/docs/gpus#introduction) that you want to use. If you plan on running graphics-intensive workloads on this VM, use one of the [virtual workstation models](https://docs.cloud.google.com/compute/docs/gpus#gpu-virtual-workstations). Choose one of the following values: - NVIDIA GPUs: - NVIDIA T4: `nvidia-tesla-t4` - NVIDIA P4: `nvidia-tesla-p4` - 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` For these virtual workstations, an NVIDIA RTX Virtual Workstation (vWS) license is automatically added to your instance. - `PROVISIONING_MODEL`: the provisioning model for the VM. Specify either `SPOT` or `FLEX_START`. If you don't specify a provisioning model, the standard model is used. This property is optional. For more information on provisioning models, see [Compute Engine instances provisioning models](https://docs.cloud.google.com/compute/docs/instances/provisioning-models). ## 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](https://docs.cloud.google.com/compute/docs/gpus/install-grid-drivers). - For most workloads, [install the GPU drivers](https://docs.cloud.google.com/compute/docs/gpus/install-drivers-gpu). ## What's next? - Learn more about [GPU platforms](https://docs.cloud.google.com/compute/docs/gpus). - [Add Local SSDs to your instances](https://docs.cloud.google.com/compute/docs/disks/local-ssd). Local SSD devices pair well with GPUs when your apps require high-performance storage. - [Install the GPU drivers](https://docs.cloud.google.com/compute/docs/gpus/install-drivers-gpu). If you enabled an NVIDIA RTX Virtual Workstation, [install a driver for the virtual workstation](https://docs.cloud.google.com/compute/docs/gpus/install-grid-drivers). - To handle GPU host maintenance, see [Handling GPU host maintenance events](https://docs.cloud.google.com/compute/docs/gpus/gpu-host-maintenance).