Deploy and inference Gemma using Model Garden and Vertex AI GPU-backed endpoints

In this tutorial, you use Model Garden to deploy the Gemma 1B open model to a GPU-backed Vertex AI endpoint. You must deploy a model to an endpoint before that model can be used to serve online predictions. Deploying a model associates physical resources with the model so it can serve online predictions with low latency.

After you deploy the Gemma 1B model, you inference the trained model by using the PredictionServiceClient to get online predictions. Online predictions are synchronous requests made to a model that is deployed to an endpoint.

Objectives

This tutorial shows you how to perform the following tasks:

  • Deploy the Gemma 1B open model to a GPU backed endpoint by using Model Garden
  • Use the PredictionServiceClient to get online predictions

Costs

In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator.

New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

This tutorial requires you to:

  • Set up a Google Cloud project and enable the Vertex AI API
  • On your local machine:
    • Install, initialize, and authenticate with the Google Cloud CLI
    • Install the SDK for your language

Set up a Google Cloud project

Set up your Google Cloud project and enable the Vertex AI API.

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the Vertex AI API.

    Enable the API

  5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  6. Verify that billing is enabled for your Google Cloud project.

  7. Enable the Vertex AI API.

    Enable the API

    8.

Set up the Google Cloud CLI

On your local machine, set up the Google Cloud CLI.

  1. Install and initialize the Google Cloud CLI.

  2. If you previously installed the gcloud CLI, ensure your gcloud components are updated by running this command.

    gcloud components update

  3. To authenticate with the gcloud CLI, generate a local Application Default Credentials (ADC) file by running this command. The web flow launched by the command is used to provide your user credentials.

    gcloud auth application-default login

    For more information, see gcloud CLI authentication configuration and ADC configuration.

Set up the SDK for your programming language

To set up the environment used in this tutorial, you install the Vertex AI SDK for your language and the Protocol Buffers library. The code samples use functions from the Protocol Buffers library to convert the input dictionary to the JSON format that is expected by the API.

On your local machine, click one of the following tabs to install the SDK for your programming language.

Python

On your local machine, click one of the following tabs to install the SDK for your programming language.

  • Install and update the Vertex AI SDK for Python by running this command.

    pip3 install --upgrade "google-cloud-aiplatform>=1.64"

  • Install the Protocol Buffers library for Python by running this command.

    pip3 install --upgrade "protobuf>=5.28"

Node.js

Install or update the aiplatform SDK for Node.js by running the following command.

npm install @google-cloud/aiplatform

Java

To add google-cloud-aiplatform as a dependency, add the appropriate code for your environment.

Maven with BOM

Add the following HTML to your pom.xml:

<dependencyManagement>
<dependencies>
  <dependency>
    <artifactId>libraries-bom</artifactId>
    <groupId>com.google.cloud</groupId>
    <scope>import</scope>
    <type>pom</type>
    <version>26.34.0</version>
  </dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<dependency>
  <groupId>com.google.cloud</groupId>
  <artifactId>google-cloud-aiplatform</artifactId>
</dependency>
<dependency>
  <groupId>com.google.protobuf</groupId>
  <artifactId>protobuf-java-util</artifactId>
</dependency>
<dependency>
  <groupId>com.google.code.gson</groupId>
  <artifactId>gson</artifactId>
</dependency>
</dependencies>

Maven without BOM

Add the following to your pom.xml:

<dependency>
  <groupId>com.google.cloud</groupId>
  <artifactId>google-cloud-aiplatform</artifactId>
  <version>1.1.0</version>
</dependency>
<dependency>
  <groupId>com.google.protobuf</groupId>
  <artifactId>protobuf-java-util</artifactId>
  <version>5.28</version>
</dependency>
<dependency>
  <groupId>com.google.code.gson</groupId>
  <artifactId>gson</artifactId>
  <version>2.11.0</version>
</dependency>

Gradle without BOM

Add the following to your build.gradle:

implementation 'com.google.cloud:google-cloud-aiplatform:1.1.0'

Go

Install these Go packages by running the following commands.

go get cloud.google.com/go/aiplatform
go get google.golang.org/protobuf
go get github.com/googleapis/gax-go/v2

Deploy Gemma using Model Garden

You can deploy the Gemma 1B by using its model card in the Google Cloud console or programmatically.

For more information about setting up the Google Gen AI SDK or Google Cloud CLI, see the Google Gen AI SDK overview or Install the Google Cloud CLI.

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

  1. List the models that you can deploy and record the model ID to deploy. You can optionally list the supported Hugging Face models in Model Garden and even filter them by model names. The output doesn't include any tuned models.

    
import vertexai
from vertexai import model_garden

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

# List deployable models, optionally list Hugging Face models only or filter by model name.
deployable_models = model_garden.list_deployable_models(list_hf_models=False, model_filter="gemma")
print(deployable_models)
# Example response:
# ['google/gemma2@gemma-2-27b','google/gemma2@gemma-2-27b-it', ...]

  2. View the deployment specifications for a model by using the model ID from the previous step. You can view the machine type, accelerator type, and container image URI that Model Garden has verified for a particular model.

    
import vertexai
from vertexai import model_garden

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# model = "google/gemma3@gemma-3-1b-it"
vertexai.init(project=PROJECT_ID, location="us-central1")

# For Hugging Face modelsm the format is the Hugging Face model name, as in
# "meta-llama/Llama-3.3-70B-Instruct".
# Go to https://console.cloud.google.com/vertex-ai/model-garden to find all deployable
# model names.

model = model_garden.OpenModel(model)
deploy_options = model.list_deploy_options()
print(deploy_options)
# Example response:
# [
#   dedicated_resources {
#     machine_spec {
#       machine_type: "g2-standard-12"
#       accelerator_type: NVIDIA_L4
#       accelerator_count: 1
#     }
#   }
#   container_spec {
#     ...
#   }
#   ...
# ]

  3. Deploy a model to an endpoint. Model Garden uses the default deployment configuration unless you specify additional argument and values.

    
import vertexai
from vertexai import model_garden

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")

open_model = model_garden.OpenModel("google/gemma3@gemma-3-12b-it")
endpoint = open_model.deploy(
    machine_type="g2-standard-48",
    accelerator_type="NVIDIA_L4",
    accelerator_count=4,
    accept_eula=True,
)

# Optional. Run predictions on the deployed endoint.
# endpoint.predict(instances=[{"prompt": "What is Generative AI?"}])

gcloud

Before you begin, specify a quota project to run the following commands. The commands you run are counted against the quotas for that project. For more information, see Set the quota project.

  1. List the models that you can deploy by running the gcloud ai model-garden models list command. This command lists all model IDs and which ones you can self deploy.

    gcloud ai model-garden models list --model-filter=gemma

    In the output, find the model ID to deploy. The following example shows an abbreviated output.

    MODEL_ID                                      CAN_DEPLOY  CAN_PREDICT
google/gemma2@gemma-2-27b                     Yes         No
google/gemma2@gemma-2-27b-it                  Yes         No
google/gemma2@gemma-2-2b                      Yes         No
google/gemma2@gemma-2-2b-it                   Yes         No
google/gemma2@gemma-2-9b                      Yes         No
google/gemma2@gemma-2-9b-it                   Yes         No
google/gemma3@gemma-3-12b-it                  Yes         No
google/gemma3@gemma-3-12b-pt                  Yes         No
google/gemma3@gemma-3-1b-it                   Yes         No
google/gemma3@gemma-3-1b-pt                   Yes         No
google/gemma3@gemma-3-27b-it                  Yes         No
google/gemma3@gemma-3-27b-pt                  Yes         No
google/gemma3@gemma-3-4b-it                   Yes         No
google/gemma3@gemma-3-4b-pt                   Yes         No
google/gemma3n@gemma-3n-e2b                   Yes         No
google/gemma3n@gemma-3n-e2b-it                Yes         No
google/gemma3n@gemma-3n-e4b                   Yes         No
google/gemma3n@gemma-3n-e4b-it                Yes         No
google/gemma@gemma-1.1-2b-it                  Yes         No
google/gemma@gemma-1.1-2b-it-gg-hf            Yes         No
google/gemma@gemma-1.1-7b-it                  Yes         No
google/gemma@gemma-1.1-7b-it-gg-hf            Yes         No
google/gemma@gemma-2b                         Yes         No
google/gemma@gemma-2b-gg-hf                   Yes         No
google/gemma@gemma-2b-it                      Yes         No
google/gemma@gemma-2b-it-gg-hf                Yes         No
google/gemma@gemma-7b                         Yes         No
google/gemma@gemma-7b-gg-hf                   Yes         No
google/gemma@gemma-7b-it                      Yes         No
google/gemma@gemma-7b-it-gg-hf                Yes         No

    The output doesn't include any tuned models or Hugging Face models. To view which Hugging Face models are supported, add the --can-deploy-hugging-face-models flag.

  2. To view the deployment specifications for a model, run the gcloud ai model-garden models list-deployment-config command. You can view the machine type, accelorator type, and container image URI that Model Garden supports for a particular model.

    gcloud ai model-garden models list-deployment-config \
    --model=MODEL_ID

    Replace MODEL_ID with the model ID from the previous list command, such as google/gemma@gemma-2b or stabilityai/stable-diffusion-xl-base-1.0.

  3. Deploy a model to an endpoint by running the gcloud ai model-garden models deploy command. Model Garden generates a display name for your endpoint and uses the default deployment configuration unless you specify additional argument and values.

    To run the command asynchronously, include the --asynchronous flag.

    gcloud ai model-garden models deploy \
    --model=MODEL_ID \
    [--machine-type=MACHINE_TYPE] \
    [--accelerator-type=ACCELERATOR_TYPE] \
    [--endpoint-display-name=ENDPOINT_NAME] \
    [--hugging-face-access-token=HF_ACCESS_TOKEN] \
    [--reservation-affinity reservation-affinity-type=any-reservation] \
    [--reservation-affinity reservation-affinity-type=specific-reservation, key="compute.googleapis.com/reservation-name", values=RESERVATION_RESOURCE_NAME] \
    [--asynchronous]

    Replace the following placeholders:

    • MODEL_ID: The model ID from the previous list command. For Hugging Face models, use the Hugging Face model URL format, such as stabilityai/stable-diffusion-xl-base-1.0.
    • MACHINE_TYPE: Defines the set of resources to deploy for your model, such as g2-standard-4.
    • ACCELERATOR_TYPE: Specifies accelerators to add to your deployment to help improve performance when working with intensive workloads, such as NVIDIA_L4.
    • ENDPOINT_NAME: A name for the deployed Vertex AI endpoint.
    • HF_ACCESS_TOKEN: For Hugging Face models, if the model is gated, provide an access token.
    • RESERVATION_RESOURCE_NAME: To use a specific Compute Engine reservation, specify the name of your reservation. If you specify a specific reservation, you can't specify any-reservation.

    The output includes the deployment configuration that Model Garden used, the endpoint ID, and the deployment operation ID, which you can use to check the deployment status.

    Using the default deployment configuration:
 Machine type: g2-standard-12
 Accelerator type: NVIDIA_L4
 Accelerator count: 1

The project has enough quota. The current usage of quota for accelerator type NVIDIA_L4 in region us-central1 is 0 out of 28.

Deploying the model to the endpoint. To check the deployment status, you can try one of the following methods:
1) Look for endpoint `ENDPOINT_DISPLAY_NAME` at the [Vertex AI] -> [Online prediction] tab in Cloud Console
2) Use `gcloud ai operations describe OPERATION_ID --region=LOCATION` to find the status of the deployment long-running operation

  4. To see details about your deployment, run the gcloud ai endpoints list --list-model-garden-endpoints-only command:

    gcloud ai endpoints list --list-model-garden-endpoints-only \
    --region=LOCATION_ID

    Replace LOCATION_ID with the region where you deployed the model.

    The output includes all endpoints that were created from Model Garden and includes information such as the endpoint ID, endpoint name, and whether the endpoint is associated with a deployed model. To find your deployment, look for the endpoint name that was returned from the previous command.

REST

List all deployable models and then get the ID of the model to deploy. You can then deploy the model with its default configuration and endpoint. Or, you can choose to customize your deployment, such as setting a specific machine type or using a dedicated endpoint.

List models that you can deploy

Before using any of the request data, make the following replacements:

  • PROJECT_ID: Your Google Cloud project ID.
  • QUERY_PARAMETERS: To list Model Garden models, add the following query parameters listAllVersions=True&filter=is_deployable(true). To list Hugging Face models, set the filter to alt=json&is_hf_wildcard(true)+AND+labels.VERIFIED_DEPLOYMENT_CONFIG%3DVERIFIED_DEPLOYMENT_SUCCEED&listAllVersions=True.

HTTP method and URL: 

GET https://us-central1-aiplatform.googleapis.com/v1/publishers/*/models?QUERY_PARAMETERS

To send your request, choose one of these options:

curl

Execute the following command: 

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: PROJECT_ID" \
     "https://us-central1-aiplatform.googleapis.com/v1/publishers/*/models?QUERY_PARAMETERS"

PowerShell

Execute the following command: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_ID" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://us-central1-aiplatform.googleapis.com/v1/publishers/*/models?QUERY_PARAMETERS" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "publisherModels": [
    {
      "name": "publishers/google/models/gemma3",
      "versionId": "gemma-3-1b-it",
      "openSourceCategory": "GOOGLE_OWNED_OSS_WITH_GOOGLE_CHECKPOINT",
      "supportedActions": {
        "openNotebook": {
          "references": {
            "us-central1": {
              "uri": "https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gradio_streaming_chat_completions.ipynb"
            }
          },
          "resourceTitle": "Notebook",
          "resourceUseCase": "Chat Completion Playground",
          "resourceDescription": "Chat with deployed Gemma 2 endpoints via Gradio UI."
        },
        "deploy": {
          "modelDisplayName": "gemma-3-1b-it",
          "containerSpec": {
            "imageUri": "us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20250312_0916_RC01",
            "args": [
              "python",
              "-m",
              "vllm.entrypoints.api_server",
              "--host=0.0.0.0",
              "--port=8080",
              "--model=gs://vertex-model-garden-restricted-us/gemma3/gemma-3-1b-it",
              "--tensor-parallel-size=1",
              "--swap-space=16",
              "--gpu-memory-utilization=0.95",
              "--disable-log-stats"
            ],
            "env": [
              {
                "name": "MODEL_ID",
                "value": "google/gemma-3-1b-it"
              },
              {
                "name": "DEPLOY_SOURCE",
                "value": "UI_NATIVE_MODEL"
              }
            ],
            "ports": [
              {
                "containerPort": 8080
              }
            ],
            "predictRoute": "/generate",
            "healthRoute": "/ping"
          },
          "dedicatedResources": {
            "machineSpec": {
              "machineType": "g2-standard-12",
              "acceleratorType": "NVIDIA_L4",
              "acceleratorCount": 1
            }
          },
          "publicArtifactUri": "gs://vertex-model-garden-restricted-us/gemma3/gemma3.tar.gz",
          "deployTaskName": "vLLM 128K context",
          "deployMetadata": {
            "sampleRequest": "{\n    \"instances\": [\n        {\n          \"@requestFormat\": \"chatCompletions\",\n          \"messages\": [\n              {\n                  \"role\": \"user\",\n                  \"content\": \"What is machine learning?\"\n              }\n          ],\n          \"max_tokens\": 100\n        }\n    ]\n}\n"
          }
        },
        ...

Deploy a model

Deploy a model from Model Garden or a model from Hugging Face. You can also customize the deployment by specifying additional JSON fields.

Deploy a model with its default configuration.

Before using any of the request data, make the following replacements:

  • LOCATION: A region where the model is deployed.
  • PROJECT_ID: Your Google Cloud project ID.
  • MODEL_ID: The ID of the model to deploy, which you can get from listing all the deployable models. The ID uses the following format: publishers/PUBLISHER_NAME/models/ MODEL_NAME@MODEL_VERSION.

HTTP method and URL: 

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy

Request JSON body: 

{
  "publisher_model_name": "MODEL_ID",
  "model_config": {
    "accept_eula": "true"
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

cat > request.json << 'EOF'
{
  "publisher_model_name": "MODEL_ID",
  "model_config": {
    "accept_eula": "true"
  }
}
EOF

Then execute the following command to send your REST request: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy"

PowerShell

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

@'
{
  "publisher_model_name": "MODEL_ID",
  "model_config": {
    "accept_eula": "true"
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployOperationMetadata",
    "genericMetadata": {
      "createTime": "2025-03-13T21:44:44.538780Z",
      "updateTime": "2025-03-13T21:44:44.538780Z"
    },
    "publisherModel": "publishers/google/models/gemma3@gemma-3-1b-it",
    "destination": "projects/PROJECT_ID/locations/LOCATION",
    "projectNumber": "PROJECT_ID"
  }
}

Deploy a Hugging Face model

Before using any of the request data, make the following replacements:

  • LOCATION: A region where the model is deployed.
  • PROJECT_ID: Your Google Cloud project ID.
  • MODEL_ID: The Hugging Face model ID model to deploy, which you can get from listing all the deployable models. The ID uses the following format: PUBLISHER_NAME/MODEL_NAME.
  • ACCESS_TOKEN: If the model is gated, provide an access token.

HTTP method and URL: 

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy

Request JSON body: 

{
  "hugging_face_model_id": "MODEL_ID",
  "hugging_face_access_token": "ACCESS_TOKEN",
  "model_config": {
    "accept_eula": "true"
  }
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

cat > request.json << 'EOF'
{
  "hugging_face_model_id": "MODEL_ID",
  "hugging_face_access_token": "ACCESS_TOKEN",
  "model_config": {
    "accept_eula": "true"
  }
}
EOF

Then execute the following command to send your REST request: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy"

PowerShell

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

@'
{
  "hugging_face_model_id": "MODEL_ID",
  "hugging_face_access_token": "ACCESS_TOKEN",
  "model_config": {
    "accept_eula": "true"
  }
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "name": "projects/PROJECT_ID/locations/us-central1LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployOperationMetadata",
    "genericMetadata": {
      "createTime": "2025-03-13T21:44:44.538780Z",
      "updateTime": "2025-03-13T21:44:44.538780Z"
    },
    "publisherModel": "publishers/PUBLISHER_NAME/model/MODEL_NAME",
    "destination": "projects/PROJECT_ID/locations/LOCATION",
    "projectNumber": "PROJECT_ID"
  }
}

Deploy a model with customizations

Before using any of the request data, make the following replacements:

  • LOCATION: A region where the model is deployed.
  • PROJECT_ID: Your Google Cloud project ID.
  • MODEL_ID: The ID of the model to deploy, which you can get from listing all the deployable models. The ID uses the following format: publishers/PUBLISHER_NAME/models/ MODEL_NAME@MODEL_VERSION, such as google/gemma@gemma-2b or stabilityai/stable-diffusion-xl-base-1.0.
  • MACHINE_TYPE: Defines the set of resources to deploy for your model, such as g2-standard-4.
  • ACCELERATOR_TYPE: Specifies accelerators to add to your deployment to help improve performance when working with intensive workloads, such as NVIDIA_L4
  • ACCELERATOR_COUNT: The number of accelerators to use in your deployment.
  • reservation_affinity_type: To use an existing Compute Engine reservation for your deployment, specify any reservation or a specific one. If you specify this value, don't specify spot.
  • spot: Whether to use spot VMs for your deployment.
  • IMAGE_URI: The location of the container image to use, such as us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20241016_0916_RC00_maas
  • CONTAINER_ARGS: Arguments to pass to the container during the deployment.
  • CONTAINER_PORT: A port number for your container.
  • fast_tryout_enabled: When testing a model, you can choose to use a faster deployment. This option is available only for the highly-used models with certain machine types. If enabled, you cannot specify model or deployment configurations.

HTTP method and URL: 

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy

Request JSON body: 

{
  "publisher_model_name": "MODEL_ID",
  "deploy_config": {
    "dedicated_resources": {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE",
        "accelerator_type": "ACCELERATOR_TYPE",
        "accelerator_count": ACCELERATOR_COUNT,
        "reservation_affinity": {
          "reservation_affinity_type": "ANY_RESERVATION"
        }
      },
      "spot": "false"
    }
  },
  "model_config": {
    "accept_eula": "true",
    "container_spec": {
      "image_uri": "IMAGE_URI",
      "args": [CONTAINER_ARGS ],
      "ports": [
        {
          "container_port": CONTAINER_PORT
        }
      ]
    }
  },
  "deploy_config": {
    "fast_tryout_enabled": false
  },
}

To send your request, choose one of these options:

curl

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

cat > request.json << 'EOF'
{
  "publisher_model_name": "MODEL_ID",
  "deploy_config": {
    "dedicated_resources": {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE",
        "accelerator_type": "ACCELERATOR_TYPE",
        "accelerator_count": ACCELERATOR_COUNT,
        "reservation_affinity": {
          "reservation_affinity_type": "ANY_RESERVATION"
        }
      },
      "spot": "false"
    }
  },
  "model_config": {
    "accept_eula": "true",
    "container_spec": {
      "image_uri": "IMAGE_URI",
      "args": [CONTAINER_ARGS ],
      "ports": [
        {
          "container_port": CONTAINER_PORT
        }
      ]
    }
  },
  "deploy_config": {
    "fast_tryout_enabled": false
  },
}
EOF

Then execute the following command to send your REST request: 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy"

PowerShell

Save the request body in a file named request.json. Run the following command in the terminal to create or overwrite this file in the current directory: 

@'
{
  "publisher_model_name": "MODEL_ID",
  "deploy_config": {
    "dedicated_resources": {
      "machine_spec": {
        "machine_type": "MACHINE_TYPE",
        "accelerator_type": "ACCELERATOR_TYPE",
        "accelerator_count": ACCELERATOR_COUNT,
        "reservation_affinity": {
          "reservation_affinity_type": "ANY_RESERVATION"
        }
      },
      "spot": "false"
    }
  },
  "model_config": {
    "accept_eula": "true",
    "container_spec": {
      "image_uri": "IMAGE_URI",
      "args": [CONTAINER_ARGS ],
      "ports": [
        {
          "container_port": CONTAINER_PORT
        }
      ]
    }
  },
  "deploy_config": {
    "fast_tryout_enabled": false
  },
}
'@  | Out-File -FilePath request.json -Encoding utf8

Then execute the following command to send your REST request: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:deploy" | Select-Object -Expand Content

You receive a JSON response similar to the following.

{
  "name": "projects/PROJECT_ID/locations/LOCATION/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployOperationMetadata",
    "genericMetadata": {
      "createTime": "2025-03-13T21:44:44.538780Z",
      "updateTime": "2025-03-13T21:44:44.538780Z"
    },
    "publisherModel": "publishers/google/models/gemma3@gemma-3-1b-it",
    "destination": "projects/PROJECT_ID/locations/LOCATION",
    "projectNumber": "PROJECT_ID"
  }
}

Console

  1. In the Google Cloud console, go to the Model Garden page.

    Go to Model Garden

  2. Find a supported model that you want to deploy, and click its model card.

  3. Click Deploy to open the Deploy model pane.

  4. In the Deploy model pane, specify details for your deployment.

    1. Use or modify the generated model and endpoint names.
    2. Select a location to create your model endpoint in.
    3. Select a machine type to use for each node of your deployment.

    4. To use a Compute Engine reservation, under the Deployment settings section, select Advanced.

      For the Reservation type field, select a reservation type. The reservation must match your specified machine specs.

      • Automatically use created reservation: Vertex AI automatically selects an allowed reservation with matching properties. If there's no capacity in the automatically selected reservation, Vertex AI uses the general Google Cloud resource pool.
      • Select specific reservations: Vertex AI uses a specific reservation. If there's no capacity for your selected reservation, an error is thrown.
      • Don't use (default): Vertex AI uses the general Google Cloud resource pool. This value has the same effect as not specifying a reservation.

  5. Click Deploy.

Terraform

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands. For more information, see the Terraform provider reference documentation.

Deploy a model

The following example deploys the gemma-3-1b-it model to a new Vertex AI endpoint in us-central1 by using default configurations.

terraform {
  required_providers {
    google = {
      source = "hashicorp/google"
      version = "6.45.0"
    }
  }
}

provider "google" {
  region  = "us-central1"
}

resource "google_vertex_ai_endpoint_with_model_garden_deployment" "gemma_deployment" {
  publisher_model_name = "publishers/google/models/gemma3@gemma-3-1b-it"
  location = "us-central1"
  model_config {
    accept_eula = True
  }
}

To deploy a model with customization, see Vertex AI Endpoint with Model Garden Deployment for details.

Apply the Configuration

terraform init
terraform plan
terraform apply

After you apply the configuration, Terraform provisions a new Vertex AI endpoint and deploys the specified open model.

Clean Up

To delete the endpoint and model deployment, run the following command:

terraform destroy

Inference Gemma 1B with the PredictionServiceClient

After you deploy Gemma 1B, you use the PredictionServiceClient to get online predictions for the prompt: "Why is the sky blue?"

Code parameters

The PredictionServiceClient code samples require you to update the following.

  • PROJECT_ID: To find your project ID follow these steps.

    1. Go to the Welcome page in the Google Cloud console.

      Go to Welcome

    2. From the project picker at the top of the page, select your project.

      The project name, project number, and project ID appear after the Welcome heading.

  • ENDPOINT_REGION: This is the region where you deployed the endpoint.

  • ENDPOINT_ID: To find your endpoint ID, view it in the console or run the gcloud ai endpoints list command. You'll need the endpoint name and region from the Deploy model pane.

    Console

    You can view the endpoint details by clicking Online prediction > Endpoints and selecting your region. Note the number that appears in the ID column.

    Go to Endpoints

    gcloud

    You can view the endpoint details by running the gcloud ai endpoints list command.

    gcloud ai endpoints list \
  --region=ENDPOINT_REGION \
  --filter=display_name=ENDPOINT_NAME

    The output looks like this.

    Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ENDPOINT_ID: 1234567891234567891
DISPLAY_NAME: gemma2-2b-it-mg-one-click-deploy

Sample code

In the sample code for your language, update the PROJECT_ID, ENDPOINT_REGION, and ENDPOINT_ID. Then run your code.

Python

To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation. 

"""
Sample to run inference on a Gemma2 model deployed to a Vertex AI endpoint with GPU accellerators.
"""

from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value

# TODO(developer): Update & uncomment lines below
# PROJECT_ID = "your-project-id"
# ENDPOINT_REGION = "your-vertex-endpoint-region"
# ENDPOINT_ID = "your-vertex-endpoint-id"

# Default configuration
config = {"max_tokens": 1024, "temperature": 0.9, "top_p": 1.0, "top_k": 1}

# Prompt used in the prediction
prompt = "Why is the sky blue?"

# Encapsulate the prompt in a correct format for GPUs
# Example format: [{'inputs': 'Why is the sky blue?', 'parameters': {'temperature': 0.9}}]
input = {"inputs": prompt, "parameters": config}

# Convert input message to a list of GAPIC instances for model input
instances = [json_format.ParseDict(input, Value())]

# Create a client
api_endpoint = f"{ENDPOINT_REGION}-aiplatform.googleapis.com"
client = aiplatform.gapic.PredictionServiceClient(
    client_options={"api_endpoint": api_endpoint}
)

# Call the Gemma2 endpoint
gemma2_end_point = (
    f"projects/{PROJECT_ID}/locations/{ENDPOINT_REGION}/endpoints/{ENDPOINT_ID}"
)
response = client.predict(
    endpoint=gemma2_end_point,
    instances=instances,
)
text_responses = response.predictions
print(text_responses[0])

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

async function gemma2PredictGpu(predictionServiceClient) {
  // Imports the Google Cloud Prediction Service Client library
  const {
    // TODO(developer): Uncomment PredictionServiceClient before running the sample.
    // PredictionServiceClient,
    helpers,
  } = require('@google-cloud/aiplatform');
  /**
   * TODO(developer): Update these variables before running the sample.
   */
  const projectId = 'your-project-id';
  const endpointRegion = 'your-vertex-endpoint-region';
  const endpointId = 'your-vertex-endpoint-id';

  // Default configuration
  const config = {maxOutputTokens: 1024, temperature: 0.9, topP: 1.0, topK: 1};
  // Prompt used in the prediction
  const prompt = 'Why is the sky blue?';

  // Encapsulate the prompt in a correct format for GPUs
  // Example format: [{inputs: 'Why is the sky blue?', parameters: {temperature: 0.9}}]
  const input = {
    inputs: prompt,
    parameters: config,
  };

  // Convert input message to a list of GAPIC instances for model input
  const instances = [helpers.toValue(input)];

  // TODO(developer): Uncomment apiEndpoint and predictionServiceClient before running the sample.
  // const apiEndpoint = `${endpointRegion}-aiplatform.googleapis.com`;

  // Create a client
  // predictionServiceClient = new PredictionServiceClient({apiEndpoint});

  // Call the Gemma2 endpoint
  const gemma2Endpoint = `projects/${projectId}/locations/${endpointRegion}/endpoints/${endpointId}`;

  const [response] = await predictionServiceClient.predict({
    endpoint: gemma2Endpoint,
    instances,
  });

  const predictions = response.predictions;
  const text = predictions[0].stringValue;

  console.log('Predictions:', text);
  return text;
}

module.exports = gemma2PredictGpu;

// TODO(developer): Uncomment below lines before running the sample.
// gemma2PredictGpu(...process.argv.slice(2)).catch(err => {
//   console.error(err.message);
//   process.exitCode = 1;
// });

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 


import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.gson.Gson;
import com.google.protobuf.InvalidProtocolBufferException;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class Gemma2PredictGpu {

  private final PredictionServiceClient predictionServiceClient;

  // Constructor to inject the PredictionServiceClient
  public Gemma2PredictGpu(PredictionServiceClient predictionServiceClient) {
    this.predictionServiceClient = predictionServiceClient;
  }

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String endpointRegion = "us-east4";
    String endpointId = "YOUR_ENDPOINT_ID";

    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.newBuilder()
            .setEndpoint(String.format("%s-aiplatform.googleapis.com:443", endpointRegion))
            .build();
    PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings);
    Gemma2PredictGpu creator = new Gemma2PredictGpu(predictionServiceClient);

    creator.gemma2PredictGpu(projectId, endpointRegion, endpointId);
  }

  // Demonstrates how to run inference on a Gemma2 model
  // deployed to a Vertex AI endpoint with GPU accelerators.
  public String gemma2PredictGpu(String projectId, String region,
               String endpointId) throws IOException {
    Map<String, Object> paramsMap = new HashMap<>();
    paramsMap.put("temperature", 0.9);
    paramsMap.put("maxOutputTokens", 1024);
    paramsMap.put("topP", 1.0);
    paramsMap.put("topK", 1);
    Value parameters = mapToValue(paramsMap);

    // Prompt used in the prediction
    String instance = "{ \"inputs\": \"Why is the sky blue?\"}";
    Value.Builder instanceValue = Value.newBuilder();
    JsonFormat.parser().merge(instance, instanceValue);
    // Encapsulate the prompt in a correct format for GPUs
    // Example format: [{'inputs': 'Why is the sky blue?', 'parameters': {'temperature': 0.8}}]
    List<Value> instances = new ArrayList<>();
    instances.add(instanceValue.build());

    EndpointName endpointName = EndpointName.of(projectId, region, endpointId);

    PredictResponse predictResponse = this.predictionServiceClient
        .predict(endpointName, instances, parameters);
    String textResponse = predictResponse.getPredictions(0).getStringValue();
    System.out.println(textResponse);
    return textResponse;
  }

  private static Value mapToValue(Map<String, Object> map) throws InvalidProtocolBufferException {
    Gson gson = new Gson();
    String json = gson.toJson(map);
    Value.Builder builder = Value.newBuilder();
    JsonFormat.parser().merge(json, builder);
    return builder.build();
  }
}

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Go API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment. 

import (
	"context"
	"fmt"
	"io"

	"cloud.google.com/go/aiplatform/apiv1/aiplatformpb"

	"google.golang.org/protobuf/types/known/structpb"
)

// predictGPU demonstrates how to run interference on a Gemma2 model deployed to a Vertex AI endpoint with GPU accelerators.
func predictGPU(w io.Writer, client PredictionsClient, projectID, location, endpointID string) error {
	ctx := context.Background()

	// Note: client can be initialized in the following way:
	// apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	// client, err := aiplatform.NewPredictionClient(ctx, option.WithEndpoint(apiEndpoint))
	// if err != nil {
	// 	return fmt.Errorf("unable to create prediction client: %v", err)
	// }
	// defer client.Close()

	gemma2Endpoint := fmt.Sprintf("projects/%s/locations/%s/endpoints/%s", projectID, location, endpointID)
	prompt := "Why is the sky blue?"
	parameters := map[string]interface{}{
		"temperature":     0.9,
		"maxOutputTokens": 1024,
		"topP":            1.0,
		"topK":            1,
	}

	// Encapsulate the prompt in a correct format for TPUs.
	// Pay attention that prompt should be set in "inputs" field.
	// Example format: [{'inputs': 'Why is the sky blue?', 'parameters': {'temperature': 0.9}}]
	promptValue, err := structpb.NewValue(map[string]interface{}{
		"inputs":     prompt,
		"parameters": parameters,
	})
	if err != nil {
		fmt.Fprintf(w, "unable to convert prompt to Value: %v", err)
		return err
	}

	req := &aiplatformpb.PredictRequest{
		Endpoint:  gemma2Endpoint,
		Instances: []*structpb.Value{promptValue},
	}

	resp, err := client.Predict(ctx, req)
	if err != nil {
		return err
	}

	prediction := resp.GetPredictions()
	value := prediction[0].GetStringValue()
	fmt.Fprintf(w, "%v", value)

	return nil
}

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

  1. In the Google Cloud console, go to the Manage resources page.

    Go to Manage resources

  2. In the project list, select the project that you want to delete, and then click Delete.
  3. In the dialog, type the project ID, and then click Shut down to delete the project.

Delete individual resources

If you're keeping your project, delete the resources used in this tutorial:

  • Undeploy the model and delete the endpoint
  • Delete the model from Model Registry

Undeploy the model and delete the endpoint

Use one of the following methods to undeploy a model and delete the endpoint.

Console

  1. In the Google Cloud console, click Online prediction and then click Endpoints.

    Go to the Endpoints page

  2. In the Region drop-down list, choose the region where you deployed your endpoint.

  3. Click the endpoint name to open the details page. For example: gemma2-2b-it-mg-one-click-deploy.

  4. On the row for the Gemma 2 (Version 1) model, click Actions, and then click Undeploy model from endpoint.

  5. In the Undeploy model from endpoint dialog, click Undeploy.

  6. Click the Back button to return to the Endpoints page.

    Go to the Endpoints page

  7. At the end of the gemma2-2b-it-mg-one-click-deploy row, click Actions, and then select Delete endpoint.

  8. In the confirmation prompt, click Confirm.

gcloud

To undeploy the model and delete the endpoint using the Google Cloud CLI, follow these steps.

In these commands, replace:

  • PROJECT_ID with your project name
  • LOCATION_ID with the region where you deployed the model and endpoint
  • ENDPOINT_ID with the endpoint ID
  • DEPLOYED_MODEL_NAME with the model's display name
  • DEPLOYED_MODEL_ID with the model ID

  1. Get the endpoint ID by running the gcloud ai endpoints list command. This command lists the endpoint IDs for all endpoints in your project. Make a note of the ID of the endpoint used in this tutorial.

    gcloud ai endpoints list \
    --project=PROJECT_ID \
    --region=LOCATION_ID

    The output looks like this. In the output, the ID is called ENDPOINT_ID.

    Using endpoint [https://us-central1-aiplatform.googleapis.com/]
ENDPOINT_ID: 1234567891234567891
DISPLAY_NAME: gemma2-2b-it-mg-one-click-deploy

  2. Get the model ID by running the gcloud ai models describe command. Make a note of the ID of the model you deployed in this tutorial.

    gcloud ai models describe DEPLOYED_MODEL_NAME \
    --project=PROJECT_ID \
    --region=LOCATION_ID

    The abbreviated output looks like this. In the output, the ID is called deployedModelId.

    Using endpoint [https://us-central1-aiplatform.googleapis.com/]
artifactUri: [URI removed]
baseModelSource:
  modelGardenSource:
    publicModelName: publishers/google/models/gemma2
...
deployedModels:
- deployedModelId: '1234567891234567891'
  endpoint: projects/12345678912/locations/us-central1/endpoints/12345678912345
displayName: gemma2-2b-it-12345678912345
etag: [ETag removed]
modelSourceInfo:
  sourceType: MODEL_GARDEN
name: projects/123456789123/locations/us-central1/models/gemma2-2b-it-12345678912345
...

  3. Undeploy the model from the endpoint. You'll need the endpoint ID and model ID from the previous commands.

    gcloud ai endpoints undeploy-model ENDPOINT_ID \
    --project=PROJECT_ID \
    --region=LOCATION_ID \
    --deployed-model-id=DEPLOYED_MODEL_ID

    This command produces no output.

  4. Run the gcloud ai endpoints delete command to delete the endpoint.

    gcloud ai endpoints delete ENDPOINT_ID \
    --project=PROJECT_ID \
    --region=LOCATION_ID

    When promted, type y to confirm. This command produces no output.

Delete the model

Console

  1. Go to the Model Registry page from the Vertex AI section in the Google Cloud console.

    Go to the Model Registry page

  2. In the Region drop-down list, choose the region where you deployed your model.

  3. At the end of the gemma2-2b-it-1234567891234 row, click Actions.

  4. Select Delete model.

    When you delete the model, all associated model versions and evaluations are deleted from your Google Cloud project.

  5. In the confirmation prompt, click Delete.

gcloud

To delete the model using the Google Cloud CLI, provide the model's display name and region to the gcloud ai models delete command.

gcloud ai models delete DEPLOYED_MODEL_NAME \
    --project=PROJECT_ID \
    --region=LOCATION_ID

Replace DEPLOYED_MODEL_NAME with the model's display name. Replace PROJECT_ID with your project name. Replace LOCATION_ID with the region where you deployed the model.

What's next