Test model capabilities in Model Garden

Model Garden provides several options for you to quickly view and test model capabilities. For supported models, you can try demo playgrounds or launch demo applications called Model Garden Spaces that you can share with others to showcase a model's capabilities.

Playgrounds are powered by predeployed Vertex AI online prediction endpoints and don't incur charges. When you open the model card for a supported model, you can use the Try out panel to quickly test the model's capabilities by sending a text prompt. You can also set some of the most common parameters such as temperature and number of output tokens. The playground is limited to text input and output only.

When you launch Spaces, you have a working web application that's ready to use with far less manual effort than deploying a model and building an app to use the model's endpoint. Model Garden deploys your selected model in Vertex AI and deploys the sample app on a Cloud Run instance that uses the deployed model's endpoint. The application can also use existing endpoints, or a MaaS endpoint.

To launch a model, open the model card for the supported model, and in the Try out Spaces panel, click a Space to launch one. You are charged for the machines that are used for the deployment and for the Cloud Run instance that's hosting the app.

Before you begin

This tutorial requires you to set up a Google Cloud project and enable the Vertex AI API.

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.

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

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Enable the API

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

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Enable the API

Try a Playground

1. In the Google Cloud console, go to a supported model's model card, such as the Gemma 2 model card.

   Go to Gemma 2

2. In the Try out panel:

   1. For Region, accept the default or choose your region.
   2. For Endpoint, select Demo playground.
   3. In the Prompt box, enter Why is the sky blue?.
   4. Expand the Advanced options section and view the default parameters.

   

3. Click Submit. The output appears below the Submit button.

Try Spaces

You can launch Spaces with models such as Gemini, Gemma, Llama, and Stable Diffusion. The following list is an example of what's supported:

• BLIP image captioning
• BLIP VQA
• BLIP2
• Flux
• Gemma 2
• Gemma 3
• Gemini 2.5 Flash
• Gemini 2.5 Pro
• Instant ID
• Llama 3.2
• Llama 3.3
• Llama 4
• Llama 3.2 90B
• Llama 4 Maverick 17B-128E
• LLaVA 1.5 & LLaVA-NeXT
• Mistral Self-host (7B & Nemo)
• PaliGemma 1 & 2
• Phi-3
• Phi-4
• Qwen2
• Stable Diffusion Inpainting
• Stable Diffusion v2.1
• Stable Diffusion XL LCM
• Stable Diffusion XL Lightning
• Stable Diffusion XL
• DeepSeek R1 (0528)

IAM permissions

In addition to the existing permissions to use Vertex AI, you must have the following permissions to launch Spaces:

Action	Required permissions	Purpose
Enable additional APIs	serviceusage.services.enable	Enable the following APIs: Cloud Run Admin API (run.googleapis.com) Artifact Registry API (artifactregistry.googleapis.com) Cloud Build API (cloudbuild.googleapis.com) Cloud Logging API (logging.googleapis.com)
Grant permissions to service accounts	resourcemanager.projects.setIamPolicy	Grant the Compute Engine default service account the following roles: Vertex AI Service Agent (roles/aiplatform.serviceAgent) Cloud Build Service Account (roles/cloudbuild.builds.builder)
Deploy specific permissions	storage.buckets.create run.services.create artifactregistry.repositories.create run.services.setIamPolicy	During deployment, a set of source codes will be uploaded to Cloud Storage and then be deployed to Cloud Run with a new service created. The artifactregistry.repositories.create is required to create a repository for the container image. The run.services.setIamPolicy is required to make the service publicly accessible.

If you are the owner of your project, you don't need to take additional actions but follow the guides in the Vertex AI Studio. If you are not the owner of your project, ask your project administrator to perform the first two actions, and then grant you the Editor (roles/editor) and the Cloud Run Admin (roles/run.admin) roles.

Launch Spaces

Launch Spaces to test and experiment with a model from a sample Gradio application.

1. In the Google Cloud console, go to Model Garden to view a model's model card.

   Go to Model Garden

2. Select the model to use. Supported models have a Try out Spaces panel, such as the Gemma 3 model card.

   Go to Gemma 3

3. Click rocket_launch Run to launch a Space.

   1. You can choose to Require authentication (via Identity Aware Proxy) or Allow public access. For more information, see Enable APIs for the first deployment and grant permissions.
   1. Click Create new service to start the deployment. You can monitor the deployment status from the model card.

4. After the Spaces status changes to Ready, click it to view details about the deployment.

   For basic protection, the web application requires a secret key that must be appended to the URL when submitting prompts. This secret key is provided in the Secret key field.

   1. Click Open to start using the app. You can send prompts to the model and view its responses from within the app.

   You can share the URL so that others can try the app too.

   1. To close access to the app, click edit Edit in the Access control field.

   In the Security tab for your Cloud Run application, select Require authentication and then click Save. The application is no longer available through the URL. Visits to the URL result in a 403 error (forbidden).

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

Delete Spaces

To clean up Spaces, you must delete both the model's resources and the sample application's resources on Cloud Run.

Delete model resources

From within the Gradio app, you can delete model endpoints to clean up Vertex AI resources. Then, you need to delete the Cloud Run service to stop and delete the Gradio app.

To manually delete Vertex AI resources, see Undeploy models and delete resources.

Delete Cloud Run service

Delete resources related to a service, including all revision of the service. Deleting a service doesn't include items like container images from Artifact Registry. For more information see, Managing services in the Cloud Run documentation.

1. In the Google Cloud console, view the list of Cloud Run services:

   Go to Cloud Run

2. Locate the service to delete, and then select it.

3. Click delete Delete. This deletes all revisions of the service.

Delete the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To 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.

What's next

See an overview of Model Garden.