LLamaRecipes/terraform/gcp-vertex-ai-default

GCP Vertex AI deployment

Deploy Llama 4 Scout models using Google Cloud Vertex AI managed service.

Overview

This Terraform configuration sets up a basic example deployment, demonstrating how to deploy/serve foundation models using GCP Vertex. Vertex AI provides fully managed ML services with Model-as-a-Service (MaaS) endpoints.

This example shows how to use basic services such as:

• IAM roles for permissions management
• Service accounts for fine-grained access control
• Creating Vertex endpoints for model serving

In our architecture patterns for private cloud guide we outline advanced patterns for cloud deployment that you may choose to implement in a more complete deployment. This includes:

• Deployment into multiple regions or clouds
• Managed keys/secrets services
• Comprehensive logging systems for auditing and compliance
• Backup and recovery systems

Getting started

Prerequisites

• GCP project with billing account enabled (required for API activation)
• Terraform installed
• Gcloud CLI configured
• Application Default Credentials: gcloud auth application-default login

Deploy

1. Configure GCP authentication:

   gcloud auth login
   gcloud config set project YOUR_PROJECT_ID
   

2. Edit terraform.tfvars with your project ID.

3. Create configuration:

   cd terraform/gcp-vertex-ai-default
   cp terraform.tfvars.example terraform.tfvars
   

4. Deploy:

   terraform init
   terraform plan
   terraform apply
   

Usage

1. Accept Llama Community License in Vertex AI Model Garden
2. Use Llama 4 Scout via MaaS API:

from google.cloud import aiplatform

aiplatform.init(
    project="your-project-id",
    location="us-central1"
)

# Model ID: meta/llama-4-scout-17b-16e-instruct-maas

Next steps

• Vertex AI Documentation
• Vertex AI Model Garden