How to Intermediate · 4 min read

How to deploy Llama on GCP

Q: How to deploy Llama on GCP

To deploy Llama on GCP, use Google Vertex AI for managed model hosting or set up a custom Compute Engine VM with GPU support to run the model locally. Use the Vertex AI Python SDK to deploy and serve the model with scalable endpoints.

Quick answer

To deploy Llama on GCP, use Google Vertex AI for managed model hosting or set up a custom Compute Engine VM with GPU support to run the model locally. Use the Vertex AI Python SDK to deploy and serve the model with scalable endpoints.

PREREQUISITES

Python 3.8+
Google Cloud SDK installed and configured
GCP project with billing enabled
Enable Vertex AI API in GCP Console
pip install google-cloud-aiplatform
Docker installed (for custom container deployment)
Access to Llama model weights or container image

Setup Google Cloud environment

Prepare your Google Cloud environment by creating a project, enabling the Vertex AI API, and installing the necessary SDKs. Authenticate your local environment with gcloud auth login and set your project ID.

bash

gcloud auth login
gcloud config set project YOUR_PROJECT_ID
gcloud services enable aiplatform.googleapis.com
pip install google-cloud-aiplatform

Deploy Llama with Vertex AI

Use Vertex AI to deploy Llama by uploading your model container or custom-trained model to Google Cloud Storage, then create a Vertex AI Model resource and deploy it to an endpoint for online predictions.

python

from google.cloud import aiplatform

PROJECT_ID = "YOUR_PROJECT_ID"
REGION = "us-central1"
MODEL_DISPLAY_NAME = "llama-model"
ENDPOINT_DISPLAY_NAME = "llama-endpoint"

# Initialize Vertex AI client
client = aiplatform.gapic.EndpointServiceClient(client_options={"api_endpoint": f"{REGION}-aiplatform.googleapis.com"})

# Upload model container or artifact to GCS before this step
MODEL_CONTAINER_IMAGE_URI = "gcr.io/your-project/llama-container:latest"

# Create model resource
model = aiplatform.Model.upload(
    display_name=MODEL_DISPLAY_NAME,
    serving_container_image_uri=MODEL_CONTAINER_IMAGE_URI,
    project=PROJECT_ID,
    location=REGION
)

# Deploy model to endpoint
endpoint = model.deploy(
    deployed_model_display_name="llama-deployment",
    machine_type="n1-standard-8",
    accelerator_type="NVIDIA_TESLA_T4",
    accelerator_count=1
)

print(f"Model deployed to endpoint: {endpoint.resource_name}")

output

Model deployed to endpoint: projects/YOUR_PROJECT_ID/locations/us-central1/endpoints/1234567890

Run inference on deployed Llama model

Send text prompts to the deployed Llama model endpoint using the Vertex AI SDK for real-time predictions.

python

from google.cloud import aiplatform

client = aiplatform.gapic.PredictionServiceClient(client_options={"api_endpoint": "us-central1-aiplatform.googleapis.com"})

endpoint_name = "projects/YOUR_PROJECT_ID/locations/us-central1/endpoints/1234567890"

instances = [{"prompt": "Explain the benefits of AI."}]
parameters = {}

response = client.predict(endpoint=endpoint_name, instances=instances, parameters=parameters)

print("Prediction response:", response.predictions)

output

Prediction response: [{'generated_text': 'AI improves efficiency, automates tasks, and enables new insights.'}]

Common variations and tips

Use Compute Engine VMs with GPUs for custom Llama deployments if you need full control over the environment.
Containerize your Llama model with Docker for portability and deploy it on GCP Kubernetes Engine or Vertex AI custom containers.
Adjust machine types and GPU accelerators based on model size and latency requirements.
Use Vertex AI Pipelines for automated retraining and deployment workflows.

✅

Key Takeaways

Use Google Vertex AI for scalable, managed Llama model deployment on GCP.
Containerize Llama models with Docker for flexible deployment options.
Choose appropriate GPU-enabled machine types for optimal inference performance.

Verified 2026-04 · llama-3.3-70b, llama-3.1-405b

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