Using Gemma 3 on GCP Vertex AI
Implementation guide · Gemma 3 · Google DeepMind
ServerlessOpen Weights
GCP Vertex AI exposes Gemma 3 through model ID gemma-3. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-06-15. Next refresh: weekly.
Quick Start
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Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
gemma-3For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
import os
import vertexai
from vertexai.generative_models import GenerativeModel
# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("gemma-3")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | Free |
| Output tokens | Free |
Capabilities
Structured Outputs
About Gemma 3
Lightweight, state-of-the-art open model built from the same technology that powers Gemini models. Available for free local deployment via Hugging Face and other hosting providers.
Model Specs
Released2025-03-12
ArchitectureDecoder Only
Knowledge cutoff2025-01