Using Gemma 3 12B on GCP Vertex AI
Implementation guide · Gemma 3 · Google DeepMind
ServerlessOpen Weights
GCP Vertex AI exposes Gemma 3 12B through model ID gemma-3-12b-it. 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
- 1
- 2Use the GCP Vertex AI SDK or REST API to call
gemma-3-12b-it— see the documentation for request format. - 3
Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
gemma-3-12b-itFor 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-3-12b-it")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.04 |
| Output tokens | $0.13 |
Capabilities
Structured Outputs
About Gemma 3 12B
Gemma 3 12B is Google DeepMind's Gemma 3 model. It offers a 33K-token context window with weights openly available for self-hosting.
Model Specs
Released2026-01-01
Parameters12B
Context33k
ArchitectureDecoder Only
Knowledge cutoff2024-08