Using Gemma 7B on Google Vertex AI on GCP Vertex AI
Implementation guide · Gemma · Google DeepMind
ServerlessOpen Weights
GCP Vertex AI exposes Gemma 7B on Google Vertex AI through model ID vertex-gemma-7b. 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
vertex-gemma-7b— see the documentation for request format. - 3
Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
vertex-gemma-7bFor 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("vertex-gemma-7b")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.13 |
| Output tokens | $0.38 |
Capabilities
Structured Outputs
About Gemma 7B on Google Vertex AI
Gemma 7B on Google Vertex AI is Google DeepMind's Gemma model. It offers an 8K-token context window with weights openly available for self-hosting.
Model Specs
Released2024-02-21
Parameters7B
Context8k
ArchitectureDecoder Only