Using Gemma 2 9B on GCP Vertex AI
Implementation guide · Gemma 2 · Google DeepMind
ServerlessOpen Weights
GCP Vertex AI exposes Gemma 2 9B through model ID gemma-2-9b. 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-2-9b— see the documentation for request format. - 3
Code Examples
Install
pip install google-cloud-aiplatformAPI key
GOOGLE_CLOUD_PROJECTModel ID
gemma-2-9bFor 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-2-9b")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.06 |
| Output tokens | $0.18 |
Capabilities
Structured Outputs
About Gemma 2 9B
Gemma 2 9B is Google DeepMind's Gemma 2 model. It offers an 8K-token context window with weights openly available for self-hosting and scores 52.1 on MMLU PRO.
Model Specs
Released2024-06-27
Parameters9B
Context8k
ArchitectureDecoder Only