Using Gemma 2B Instruct on GCP Vertex AI
Implementation guide · Gemma · Google DeepMind
GCP Vertex AI exposes Gemma 2B Instruct through model ID gemma-2b-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-2b-it— see the documentation for request format. - 3
Code Examples
pip install google-cloud-aiplatformGOOGLE_CLOUD_PROJECTgemma-2b-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-2b-it")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.04 |
| Output tokens | $0.12 |
Capabilities
About Gemma 2B Instruct
Gemma 2B Instruct is a large language model developed by Google, designed to balance performance and accessibility with its 2 billion parameters. Derived from the Gemini family, it excels in tasks such as text generation, code interpretation, and mathematical problem-solving. Built on a transformer decoder architecture, it features multi-query attention, RoPE, GeGLU activations, and RMSNorm. Trained on approximately 6 trillion tokens, including web documents, code, and mathematical content, it uses SFT and RLHF for instruction-tuning. Notable for its lightweight design permitting deployment on consumer-grade hardware, it's open-source and optimized for dialogue applications.