LLM Reference
GCP Vertex AI

Using Gemma 4 E4B on GCP Vertex AI

Implementation guide · Gemma 4 · Google DeepMind

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GCP Vertex AI exposes Gemma 4 E4B through model ID gemma-4-e4b. 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. 1
    Create an account at GCP Vertex AI and generate an API key.
  2. 2
    Use the GCP Vertex AI SDK or REST API to call gemma-4-e4b — see the documentation for request format.
  3. 3
    You'll be billed Free/1M input, Free/1M output tokens. See full pricing.

Code Examples

Install
pip install google-cloud-aiplatform
API key
GOOGLE_CLOUD_PROJECT
Model ID
gemma-4-e4b

For 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-4-e4b")
response = model.generate_content("Hello")
print(response.text)

Pricing on GCP Vertex AI

TypePrice (per 1M)
Input tokensFree
Output tokensFree

Capabilities

MultimodalJSON / Tool use

About Gemma 4 E4B

Efficient 4B model with native audio input support. Balances performance and efficiency for edge and on-device deployment with reasoning and coding capabilities.

Model Specs

Released2026-03-31
Parameters4B
Context128k
Knowledge cutoff2025-01

Provider

GCP Vertex AI
GCP Vertex AI

Google Cloud Platform (GCP)

Mountain View, California, United States