How to Deploy Gemma 4 AI Models Using Docker Hub

Introduction

Docker Hub is rapidly becoming the go-to registry for AI models, offering millions of developers a curated catalog that ranges from lightweight edge models to high-performance large language models (LLMs). Now, with the arrival of Gemma 4, you can access the latest generation of lightweight, state-of-the-art open models—built on the same technology behind Gemini. This guide walks you through the simple steps to get Gemma 4 running in your environment, from choosing the right model variant to deploying it with your existing Docker workflows.

What You Need

• Docker Desktop or Docker Engine installed (version 24.0 or later recommended)
• A Docker Hub account (free tier works fine)
• Basic familiarity with the command line
• Internet access to pull model artifacts

Step-by-Step Deployment Guide

Step 1: Understand the Gemma 4 Model Portfolio

Gemma 4 introduces three distinct architectures optimized for different scenarios. Before pulling a model, decide which variant fits your needs:

• Small & Efficient (E2B, E4B) – Designed for edge devices with high throughput and low memory footprint.
• Sparsely Activated (26B A4B) – A mixture-of-experts model that delivers large-model quality with smaller-model speed.
• Flagship Dense (31B) – Full-performance model with a 256K token context window for long-context reasoning.

All models support multimodal inputs (text, image, audio), advanced reasoning via “thinking” tokens, and strong coding and function-calling abilities.

Step 2: Pull a Gemma 4 Model from Docker Hub

Open your terminal and use the docker model pull command. This works exactly like pulling container images—no proprietary tools or custom authentication flows required. For the default Gemma 4 model, run:

docker model pull gemma4

If you need a specific variant, append the architecture tag. For example:

docker model pull gemma4:26b-a4b   # for the sparse MoE model

Docker Hub treats AI models as OCI artifacts, so they are versioned, shareable, and instantly deployable. You can also tag and push your own fine-tuned models.

Step 3: Verify the Model Artifact

After the pull completes, verify that the model is stored locally:

docker model ls

You should see your pulled Gemma 4 model listed with its tag and size. This confirms the artifact is ready for deployment.

Step 4: Run the Model (Local Inference)

Docker Model Runner, which lets you run models directly from Docker Desktop, is coming soon for Gemma 4. For now, you can run inference using a lightweight inference wrapper. Docker Hub’s catalog includes many inference tools (like Ollama, Llama.cpp) that you can combine with Gemma 4. For example, you can mount the model artifact into a container:

docker run --rm -v /var/lib/docker/models/gemma4:/model ghcr.io/your-inference-tool --model /model

Check the Tips section for recommended inference containers.

Step 5: Integrate into Your CI/CD Pipeline

Because Gemma 4 is packaged as an OCI artifact, you can treat it like any other container image in your pipeline. Use familiar Docker commands to:

• Authenticate with your Docker Hub credentials.
• Pull the model during the build phase.
• Tag and push fine-tuned versions back to a private registry.
• Deploy the model to Kubernetes or cloud services using standard OCI tooling.

Example GitLab job snippet:

docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD
 docker model pull gemma4:latest
 # ... run tests ...
 docker model tag gemma4 my-registry/gemma4:v1
 docker model push my-registry/gemma4:v1

Step 6: Scale Performance Across Environments

Gemma 4’s architectures allow you to scale from laptop to server. Use Docker Compose or Kubernetes to deploy multiple instances. For sparse models, you can run several replicas with minimal memory overhead. The same docker model pull workflow works on any machine with Docker installed.

Tips for Success

• Start small: Choose the E2B or E4B variant for edge devices or local testing. They require less than 8GB VRAM.
• Use the right inference container: Docker Hub’s GenAI catalog includes tools like Ollama, vLLM, and TensorFlow Serving that are compatible with Gemma 4 OCI artifacts.
• Stay updated: Over the next few weeks, Docker Model Runner will add native support for running Gemma 4 directly from Docker Desktop. Keep an eye on the Docker Hub GenAI page.
• Secure your models: Use Docker’s access control and signing features to manage who can pull or push your AI artifacts.
• Monitor performance: For production, use Docker’s logging and metrics to track inference times and resource usage.

With Gemma 4 now on Docker Hub, you have everything you need to integrate cutting-edge AI into your applications using a single, familiar toolchain.