Gemma 4 Guide: How to Choose the Right Model & Run It Locally with Ollama

Google dropped Gemma 4 on April 2–3, 2026, and the developer community hasn't stopped talking about it since. Within 24 hours of launch, it ranked #3 among all open models on the Arena AI leaderboard — outcompeting models 20x its size. The best part? It's completely free, open-source under the Apache 2.0 license, and you can run it on your own laptop today using Ollama.

In this guide, we'll explain what Gemma 4 is, how to pick the right variant for your hardware, and how to get it running in minutes.

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What Is Gemma 4?

Gemma 4 is Google DeepMind's latest open-source AI model family. Built from the same research that powers Gemini, it supports:

• Text, image, and audio input (on E2B and E4B variants)
• Native function calling with JSON schema — no prompt tricks needed
• Configurable thinking mode — enable chain-of-thought reasoning on-demand
• Context windows up to 256K tokens on larger variants
• Agentic workflows out of the box

Compared to Gemma 3, the jump is massive: the 31B model scores 80% on LiveCodeBench v6, up from 29.1% on Gemma 3 27B. Its Codeforces ELO jumped from 110 to 2150.

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Gemma 4 Model Variants at a Glance

Gemma 4 comes in four sizes to fit different hardware and use cases:

Tip: The 26b model uses a Mixture of Experts (MoE) architecture — it has 26B total parameters but only activates 3.8B per token. That means you get near-13B quality at 4B speed. It's the smart pick for most power users.

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How to Choose the Right Model

💻 You have a basic laptop (8GB RAM)

Go with gemma4:e4b — the default. It handles code review, Q&A, and generation tasks comfortably. If RAM is very tight, try gemma4:e2b (7.2 GB).

🖥️ You have a mid-range workstation (16–20 GB RAM or VRAM)

The gemma4:26b MoE variant is your sweet spot. With Q4_K_M quantization it fits in ~16 GB and performs like a 13B dense model at 4B speeds.

🔥 You have a high-end GPU (24GB+ VRAM) or Apple Silicon (32GB+)

Go all in with gemma4:31b. It competes with cloud models on most coding and reasoning tasks, and scores 85.2% on MMLU Pro.

📱 You're building for edge / mobile / IoT

Use gemma4:e2b — it supports audio and image input, runs on 4GB RAM, and is optimized for low-latency on-device inference.

Rule of thumb: Start with e4b. If it feels slow or quality isn't enough, scale up to 26b.

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Quick Start: Running Gemma 4 with Ollama

Step 1 — Install Ollama

Ollama is the easiest way to run local LLMs. One install, one command.

macOS:

brew install ollama

Linux:

curl -fsSL https://ollama.com/install.sh | sh

Windows: Download the .exe installer from ollama.com.

Then start the service:

ollama serve

Make sure you're on Ollama v0.20.0 or later — earlier versions don't support Gemma 4. Check with: ollama --version

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Step 2 — Pull Your Gemma 4 Model

# Default (E4B — best for most users)
ollama pull gemma4

# MoE variant (best quality-to-size, needs 20GB+)
ollama pull gemma4:26b

# Full flagship (needs 24GB+)
ollama pull gemma4:31b

# Smallest model (edge / limited hardware)
ollama pull gemma4:e2b

Download sizes range from ~7 GB (E2B) to ~20 GB (31B).

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Step 3 — Run It

ollama run gemma4

That's it. You're now running a top-3 open-source AI model, completely offline, for free. Try asking it something practical: