Google Gemma 4: What It Is and How to Run It on Your PC or Mac

Google just released Gemma 4 — and this one is different.

While most AI tools require an internet connection and send your data to a cloud server, Gemma 4 runs entirely on your own device. Offline. Private. Fast. And it's free to use, including commercially, under the Apache 2.0 license.

Here's everything you need to know about what Gemma 4 is, what makes it a leap forward, and exactly how to get it running on your Mac or PC today.

What Is Gemma 4?

Gemma 4 is an open-source AI model family released by Google DeepMind on April 2, 2026. It's built from the same research that powers Gemini 3 — one of the most capable AI systems in the world — but packaged to run on your local hardware without any cloud dependency.

The name "Gemma" (from "gemma," meaning gem) signals Google's intent: these are precision-cut open models for developers and power users who want real AI capability without the data-sharing trade-offs of cloud services.

What's new in Gemma 4 vs. previous versions:

• Agentic by design — Gemma 4 can perform multi-step tasks, use tools, and make decisions autonomously. Previous Gemma models were primarily conversational.
• Multimodal — understands images, audio, and video alongside text
• 140+ language support — usable across a global audience without model swapping
• 128K context window — reads and reasons over massive documents in a single pass
• E2B and E4B model sizes — runs on hardware with as little as 1.5GB–3GB of memory

The E2B and E4B naming refers to "effective parameters." Under the hood, Gemma 4 uses the MatFormer architecture (a nested "Matryoshka-style" transformer), which means a 5B-parameter model behaves with the memory footprint of a 2B model (E2B), and an 8B-parameter model runs like a 4B (E4B).

In practical terms: E2B runs on almost any modern laptop with 8GB RAM. E4B runs well on 16GB RAM machines.

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How to Install Gemma 4 on Mac or PC

There are three main ways to run Gemma 4 locally. Here they are ranked by ease.

Option 1: Ollama (Recommended — Easiest)

Ollama is the simplest way to run any local AI model. It handles the download, model management, and a local API endpoint automatically.

New to the terminal? The commands below are copy-paste simple, but if you've never opened Terminal on Mac or Command Prompt on Windows, our Terminal Beginner's Guide walks you through it step by step.

macOS:

# Install Ollama via the official installer
# Download from: https://ollama.com/download

# Then pull and run Gemma 4
ollama run gemma4

Or choose a specific size:

ollama run gemma4:e2b   # Smaller, faster — best for most laptops
ollama run gemma4:e4b   # Better quality — recommended for 16GB+ RAM

Windows:

1. Download the Ollama installer from ollama.com
2. Run the .exe installer
3. Open Command Prompt or PowerShell
4. Run ollama run gemma4

Linux:

curl -fsSL https://ollama.com/install.sh | sh
ollama run gemma4

Once Ollama is running, Gemma 4 is also accessible via a local API at http://localhost:11434 — compatible with the OpenAI SDK, so any app that supports OpenAI models can be pointed at your local Gemma 4 instance.

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Option 2: LM Studio (Best for Non-Developers)

LM Studio is a free desktop application with a clean GUI. No terminal required.

1. Download LM Studio from lmstudio.ai
2. Open the Discover tab and search "gemma 4"
3. Download the E2B version for most laptops, or E4B if you have 16GB+ RAM
4. Click Load Model, then open the Chat tab
5. Start talking

LM Studio also provides a local server mode, so you can integrate your locally-running Gemma 4 into other apps.

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Option 3: LiteRT-LM CLI (For Developers)

Google's own CLI tool for running Gemma 4 — optimized for performance and tool calling.

pip install litert-lm
litert-lm --model gemma4-e4b-it

This is the setup Google uses internally for Agent Skills — the multi-step agentic workflows that make Gemma 4 unique. It also supports structured output and constrained decoding, making it ideal for production pipelines.

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System Requirements

Setup	Minimum RAM	Recommended	Storage
Gemma 4 E2B	6GB RAM	8GB RAM	~3GB
Gemma 4 E4B	8GB RAM	16GB RAM	~5GB
Gemma 4 E4B (GPU)	4GB VRAM	8GB VRAM	~5GB

Apple Silicon Macs (M1/M2/M3/M4) are particularly well-suited — unified memory handles both CPU and GPU workloads, so even an M1 MacBook Air can run E4B smoothly.

Not sure how much VRAM your GPU has? Here's how to check your VRAM on Windows and Mac — takes about 30 seconds.

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10 Things You Can Actually Do with Gemma 4

Gemma 4 isn't just a chatbot. Its agentic architecture opens up a range of practical local use cases that weren't viable with previous open models.

1. Private AI assistant (offline chat)

Ask questions, get summaries, brainstorm — without a single byte leaving your device. Useful for sensitive business documents or NDA-protected content.

2. Offline code generation and debugging

Paste your codebase context, describe a bug, and get a fix — entirely offline. At 128K tokens of context, Gemma 4 can handle entire codebases in a single pass.

3. Real-time translation

Supports 140+ languages out of the box. Use the audio encoder for speech-to-translated-text in real time, no internet required.

4. Local document analysis

Drop in a 100-page PDF, ask specific questions, get structured answers — processed locally with no cloud upload.

5. Multi-step task automation (agentic workflows)

Using the LiteRT-LM tool calling API, Gemma 4 can plan and execute multi-step workflows: web queries, data formatting, file manipulation, and API calls — all orchestrated by the model itself.

6. Image and video understanding

Point it at a photo or video file — Gemma 4's MobileNet-V5 vision encoder identifies objects, reads text, and answers questions about the visual content.

7. Local voice assistant

Pair Gemma 4's audio understanding with a text-to-speech layer and you have a fully offline voice assistant — privacy-first Siri/Alexa alternative.

8. On-device coding teacher

Create a local study tool that explains code, quizzes you on concepts, or generates exercises — fully customized and offline.

9. Customer support chatbot (self-hosted)

Build a fully self-hosted support chatbot trained on your documentation — no third-party API fees, no data going to Google or OpenAI.

For developers who want to combine Gemma 4's local capability with a production-ready chatbot deployment layer, CustomGPT.ai offers a zero-hallucination infrastructure that handles document ingestion, session management, and analytics on top of your model.

10. AI on IoT devices

Google officially supports Gemma 4 E2B on Raspberry Pi 5 — achieving 133 tokens/second prefill and 7.6 tokens/second decode. This opens up use cases in robotics, smart home controllers, and offline edge computing.

For running heavier Gemma 4 workloads — fine-tuning, batch inference, or running E4B at scale — Ampere.sh provides GPU compute optimized for ARM-based AI workloads at significantly lower cost than AWS or GCP at comparable performance.

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Gemma 4 vs. Other Local Models in 2026

Model	Context Window	Local VRAM Needed	Agentic	License
Gemma 4 E4B	128K	~3GB	✅ Yes	Apache 2.0
Llama 4 Scout 17B	10M	~8GB	Partial	Meta
Mistral 7B	32K	~4GB	No	Apache 2.0
Phi-4	16K	~4GB	Partial	MIT
Qwen2.5 7B	128K	~4GB	Partial	Apache 2.0

Gemma 4 E4B stands out for combining the smallest effective memory footprint with the largest context window and the most capable agentic execution among all comparable open models at the time of release.

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Why This Release Matters

Every AI tool you use today — ChatGPT, Claude, Gemini — runs on cloud servers. Your prompts, documents, and conversations pass through third-party infrastructure.

Gemma 4 is Google's strongest signal yet that capable AI can run at the edge — on your device, offline, with no usage costs after setup. For businesses handling sensitive data, developers building privacy-first apps, and hobbyists in low-connectivity environments, this is a meaningful shift.

The agentic capability is the real story. Earlier local models were good for conversations. Gemma 4 can actually act — plan tasks, call tools, produce structured outputs, and chain multi-step workflows — all without an internet connection.

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Frequently Asked Questions

Is Gemma 4 free to use commercially?

Yes. Gemma 4 is released under the Apache 2.0 license, which allows commercial use, redistribution, and modification without royalties or restrictions.

Can Gemma 4 run on a MacBook Air?

Yes. The E2B model runs on any MacBook Air with 8GB or more of unified memory, including M1, M2, M3, and M4 models. Apple Silicon's unified memory architecture makes local AI particularly efficient.

Does Gemma 4 require an internet connection after setup?

No. Once the model is downloaded (3–5GB depending on size), Gemma 4 operates entirely offline. No API keys, no cloud calls, no usage costs.

What is the difference between Gemma 4 E2B and E4B?

Both models have the same architecture, but E4B uses more active parameters during inference, resulting in higher quality outputs — particularly for complex reasoning, code generation, and multilingual tasks. E2B is faster and more memory-efficient, suitable for most everyday tasks.

Can I fine-tune Gemma 4?

Yes. Fine-tuning support is available via Hugging Face Transformers + TRL, Unsloth (for memory-efficient fine-tuning on consumer GPUs), and NVIDIA NeMo for enterprise fine-tuning pipelines.

What happened to Gemma 3n — is it the same as Gemma 4?

Gemma 3n (released in preview in March 2026) introduced the MatFormer architecture and Per-Layer Embeddings. Gemma 4 is the full commercial release building on those foundations, adding complete agentic support, extended tool-calling, the 128K context window, and broader platform support (iOS, Android, desktop, IoT).