Gemma 4: Google's AI Model You Can Run in Your Own Company Without Cloud Dependency

Most SMEs that want to use artificial intelligence face the same dilemma: either they pay a monthly subscription to a cloud service (ChatGPT, Claude, Gemini) and send their data to external servers, or they abandon AI because they believe they need a large corporate technical team and budget to set it up internally.

Gemma 4 breaks that equation. On April 2, 2026, Google published a family of open AI models, with an Apache 2.0 license, that you can run on your own computer, server, or even a mobile device. No fees, no sending data outside, no commercial restrictions. And with performance that surpasses models 20 times larger.

This is not an academic toy. Gemma 4 already ranks third on the Arena AI leaderboard, ahead of models that require entire GPU clusters to function.

What is Gemma 4 and why should your company care

Gemma 4 is Google DeepMind's latest family of open AI models, derived from Gemini 3 technology but designed to run outside of Google's infrastructure. It comes in four variants that cover everything from a mobile device to a GPU-equipped server:

Variant	Effective Parameters	Minimum RAM/VRAM	Primary Use Case
E2B	~2.3B	4-8 GB RAM	Mobile, Raspberry Pi, IoT devices
E4B	~4.5B	6 GB VRAM	Laptops, office computers
26B MoE	~4B active (of 26B)	~8 GB VRAM	Light servers, consumer GPUs
31B Dense	31B	~20 GB VRAM	Maximum quality, professional GPUs or Mac with 32GB+

The E in E2B and E4B stands for "effective." These are models optimized for the edge (devices with limited resources) that activate only a fraction of their parameters during inference, saving memory and battery without sacrificing quality.

The 26B MoE (Mixture of Experts) uses 128 experts but only activates 3.8B parameters per query, giving it surprising speed for its level of quality.

Apache 2.0 License: No Fine Print

Unlike models like Llama, which have commercial usage restrictions above certain volumes, Gemma 4 uses Apache 2.0, one of the most permissive licenses available. You can use it for any commercial purpose, modify it, distribute it, and integrate it into your products without paying royalties or asking for permission from Google.

Benchmarks: Numbers that Matter

Benchmarks must be taken with perspective, but when a 31B parameter model consistently outperforms 400B+ models, it deserves attention:

Benchmark	Gemma 4 31B	Llama 4 (400B+ MoE)	DeepSeek V4	GPT
AIME 2026 (math)	89.2%	88.3%	42.5%	37.5%
LiveCodeBench v6 (programming)	80.0%	77.1%	52.0%	44.0%
GPQA Diamond (science)	84.3%	82.3%	58.6%	43.4%
MMLU Pro (general knowledge)	85.2%	—	—	—

The key takeaway: Gemma 4 achieves these results with 31B parameters. Llama 4 requires over 400B in its MoE configuration. This means Gemma 4 can run on a single PC with a consumer GPU, while Llama 4 in its full version requires data center infrastructure.

For an SME, this translates to something very concrete: you can have a top-tier AI model running in your office for the cost of a computer with a good GPU.

Multimodal Capabilities: More Than Just Text

Gemma 4 is not limited to processing text. All models in the family can analyze images and video. The smaller models (E2B and E4B) also natively understand audio.

This opens up practical possibilities for businesses:

• Analysis of scanned documents: invoices, delivery notes, photographed contracts. Gemma 4 can extract data from document images without needing external OCR.
• Visual inspection: if your company works with physical products, you can use Gemma 4 to analyze photographs and detect defects or classify items.
• Meeting transcription and analysis: edge models can process meeting audio locally, ensuring conversations never leave your network.
• Multi-language support: with native support for over 140 languages, it is ideal for companies working with international clients or suppliers.

Agentic Capabilities: The AI That Acts

Gemma 4 comes pre-built to function as an AI agent. It includes native support for function calling, with 6 dedicated control tokens that allow it to interact with external tools in a structured way.

This means you can configure Gemma 4 to not only answer questions but also execute actions: query your database, send emails, update records in your CRM, or generate documents. If you want to delve deeper into how AI agents transform SME operations, we explain it in detail in our article on AI agents for SMEs.

The combination of a local model with agentic capabilities is especially powerful: all decision logic, data access, and action execution occur within your infrastructure. No data leaves your network.

How to Install Gemma 4 in Your Company with Ollama

The easiest way to run Gemma 4 locally is with Ollama, a free tool that simplifies the download and execution of AI models. It works on Mac, Windows, and Linux.

Step 1: Install Ollama

Download Ollama from ollama.com and install it like any other application. You need version 0.20.0 or higher for Gemma 4.

Step 2: Download and Run the Model

Open the terminal and run:

# For the E4B model (recommended to start)
ollama run gemma4

# For the lightest model (mobile, Raspberry Pi)
ollama run gemma4:e2b

# For the MoE model (best quality/speed ratio)
ollama run gemma4:26b

# For the most powerful model
ollama run gemma4:31b

The first time, it will download the model (between 1.5 GB for E2B and 18 GB for 31B Dense). After that, every execution is instant.

Step 3: Integrate it with Your Applications

Ollama exposes a local REST API on port 11434. Any application on your network can send queries to the model without an internet connection:

curl http://localhost:11434/api/generate \
  -d '{"model": "gemma4", "prompt": "Summarize this contract in 5 key points"}'

From there, you can connect it with your existing tools. If your company already uses MCP servers to connect AI with internal data, Gemma 4 is compatible with the protocol and can replace or complement cloud models.

You can also combine Gemma 4 with a RAG system to answer with your company's real information: manuals, policies, product catalogs, customer histories. All processed locally.

Practical Use Cases for SMEs

100% Local Customer Service Chatbot

Set up a chatbot on your website that answers questions about your products and services using Gemma 4 + RAG. Conversations with your clients never leave your server. Monthly API cost: zero.

Document Processing

Invoices, orders, delivery notes, contracts. Gemma 4 can read scanned documents (thanks to its multimodal capability), extract key data, and automatically feed it into your management system. What previously required manual data entry or expensive OCR software.

Internal Knowledge Assistant

Your employees can ask about internal procedures, regulations, incident history, or any company documentation. Instead of searching shared folders for 20 minutes, they get an accurate answer in seconds.

Content and Communication Generation

Emails to clients, commercial proposals, internal reports, translations. Gemma 4 handles over 140 languages, making it especially useful if you work with international markets.

Data and Code Analysis

With an 80% score on LiveCodeBench v6, Gemma 4 is a competent programming assistant. It can help your technical team generate scripts, analyze data, automate repetitive tasks, or review existing code.

What Hardware Do You Need: Real Costs

Let's talk about concrete numbers. These are the realistic options for an SME:

Option 1: Use What You Already Have (0 euros)

If your office has a computer with 8 GB of RAM, you can run Gemma 4 E2B or E4B today. It won't be the most powerful model, but it is more than enough for an internal chatbot or document assistant.

Option 2: Office PC with GPU (800-1,500 euros)

A PC with an NVIDIA RTX 4060 (8 GB VRAM) or higher comfortably runs the E4B and 26B MoE models. This is the option we recommend for most SMEs: good performance, manageable cost, and enough for most use cases.

Option 3: Mac with Apple Silicon (1,500-3,000 euros)

Macs with M2/M3/M4 chips and 16-32 GB of unified memory run Gemma 4 very efficiently, including the 31B Dense model. If you already have a recent Mac in the office, you can probably use the most powerful model without buying anything additional.

Option 4: Dedicated Server with Professional GPU (3,000-6,000 euros)

For companies that need the 31B Dense model with multiple simultaneous users. An NVIDIA RTX 4090 (24 GB) or an A6000 (48 GB) gives you more than enough capacity. This is a one-time investment that eliminates the recurring cost of cloud APIs.

The comparison with the cloud: An enterprise subscription to GPT or Claude costs between 20 and 60 euros per user per month. With 10 employees and an average of 30 euros, that's 3,600 euros per year. A dedicated server with Gemma 4 pays for itself in the first year, and after that, the operating cost is only electricity.

Gemma 4 vs. The Competition: When to Choose It

Not all models are suitable for everything. Here is a practical guide:

• Choose Gemma 4 if data privacy is a priority, if you want to eliminate recurring API costs, if you need multimodal capabilities locally, or if you are looking for the best performance/size ratio in an open model.

• Consider Llama 4 if you need a more mature fine-tuning ecosystem or if you already have Meta infrastructure. Keep in mind that its comparable model (Maverick, 400B+ MoE) requires significantly more resources than Gemma 4 31B for similar results.

• Consider DeepSeek V4 if your primary use case is long-form text generation. In reasoning and programming benchmarks, Gemma 4 widely surpasses it.

• Stay in the cloud (GPT, Claude, Gemini) if you have no privacy requirements, if the usage volume is very low (it's cheaper to pay per use), or if you need the latest generation capabilities that only the largest closed models offer in certain specific domains.

For most SMEs, Gemma 4 covers 80-90% of AI use cases at a fraction of the cost and with total control over the data. You can complement it with cloud models for specific tasks that require maximum capacity.

How We Can Help

At Navel Digital, we help companies implement local AI models like Gemma 4 adapted to their real needs. It's not just about installing a model: we configure the entire ecosystem to function practically in your day-to-day operations.

This includes selecting the appropriate model and hardware for your case, connecting it to your internal data using RAG and MCP, configuring agents that automate real business tasks, and training your team so they can benefit from it from day one.

If your company wants to use artificial intelligence without depending on external services, without recurring costs, and with total control over its data, contact us at no obligation.