Gemma 4 by Google: Specs, Benchmarks, and How to Run It Locally Best overall.

Since the first Gemma models launched, developers around the world have downloaded them over 400 million times and created more than 100,000 custom variants. That level of adoption tells you something important: people wanted open models that were practical, fast, and deployable beyond the cloud.

Gemma 4 is Google’s answer to that demand. It brings frontier-level intelligence into model families that can run on everything from a Raspberry Pi to a data-centre GPU, while remaining open enough for developers and businesses to actually build with.

Gemma 4 is Google’s newest family of open AI models, released on April 2, 2026. The models are built from the same research foundations behind Gemini 3, but unlike Google’s proprietary offerings, Gemma 4 is released openly for the community to use, modify, and deploy.

Google has published Gemma 4 under the Apache 2.0 license, which means developers and companies can use it commercially without restrictive licensing headaches. Whether you need a small model for a mobile app or a larger model for research and advanced tooling, the family has multiple sizes built for different hardware profiles.

For teams working on AI-powered product development, Gemma 4’s function calling and structured output support opens the door to real agentic systems. Its long context support also makes it practical for data-heavy analytics workflows and technical document processing.

On the Arena AI text leaderboard, the 31B Dense model ranks #3 among open models with an estimated ELO of 1452. The 26B MoE model ranks #6 at 1441, while only activating roughly 4 billion parameters during inference. That level of efficiency is why Google talks about “intelligence per parameter.”

Early reports also show encouraging local inference speeds: the 31B model can exceed 10 tokens per second on local setups, while smaller models like E2B and E4B can move significantly faster.

Download Model Weights

The Easiest Way: Install with Ollama

If you want the fastest route from zero to a working local model, Ollama is the easiest option. Install Ollama, then run one command.

ollama run gemma4

To choose a specific model size, use these tags:

ollama run gemma4:e2b
ollama run gemma4:e4b
ollama run gemma4:26b
ollama run gemma4:31b

For Developers: Run with Python

If you prefer building directly with Hugging Face Transformers, install the basic libraries and load a Gemma 4 model ID like google/gemma-4-31B-it.

pip install transformers torch

Try It Without Installing Anything

You can explore the larger Gemma 4 models in Google AI Studio. For smaller on-device variants, Google’s AI Edge resources are the best place to start.

Other Compatible Tools

Gemma 4 has day-one support across tools like vLLM, llama.cpp, LM Studio, MLX, NVIDIA NIM, NeMo, Unsloth, Keras, Docker, SGLang, and Baseten, which makes it much easier to fit into existing AI workflows.

For teams looking to deploy AI at scale on reliable cloud infrastructure, Gemma 4’s compatibility with Google Cloud services helps make the path from prototype to production much cleaner. And if you need a ready enterprise automation layer, Cygnus Alpha can help integrate open models into real business workflows.

Yes. The E2B and E4B models were specifically built for on-device use. They can run offline on smartphones, Raspberry Pi boards, and embedded hardware like NVIDIA Jetson devices. In 4-bit mode, the smaller models can fit into roughly 5 GB RAM, which makes them feasible for modern mobile and edge scenarios.

That matters because it pushes AI features like local summarization, on-device assistants, private image analysis, and edge-side agentic flows into practical reach for product teams.

Gemma 4 represents a real shift in open AI. Models that previously needed large-scale infrastructure are now viable on smaller devices, local machines, and more affordable hardware profiles. That changes how teams can think about privacy, cost, latency, and product design.

The bigger takeaway is not just that Gemma 4 is good. It is that open AI is increasingly becoming practical, competitive, and deployable in real-world products.

At Auriga IT, we help businesses turn AI breakthroughs like Gemma 4 into working products and scalable systems. From building intelligent applications to deploying them on strong infrastructure, we work with the latest tools so teams can move faster with less uncertainty.