Gemma 4 Explained: What It Is, What It Can Do, And How To Use It Right Now

Google has unveiled Gemma 4, its most capable family of open artificial intelligence models to date, in a move that significantly lowers the barrier to building powerful AI applications for developers, researchers, and businesses around the world.

Developed by Google DeepMind and described as purpose-built for advanced reasoning and complex automated workflows, Gemma 4 delivers what the company calls an unprecedented level of intelligence per parameter. According to official Google blog, it is released under the commercially permissive Apache 2.0 licence, meaning anyone can use, modify, and build upon it for free, including for commercial purposes.

What Makes Gemma 4 Different

Gemma 4 is built on the same research and technology that underpins Gemini 3, Google's flagship AI model, making it the most capable open model the company has ever released. Since the first generation of Gemma launched, developers have downloaded the models over 400 million times, producing more than 100,000 variants in what Google calls the "Gemmaverse".

The new release comes in four sizes, designed to suit everything from a mobile phone to a large-scale server. The smaller edge models, with effective footprints of two billion and four billion parameters during inference, are engineered to preserve RAM and battery life, running completely offline with near-zero latency on devices such as phones, Raspberry Pi, and NVIDIA Jetson Orin Nano.

According to Google for Developers, Gemma 4 goes beyond traditional chatbot functionality, enabling multi-step planning, autonomous action, offline code generation, and audio-visual processing, all without requiring specialised fine-tuning. It also supports more than 140 languages, making it suitable for a genuinely global developer audience.

Running on Your Phone

According to Android Developers Blog, one of the most striking aspects of Gemma 4 is how much it can do directly on a mobile device. Android developers can access the model today through the AICore Developer Preview, with Google confirming that code written for Gemma 4 will automatically work on Gemini Nano 4-enabled devices arriving later this year. The models run on the latest AI accelerators from Google, MediaTek, and Qualcomm Technologies.

The Google AI Edge Gallery app, available on both Android and iOS, allows developers to experiment with AI capabilities running entirely on-device, including a new feature called Agent Skills that enables multi-step, autonomous workflows without any internet connection.

How You Can Use Gemma 4

Whether you are a developer, student, researcher, or business, there are several straightforward ways to get started. The larger 31 billion and 26 billion parameter models are accessible through Google AI Studio, while the smaller edge models can be explored via the Google AI Edge Gallery app. Developers can download model weights directly from Hugging Face, Kaggle, or Ollama, and can train or customise Gemma 4 using platforms such as Google Colab, Vertex AI, or even a gaming GPU.

Gemma 4 has day-one support for a wide range of popular tools including Hugging Face Transformers, vLLM, llama.cpp, MLX, Ollama, NVIDIA NIM, LM Studio, and Keras, giving developers the flexibility to integrate it into existing workflows with minimal friction.

For desktop users, Gemma 4 runs on Windows, Linux, and macOS, while a new Python package and command-line tool make it easy to experiment in the console or power Python-based pipelines for connected devices and robotics applications on Raspberry Pi 5.

The Competitive Picture

Not everyone is hailing Gemma 4 as a complete breakthrough. Some analysts have noted that while Gemma 4 is a significant step forward for Google's open model family, it faces stiff competition from Chinese open-source rivals in certain benchmarks. The AI landscape remains intensely competitive, with Meta's Llama series and China's DeepSeek models also vying for developer attention.

What Experts Are Saying

Google CEO Sundar Pichai highlighted the model's intelligence-per-parameter efficiency as a defining characteristic, while the DeepMind team described it as a direct response to feedback from the developer community about what they needed to push the boundaries of AI further.

The open-source nature of the release, combined with its commercial licence, is being seen as a deliberate challenge to more closed AI ecosystems and a signal that Google intends to compete aggressively for developer loyalty.