Google Gemma 4 Unveiled: Now More Open and More Powerful!

The importance of open-source and "open-weight" models in the AI world grows by the day. To strengthen its position in this race, Google has officially announced its next-generation model family, Gemma 4. Drawing on Gemini 3-based technologies, this new family stands out not only for its technical capabilities but also for a radical change on the licensing side.

Gemma 4, with which Google claims to have made a significant leap in "intelligence per parameter," opens a whole new era for developers with its ability to run on local hardware and its broad language support.

Four Different Models: Power for Every Need

The Gemma 4 family consists of four separate models targeting different hardware levels and use cases. With this variety, Google is aiming at both high-performance cloud-based systems and the smartphones in our pockets.

1. "Effective" Models (E2B and E4B)

Developed for devices with more limited resources, the 2 billion (E2B) and 4 billion (E4B) parameter models are optimized especially for smartphones, IoT devices and embedded systems. The biggest promise of these models is low latency and minimal energy consumption.

2. Mixture of Experts (26B MoE)

Offered for powerful systems, the 26 billion parameter MoE model combines efficiency and speed. By actively using only 3.8 billion parameters during inference, it reaches much higher token generation speeds than models of a similar size.

3. Dense Model (31B)

The largest and most powerful member of the family, the 31 billion parameter Dense model focuses on maximum accuracy and quality rather than speed. It delivers performance close to Gemini 3 level in areas such as complex reasoning, mathematics and advanced code generation.

According to data shared by Google, the 31B model ranks third on the Arena AI leaderboard and the 26B model sixth, managing to outperform models 20 times their size.

Running Locally Comes First: The "Zero Latency" Goal

One of the most critical features of Gemma 4 is that its ability to run on local hardware has been pushed to the highest level. Thanks to joint work with Qualcomm and MediaTek, Google optimized the E2B and E4B models for smartphones and popular hardware such as the Raspberry Pi and Jetson Nano.

Thanks to these optimizations:

• "Near-zero latency" was achieved on the small models.
• They consume less memory (RAM) and battery than the previous generation.
• While the large models (31B) can run at full precision (bfloat16) on a single 80GB Nvidia H100 GPU, when quantized they can fit on consumer-grade GPUs (such as the RTX series).

Multimodal Capabilities and Support for 140+ Languages

Gemma 4 goes far beyond being a text-only model. The entire family is equipped with multimodal capabilities:

• Image and Video Analysis: Strong performance in areas such as OCR (optical character recognition) and chart analysis.
• Audio and Speech Recognition: Audio input is supported, especially on the "Edge" (small) models.
• Large Context Window: With 128K tokens of context on the Edge models and 256K tokens on the large models, analyzing long documents is now much easier.

In addition, the models were trained on more than 140 languages, offering global accessibility and paving the way for local projects.

Apache 2.0 License: Real Freedom

For developers, perhaps the most exciting news came from the legal side. Google has abandoned the controversial custom license model used in previous Gemma versions and moved entirely to the Apache 2.0 license.

This change means:

• The models can be used in commercial projects without any restrictions.
• Developers can freely modify the models, take parts of them and redistribute them.
• You have the right to deploy them freely both in the cloud and on your own infrastructure.

Ready for Agentic Workflows

In line with modern AI trends, Gemma 4 has been made fully compatible with "agentic" workflows through features such as function calling and structured JSON output. Its ability to generate code without an internet connection is revolutionary for closed-loop (offline) development environments.

Conclusion: Where Can You Download Gemma 4?

Gemma 4 models are now available on the most popular platforms in the ecosystem:

• They can be downloaded right away from Hugging Face, Kaggle and Ollama.
• They can be tested in the browser via Google AI Studio and AI Edge Gallery.
• For professional needs, they can be run on ready-made scalable infrastructure via Google Cloud (Vertex AI).

Google's Gemma 4 move is the clearest sign yet that open-source AI models have closed the performance gap with closed models. A model family that delivers such high performance on both mobile and desktop hardware being released under the Apache 2.0 license looks set to be one of the biggest events driving AI innovation in 2026.