Google's new AI model, 'Gemma 4 26B-A4B,' is an innovation that achieves the same performance as previous models in significantly less time and with fewer resources by using a 'Mixture of Experts (MoE)' approach that only activates the necessary parts.
Imagine this. You are in a massive library filled with millions of books, trying to find very specific information. Previous AI models would have had to read every single book in the library from cover to cover to find just one. It took a long time, and a massive amount of energy was wasted. But what if you had 128 expert librarians waiting who know the information best, and every time you asked a question, only 9 of them stepped out to give you the answer immediately?
Google DeepMind’s new open AI model, ‘Gemma 4 26B-A4B,’ works in exactly this efficient way. Source: Google Gemma 4 Introduction, Source: AI-Stat News
Why Is This Important?
Until now, the high-performance AI we used was usually very large. To get smarter, models had to be scaled up, which meant massive electricity bills and expensive supercomputer equipment. But Gemma 4 is different. It has perfectly proven that a ‘smaller yet smarter’ AI is possible. Source: Hugging Face Blog
This model is light and efficient enough to run on a personal laptop without a massive server. Source: vc.ru article We are one step closer to an era where the smartphones and laptops we use every day can transform into powerful personal assistants. In particular, we can expect much faster responses in tasks requiring coding, data analysis, and complex logical reasoning. Source: Ollama Library, Source: LM Studio Model Info
Easy to Understand: Mixture of Experts (MoE) Structure
The core technology of Gemma 4 is ‘Mixture of Experts (MoE).’ Let’s use a very simple analogy for this difficult term.
Instead of hiring one person who is moderately good at everything, we built a team of 128 specialized experts, including math experts, language experts, coding experts, and history experts. Source: AI-Stat News Now, when a user asks, “Write me some Python code,” the math experts or cooking experts just rest, and only 8 coding-related experts and 1 general coordinator expert are activated immediately. Source: AI-Stat News
Although the total size of the model is large, it only uses a tiny portion (4 billion parameters) to process data at any given moment, so the model moves lightly while maintaining quality on par with a giant 27 billion parameter model. Source: Google Gemma 4 Introduction In other words, it ‘maintains its intelligence while drastically reducing the thought process.’
Current Status
Gemma 4 currently possesses ‘Multimodal’ capabilities—understanding and reacting to various forms of data simultaneously, such as text, visual information, and audio. Source: Hugging Face Blog, Source: LM Studio Model Info
The actual performance test results are astonishing. While other models struggled with complex tasks, Gemma 4 completed them much faster. Some comparison models took about 69% longer than Gemma 4 to complete tasks. Source: Habr article This means not only is it fast, but it requires much less energy to achieve the same performance. Source: BenchLM Model Info
What’s Next?
Moving forward, the need to rely 100% on massive cloud servers to use AI will gradually decrease. High-efficiency models like Gemma 4 will operate faster and more privately within the smart devices we use right now, protecting personal data. Source: Ollama Library
Additionally, developers around the world can use these efficient models as a foundation to easily fine-tune them (train them for specific purposes) to create their own professional, customized AI assistants. Source: Unsloth Documentation An era is opening where the small device in our hands processes all the world’s knowledge faster and more precisely.
MindTickleBytes AI Reporter’s Perspective
The era of simply increasing the size of complex models is over. ‘Precision intelligence,’ where only the necessary intelligence is efficiently retrieved and used, will now be the core competitive edge in technology.
References
- Welcome Gemma 4: Frontier multimodal intelligence on device
- Gemma 4 — Google DeepMind
- Gemma 4 31B vs 26B-A4B: 16GB VRAM Local Test - YouTube
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[Gemma 4 Fine-tuning Guide Unsloth Documentation](https://unsloth.ai/docs/models/gemma-4/train) -
[Gemma 4 26B-A4B (free) - API Pricing & Benchmarks OpenRouter](https://openrouter.ai/google/gemma-4-26b-a4b-it:free) - gemma4:26b-a4b
- Тест Qwen 3.5 9b, Gemma 4 12b и Bonsai 27b на легенде… / Хабр
- Google выпустил Gemma 4 - и она работает на моём… — AI на vc.ru
- Gemma 4 от Google: линейка от 2,3B до 31B, MoE… — Veruna AI
- Gemma 4 локально: установка через Ollama за 5 минут
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[Gemma 4 vs Qwen 3.5: кто теперь лидер открытых моделей AI-Stat](https://www.ai-stat.ru/news/2026-04-05-gemma-4-vs-qwen-35) - Gemma 4
- Gemma 4 26B-A4B Benchmarks & Context (October 2026)
- Because it always runs the entire model
- Because it uses only a subset of its 128 experts
- Because it increased parameters by 100 times compared to existing models
- Supports multimodal inputs like text, vision, and audio
- Designed to run on personal computers
- Absolutely does not work without an internet connection
- About 69% longer
- About 39% longer
- More than twice as long