Gemma 4: powerful, open, and mobile models | Keryc
What is Gemma 4
Gemma 4 is a family of open models built on the same research that powered Gemini 3. The big idea? Offer frontier-grade intelligence you can run on your own hardware: from Android phones to GPUs in laptops and workstations.
Why does that matter to you? Because now you can get advanced capabilities without relying only on closed APIs or constant cloud access. More control, less lock-in.
Google says the Gemma family has already surpassed 400 million downloads and more than 100,000 community-created variants. Gemma 4 ships in four sizes optimized for different uses and resource limits.
Main capabilities
Advanced reasoning: improvements in logic tasks, multi-step planning, and math/instruction benchmarks.
Agentive workflows: native support for function-calling, structured JSON outputs and system instructions, designed to build autonomous agents that talk to APIs and tools.
Offline code generation: turn your local machine into a coding assistant without needing the cloud—handy when you’re on the train or in a café with flaky Wi‑Fi.
Native vision and audio: image and video processing, OCR and chart understanding; E2B and E4B add audio input for speech recognition and comprehension.
Long contexts: context windows up to 128K on edge models and up to 256K on the largest models—great when you need to pass a whole repo or a long report in one shot.
Global coverage: trained on 140+ languages so your app can be inclusive from day one.
Gemma 4 combines cutting-edge performance with the ability to run locally, ideal for offline prototypes and deployments with privacy needs.
Sizes and where to run them
Gemma 4 comes in four configurations aimed at specific scenarios:
E2B (Effective 2B) and E4B (Effective 4B): built for mobile devices and IoT. They keep an effective small footprint during inference to save RAM and battery. They work on hardware like Pixel phones, Raspberry Pi and Jetson modules, and offer multimodality with minimal latency.
26B Mixture of Experts (MoE): designed for low latency and high efficiency; it activates only 3.8B parameters per inference for fast tokens‑per‑second.
31B Dense: maximizes quality and is a solid base for fine‑tuning. Google notes the 31B ranks #3 among open models on the Arena AI leaderboard, while the 26B sits at #6—competing with much larger models.
For developers, the unquantized bfloat16 versions fit on a single NVIDIA H100 80GB GPU; quantized versions can run on consumer GPUs.
License, security and sovereignty
Gemma 4 is released under the Apache 2.0 license. What does that mean for you? Freedom to use, modify and deploy commercially—making digital sovereignty easier: control over data, infrastructure and the model.
Google also says these weights go through the same safety protocols as their proprietary models, aiming at enterprise and government use cases with reliability guarantees.
Ecosystem and real-world use cases
Gemma 4 is already showing up in concrete examples: INSAIT built a Bulgarian model (BgGPT) and at Yale the tool Cell2Sentence-Scale helped explore paths for cancer therapies. That shows both research impact and practical application.
Tools and platforms supported from day one include Hugging Face, llama.cpp, vLLM, Ollama and many popular libraries and runtimes so you can plug Gemma 4 into your existing workflows. Weights are available on Hugging Face, Kaggle and Ollama, and you can try them in Google AI Studio or the AI Edge Gallery.
How to get started today
If you want to experiment quickly: try Gemma 4 on Google AI Studio (31B and 26B MoE) or on the AI Edge Gallery (E4B and E2B).
If you prefer local: download weights from Hugging Face or Ollama and run quantized versions on your consumer GPU.
If you’re thinking mobile: E2B and E4B enable offline prototyping and work with Android tools like ML Kit GenAI Prompt API.
If you’re heading to production: you can scale on Google Cloud (Vertex AI, TPU, GKE) or keep on‑prem deployments for sovereignty.
Final reflection
Gemma 4 is another sign that open AI isn't retreating—it's getting more capable and more practical. For you as a developer, researcher or product builder, that means more choices: powerful models you can run locally, adapt to your language and align with your policies.
Ready to try it on your laptop, phone or server? The entry barrier is lower, and the Apache 2.0 license makes experimentation easier without strings attached.
