I ran a frontier-quality AI model on my phone in airplane mode. No internet, no cloud, no API. That didn't exist a week ago.
Gemma 4 is a family of four open-source models under Apache 2 license (fully commercial, no restrictions). The smallest one runs on your phone in under 1.5GB RAM. The 26B model only uses 4B active parameters but ranks #6 among all open-source models. The 31B dense is #3 open-source overall โ ahead of Llama, DeepSeek, and Qwen.
The math benchmark jump is insane: 20.8% โ 89.2% in one generation. A 4.3x improvement. These models handle text, images, audio, and video natively with built-in function calling and step-by-step reasoning.
Install with Ollama in 6 minutes: ollama pull gemma4:26b and you're running locally. Zero API costs, zero data leaving your machine, zero vendor lock-in.
Limitations worth knowing: edge models struggle with complex multi-step reasoning, quantized versions lose quality, and the ecosystem is brand new (4 days old vs Llama's years of tooling). For hard tasks, closed models are still ahead โ but the gap is shrinking fast.
What would you run locally first? ๐