Google Deepmind's Gemma 4 12B squeezes multimodal AI onto a laptop with just 16 GB of RAM
Quick Answer
Google Deepmind's Gemma 4 12B is an open-source multimodal AI model that efficiently runs on laptops with just 16 GB of RAM, achieving performance close to the larger 26B model in benchmarks.
Quick Take
It is available under an Apache 2.0 license for commercial use, making advanced AI accessible for personal and business applications.
Key Points
- Gemma 4 12B processes text, images, and audio natively.
- Runs efficiently on laptops with only 16 GB of RAM.
- Performance benchmarks nearly match the 26B model.
- Available under an Apache 2.0 license for commercial use.
- Makes advanced AI technology accessible to a wider audience.
📖 Reader Mode
~1 min readGoogle Deepmind has released Gemma 4 12B, an open AI model that brings multimodal capabilities to everyday laptops. It processes text, images, and audio natively without separate encoders, cutting processing time, memory use, and latency, according to Google. The model runs locally with just 16 GB of RAM and nearly matches the 26B model—twice its size—across benchmarks, Google says. It's also the first mid-sized Gemma model with native audio processing.
Gemma 4 12B handles speech recognition, code generation, and video analysis. Per the Developer Guide, it can parse multi-minute video clips by analyzing frames and audio together. In one demo, it chewed through a five-minute Google I/O keynote clip: 313 frames at one per second, plus audio.
The model is available on Hugging Face, Ollama, LM Studio, and other platforms, licensed under Apache 2.0 for commercial use.
— Originally published at the-decoder.com
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from The Decoder
See more →
An AI model programmed nonstop for 19 days on a single MirrorCode task that cost $2,600 to run
Epoch AI's MirrorCode benchmark reveals Claude Opus 4.7 as the leader with a 56% solve rate, reconstructing a 16,000-line toolkit in 14 hours. Despite this, all models tested struggle with the most complex tasks, highlighting limitations in current AI capabilities. The single task consumed $2,600 over 19 days, raising questions about cost-effectiveness in AI development.
