Google's Gemma 4 12B brings encoder-free multimodal AI to laptops
Google says the Apache 2.0 model handles vision and native audio without separate encoders, runs with 16GB of VRAM or unified memory, and nears its 26B MoE model on benchmarks.
By Ryan Merket · Published
Primary source: X
Why it matters
Local multimodal models are becoming a distribution fight. If Google's claimed 16GB memory target holds up in developer use, Gemma 4 12B gives builders another Apache-licensed option for on-device assistants, vision workflows, and private inference without renting large cloud GPUs.
Google DeepMind product managers Olivier Lacombe and Gus Martins introduced Gemma 4 12B, a mid-sized open model designed to run multimodal and agentic workloads locally on laptops.