This stack uses Ollama with Gemma 4 QAT to run a 12B model on a 10GB VRAM laptop GPU. The latest...
This stack uses Ollama with Gemma 4 QAT to run a 12B model on a 10GB VRAM laptop GPU. The latest Gemma 4 QAT checkpoints reduce memory usage and enable compact local inference.
What you get
Local Gemma 4 12B inference on 10GB VRAM hardware
QAT compression that fits the model into ~6.7 GB VRAM
A laptop-friendly private AI stack for writing, notes, and prompts
We’re releasing Gemma 4 quantization-aware training checkpoints, reducing memory requirements and improving on-device performance.
Submitted for the Gemma 4 Challenge: Write About Gemma 4 A 2B model running entirely on my local...
Google DeepMind releases Gemma 4 QAT checkpoints; Q4_0 and a new mobile format cut on-device memory sharply.
Google shipped Gemma 4 12B this week — a model that packs near-26B performance into something that...
Google Deepmind's Gemma 4 12B is an open-source model that processes text, images, and audio natively and runs on laptops with…
Google introduces the efficient Gemma 4 12B AI model, designed to operate seamlessly on laptops with just 16GB of RAM, catering…
