Given I know people running gemma3 on local devices for over almost a month now this is either a very slow news day or evidence of finger missing the pulse...
https://blog.google/technology/developers/gemma-3/
> Last month, we launched Gemma 3, our latest generation of open models. Delivering state-of-the-art performance, Gemma 3 quickly established itself as a leading model capable of running on a single high-end GPU like the NVIDIA H100 using its native BFloat16 (BF16) precision.
> To make Gemma 3 even more accessible, we are announcing new versions optimized with Quantization-Aware Training (QAT) that dramatically reduces memory requirements while maintaining high quality.
The thing that's new, and that is clearly resonating with people, is the "To make Gemma 3 even more accessible..." bit.
As I've said in my lectures on how to perform 1bit training of QAT systems to build classifiers...
"An iteration on a theme".
Once the network design is proven to work yes it's an impressive technical achievement, but as I've said given I've known people in multiple research institutes and companies using Gemma3 for a month mostly saying they're surprised it's not getting noticed...
This is just enabling more users but the none QAT version will almost always perform better...