For me that moment was Gemma 4 12B QAT. You're not suddenly going to start throwing your hardest programming problems at Gemma 4 12B QAT, it is still 15B parameters less. It's more that, aside from pelican art which isn't what local models are for, I didn't see anything on Simon's post that it couldn't assist with or largely succeed at.

It can run 80-100t/s on a laptop, can understand images natively and do bounding boxes, read tiny text, understands audio natively as well and can transcribe or translate anything you say, can do accurate long context retrieval with pretty large context windows, tool calling, excellent reasoning and is very token efficient.

It's only 7GB including the mmproj or 8GB with MTP. The Qwen 3.8 27B model Simon was using is ~18GB with MTP+mmproj, rather than 17GB alone. The point is not really that you compare these models directly, but that Gemma 4 12B QAT was really a special moment in model releases deserving of a similar reaction relative to its size, but was mutilated by Google themselves, Unsloth and Llama.cpp.

The overall appreciation I think we're seeing this year in particular is that people are easily surprised when multiple things are improving simultaneously which produce seemingly exponential changes. It isn't just that models are getting smaller, or that reasoning is getting better, or that speculative decoding is becoming mainstream, or that models can understand audio and images better now, or that they can reliably call tools which expands their capabilities, or that context windows are getting larger, or that accurate retrieval is improved, or that.... and so on. It's all of them narrowing in at once that is starting to make local models incredible and truly useful for far more use cases on the existing hardware people already have.