“The fact that a 17GB file can do all of this stuff on my home machines is a miracle. Once again, I’m delighted and amazed at how much progress local models have made this year.”

I think that should be the blinking headline - this shows what can be done with consumer hardware.

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.

Like the old proverb: "The marvel is not that the bear dances well, but that the bear dances at all."

Full agree. I until very recently thought AI tools of today were limited to prohibitively expensive high end hardware hosted in data centers.

I was surprised and amazed to get "decent" (with the expectations set right / low) coding performance out of Qwen3.5-9B on a decidedly medium end Radeon 9070 paired with a 5700x3d and 32GB of DDR4 RAM.

We can finally reason with and "talk" to our hardware.

In this kind of moment, I really wished hardware manufacturing and demand situation is in much state. Imagine this can be accessible by everyday people with only 6 months hardware market gap. The societal impact would be much bigger.

I wish we could have better hardware and I think the tech is there for a few years already.

I've gone in (too many) details last night with the calcs: https://news.ycombinator.com/item?id=49324600

Tried yesterday on my own laptop (a UltraCore 7 255H without dedicated GPU,with 32 GB RAM), it wasn't even starting thinking, even on a small context window (65k)

his 128gb Ram laptop is quite extreme

I thought the same thing, and I generally do a lot of animation in my work, and the results in motion graphics with Qwen are impressive, I really fell in love with it