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.
> It's only 7GB including the mmproj or 8GB with MTP.
Even more impressively it doesn't have a separate mmproj at all — it is fully integrated, and the vision encoder doesn't speak words into the LLM, as it were —- it is directly integrated into the model's weights.
I have banged on about this model here enough but I really agree that Gemma 4 12B is a candidate for the most impressive LLM of the year. It is remarkable, and I think because it is a small model that isn't apparently excellent for long-context agentic coding, it has been largely ignored.
It is, actually, quite good at coding jobs. (Though its grasp of nuance is a bit weaker. For example, it doesn't know that closures created inside PHP objects have implicit access to the object as $this, and always seems to need reminding.)
If you instead treat it as a prediction of what consumer on-device AI may very soon be able to do, or even as a possible future into a sort of lower-ratio MoE, or the basis of a modest private offline educational LLM model, it's very interesting indeed.
I've learned a lot from it — the fact that it performs so well at such a small size really does help you assess claims made for much larger models.
I do think the release of these models was somewhat fluffed up, and I don't think it helps that the 31B model uses global attention so it underperforms on the kind of older GPUs that are on a lot of desks; it's no better on those than it is on my M1 Max, where other attention schemes seem to be radically better.
Now that tool-calling is mostly fixed, it's well worth playing with them.
Out of the loop here. What did Google and unsloth and llama do to mutilate Gemma? I can understand Google shenanigans but llama and gunsmith is kind of surprising.
Google provided incorrect settings and an imperfect template.
Unsloth modified the template and then finetuned their own version of the model to optimize for some benchmarks as a means of validating quants.
Google and Llama.cpp then adopt template changes by default, so anyone downloading the new model or even using the original model will now automatically be using it incorrectly.
Llama.cpp also uses the same inference setting defaults regardless which version of the model you use and some settings are simply defaults it uses for all models.
Then even if you account for all of these, you have to be using Gemma 4 itself correctly, which many people do not.
All of these little changes and inconsistencies hurt some of the model's original capabilities. Even if you go directly to Google's repo and download the full float 16 weights with the template they have there now, you cannot simply assume you're getting the best results.
And what's the right way to use Gemma? Where can I find the correct template and settings if those aren't the ones provided by Google, Unsloth, and aren't built into llama.cpp? I discarded using Gemma 4 because it got into weird loops when tool calling
Some weeks ago a new official Gemma 4 release was posted that corrected some of the chat template problems. So the official release files on hugging face should be the way to go.
The updated version will handle tool calling better by default, but the reasoning quality is no longer preserved and is mutilated quite badly.
I am very much a beginner to local LLM stuff and I find it incredibly hard to figure out how to run models optimally with the correct settings for my hardware. The number of different variations of the same model and how each quant work is super confusing as well.
When I tried to run llama.cpp directly I was getting max 9tk/s on qwen3.5-9B, then I tried LM Studio with the same model and got 77tk/s. I haven't figured out yet how to get MTP working properly in either.
If you are on Mac, have a look at the Llama-macOS app. They claim sensible settings for the linked model downloads. I'd expect the authors of Llama.cpp and the Huggingface folks to know this stuff.
https://news.ycombinator.com/item?id=49328008
If your package manager / configurator isn’t claude code or codex, you’re wasting time.
Unless, of course, your goal is to actually understand what is going on, regardless of whether that is difficult.
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> It can run 80-100t/s on a laptop
That is a lot, what is your laptop hardware?
One issue I have with Gemma is that they seem to use old architectures that rely on full attention, requiring a lot of RAM for context and quickly degrading speeds as context is filled.
Qwen 3.5+ is much better in that regard with its super efficient context. Even on Macs, speeds take degrade much more slowly.
> One issue I have with Gemma is that they seem to use old architectures that rely on full attention, requiring a lot of RAM for context and quickly degrading speeds as context is filled.
Yes, this is something I hope they will change. Gemma 4 31B is much slower on pre-Blackwell GPUs as a result, which is a bit of a shame for local model experimentation.
> transcribe or translate anything you say
Is it multimodal? How do you do transcription with it?
Gemma 4 E2B, E4B and 12B unified accept audio - here's a recipe using MLX that can use it for transcription: https://simonwillison.net/2026/Apr/12/mlx-audio/
Only up to 30s though, and the larger 26B A4B and 31B models are text and image only.
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