> 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, and it's quick enough on my M1 Max to just muck about with.
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.
Well in my case I'm using llama.cpp and the mmproj is required, but I think it is just an extracted part of the original model file. Even with audio, yes it technically supports them natively and they're "encoder-free", but in practice that doesn't mean no translation or processing is required before it goes into the model. It does require much less processing though, which reduces latency.
As for coding, for sure there are many important details that a model needs to know in order to produce correctness and the smaller a model is the more it ends up training out. If there's a task you do consistently enough though, often times you can simply provide a pile of essential context so it has good enough reference to not need the extra training data.
My issue with Gemma 4 is that any task fails to complete after any compaction event. It often ends up in a loop that keeps compacting and showing the same compaction output.
Qwen3.8-27B-IQ4_XS was a massive improvement. It's tasks survive compaction and actually get completed.
I switched to Qwen3.8-27B-UD-Q3_K_XL for better performance and its working just as well.
Gemma4 screwed up a proxmox install I had. I booted to a SystemRescue install and tried to get gemma4 to fix it. It just could not do it and kept having issues where it dropped a linux command into the local powershell because it did not ssh into systemRescue or killed the ssh connection somehow so the text landed on the wrong system.
I told qwen3.8 to investigate fixing the partition. It said information was lost, but displayed enough info that it was easy to tell it was right. I told it to install fresh proxmox and gave a short rundown on settings and partition sizes I wanted. It made a plan and told me I had to manually installed proxmox by booting the iso. I responded with something like "there are other ways to install promox without human interaction so use one of those". That was it. I woke up to the system having booted to a new proxmox install with my previous ssh keys restored and my existing zfs pool already mounted.
I don't see how any model that is limited to a single context window in a single session would be viable for coding. I want something that can manage the entire project and not just individual files or inline suggestions. I need to be able to feed it all the info I would use to make coding decisions and then have it at least make a working project that it can launch and test successfully. You want it to ask as many questions up front to enable continuous work without stopping for human input.
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, and it's quick enough on my M1 Max to just muck about with.
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.