I think that should be the blinking headline - this shows what can be done with consumer hardware.
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.
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.
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.
Then create your own reasoning tests to verify that it is working correctly. You can set a specific seed value to make sure the generation is the same every time, that way you can identify any tokens that are different.
Afterwards, try making small incremental changes to the template and validate your tests each time in order to try to adopt the improvements from the newer templates. If the reasoning quality degrades, undo your changes and try again or test alternative solutions.
Would appreciate any kind of pointer to the latest!
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.
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.
Of course it’s possible the labs just stick with the optimal architecture for large models and GDN is best for smaller models.
Is it multimodal? How do you do transcription with it?
Only up to 30s though, and the larger 26B A4B and 31B models are text and image only.
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.
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.
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.
This isn't an isolated incident, really, I find myself always having these issues with the Gemma series. I'm sure they can do useful things for someone else, but for the things I want to use LLMs for (very small code generation, quick questions, code review) they always seem to disappoint me. I'm sure it's because of the stuff that I do and use, but it's a very consistent red thread with these models for me.
Edit:
The same question for Qwen3.6-35B-A3B produces a pretty concise and correct answer that would be useful to the questioner, without even going to the web. I don't know what Gemma models are trained on, but it's not the stuff that's relevant to me.
Great times!
You can recognize 70% of those (true positive rate) and still have a false negative rate of 30%, while thinking you got 100% of the AI ones!
The problem is that you'd be oblivious to those you don't recognize.
I keep hearing this claim, and yet I keep seeing LLM output which is trivially distinguished from human writing. I really can't understand how this gap persists; but then, there seem to have been at least some people who couldn't sniff out ELIZA, back in the day, too.
That's mostly true for longer LLM output with all the sycophancy / LinkedIn bias thrown in.
Make it casual conversation or comments, and give it instructions on appearing casual, or even better kill the censoring and fixed-prompt (with an open model), and it's orders of magnitude more difficult, unless if you suspect it and try specifically tailored prompts to sniff it.
There's no shortage of people obliviously discussing with AI bots in comment sections.
People, many of them at least, cannot make this distinction anymore. You can, I can, but people as a whole are having problems with that.
Even this (assuming it's even true) will likely not be true in some near-term future.
>continuously claiming this or that is a bot
I see it as a contemporary form of religious thinking. Like (say) pilgrims seeing blood on a statue of the virgin, plenty of people are now seeing the hand of AI in everything they read. If you want to see something hard enough, it tends to become magically visible.
Most users aren't very critical of the output. They just want a sycophantic ear, and 4o was perfect for that task. It's not _good_ but there is high demand for it.
From the abstract: "When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time"
That's because they're trained that way. If you trained a modern frontier LLM with the explicit goal of passing the Turing test, it would have no difficulty doing so.
To me it just means they can brilliantly fake human conversation - the original design goal of Large Language Models.
It's really easy to tell if you're talking to an LLM if you ask a question that requires actually knowing things, not going for the first search result of a tool call or whatever most popular answer was embedded in the weights.
For this reason even the most sophisticated models still require system prompts, skills and all that other crap.
That's exactly the criteria we used to assume for over half a century for it finally being intelligent: the Turing Test.
And what does "brilliantly fake human conversation" even mean if not some kind of intelligence? It's like saying "He is not good at math, he just brilliantly proves theorems".
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.
As it is, it seems the improvements are about making the hardware cheaper (as in capex, not opex).
This is just feels from me from what I hear on the news and see on the products though.
Especially when chips are becoming cheaper (in the capex sense), then you can afford to only run them when power is cheap.
Btw, from where do you take the notion that performance per Watt ain't increasing? We are also still using what's more or less general purpose GPU hardware; we could get a lot further if we were willing to specialise more. Which would be the natural avenue to explore, if progress in general purpose hardware slows down. Google is already looking.
GPUs have been getting physically bigger with huge heatsinks and fans to support those bigger dies power consumption. Just compare the TDPs:
2020 RTX 3090: 350W
2022 RTX 4090: 450W
2025 RTX 5090: 575W
Bigger dies means lower capex of course, but the similar opex (maybe slightly lower as there is less physical hardware to maintain).
I seen some specialized hardware like google's TPUs. Not sure how they compare on performance per watt with GPUs though. Regardless the manufacturing processes are still the same (EUV) which is the thing that hasn't been improving. A fully optimized specialized hardware can at most deliver a single-time linear improvement (that could be very significant, for example 30% is still huge of course) and then little compared to normal GPUs.
I don't think renewable power generation is going to massively reduce costs for data centers, especially considering power transmission hasn't meaningfully reduced in cost. If anything the only thing that I think will have significant impact for data centers would be dedicated nuclear power plants physically located right next to the data center.
In fact I expect power generation to get more expensive as demand can increase faster than supply can be established. I imagine setting up new solar farms and transmission lines to be significantly harder (as in, takes longer time due to approvals and so on) than new data centers (which requires a single large location and I assume less approvals).
I don't think the main use case is for a human to directly interact with the raw token stream. You probably want reasoning and you want the thing to be able to program on its own. That uses way more tokens than you can read.
You can also look at what's been happening in mobile and especially with Apple's integrated processors. They are more power constrained, so people worried more about power there.
I think raw flops per watt come mostly from fab process, not architecture. This was my original point, fab process is not getting better at a linear (much less exponential) scale anymore.
I think really good efficiency is possible right now, but the GPU makers don't want to make their consumer GPUs too good for AI - if the cards were more efficient, it'd be much easier to run multiple - while data center ones have a bunch of additional power overheads.
As a recent example, my /boot partition kept filling up. ChatGPT walked me through the root cause (leftover kernels after updates), mitigations (deleting older kernels), and future prevention (installing unattended-updates and enabling its config file).
The next 10 years of computing is going to be VERY interesting. May be not just computing but everything, an even wider reach and disruption than iPhone.
Unlike iPhone / Smartphone which I thought was obvious, a computer or web browser in your pocket that basically extends the internet to anywhere. Local Model AI in our computer or pocket that controls other things opens seemingly unlimited possibilities.
I initially thought it was going to be 15-20 years time frame, with perhaps a bubble burst in between. But development of both software and hardware is accelerating.
I ask because Claude is fun for rewriting abandoned code and I am not a proper developer so it's been great for me. Claude refuses to answer questions about science and medicine that stray outside of the officially supported narratives of the AMA and I have issues that have surpassed anything a doctor can do so I am entirely on my own. Will the self hosted models answer such questions or will it also try to put walls or bumper guards around topics?
I personally like the https://huggingface.co/HauhauCS version of Qwen 27B from a purely subjective point of view as a user, but that particular one has come under criticism for reasons that don't necessarily affect its quality or usability.
Some of the larger models have also undergone similar treatment, but it's less common.
For the most part, open base models are corporate releases with somewhat similar guardrails to commercial hosted models (not quite as complete, because hosted models tend to have a combination of trained and external guardrails applied); but no one is monitoring and trying to terminate your account for using jailbreak prompts, and there are often community finetunes available that (among other things) weaken the trained-in guardrails.
Of course, even if the model does answer, it may nto answer according to the particular worldview that produces hostility to the “narratives of the AMA”.
The over reasoning that Simon Willison highlights here is a real issue though. I've observed it with some of the OpenAI models as well. They are prone to overthinking and overengineering things.
What I would love is models that figure out their own appropriate reasoning effort given a task. I'm spending too much brain cycles worrying on what model speed, reasoning, and quality settings to pick. It's not just a cost concern it's also a time concern. Wasting a lot of time for simple UI tweaks because the model is set to high or ultra or whatever is counter productive. The last few iterations of frontier models seem to emphasize benchmarks and reasoning effort.
But of course the day to day reality of many developers is that they are trying to solve relatively simple problems compared to e.g. proving some so far unproven theorems, solving some Nobel prize level problems, etc. I'd love my tools to start making sane choices based on what I ask rather than defaulting to "boil the oceans". These tools need some kind of Auto select. Mostly Ultra is overkill and a waste of time and resources. And of course with local models, keeping simple things local is a nice option.
It's nice to have Sol Ultra extra fast as an option in my back pocket. But it's complete overkill 99% of the time. And it's not like most users make good choices here or are even capable of making good, informed choices. The models are more intelligent than the tool UX. Arguably, a local model of very modest size might be able to do better for this specific choice.
If you want a better experience, maybe wait for either a moe model (like 3.6 35b A3) or a model with less parameters (like 9b). Qwen has been releasing those in the past, so maybe we’ll have them for 3.8 too.
For example make an essay about something where you don't actively engage with the LLM after the initial prompt. So mostly one-shot prompts.
> It feels pretty slow on both the M5 Mac and the DGX Spark.
Or the "<|think_xhigh|> | <|think_low|> | <|think_off|>" tags: apart from this template detail, it is not immediately clear if reasoning_effort is deterministic (API) or is prompt engineering.
Probably stuck in prompt processing which is compute bound especially for iGPUs.
You've mentioned 3.5 - but it's actually the same model the only differences are training and implicit MTP support (affects prompt processing - can be disabled)
Besides, Macbooks with 32GB RAM is consumer hardware, just maybe on the higher end.
for inference the compute is the last thing we need more of.
memory bandwidth is the numebr one blocker, after that the inefficiencies that where introduced with MoE models (and all new large models are made that way)
Here is a quick read: https://news.ycombinator.com/item?id=49324600
As soon as I switch to a model that doesn't fully fit into vram it tanks to <10tk/s which makes it unusable for me for most tasks.
I've gone in (too many) details last night with the calcs: https://news.ycombinator.com/item?id=49324600