Comment by moontear
21 hours ago
Is „frontier“ overused? I thought frontier models were the best-of-the-best such as Fable right now. I assume you can’t host these models yourself since you would need many GB of RAM and expensive GPU of is my thinking of „frontier models“ wrong?
I was confused too, but I believe that it refers to the Pareto Frontier: the best set of solutions to a multi-objective problem.
Look it up, it’s a bit difficult to explain concisely in words but it is intuitive visually.
If we are thinking of intelligence and price, a model will be in the Pareto Frontier if there’s no cheaper model of the same or higher intelligence. Or if there’s no more intelligent model for that price or lower.
EDIT: See this chart from Artificial Analysis: https://artificialanalysis.ai/#intelligence-comparison-tabs
So for example DeepSeek V4 Pro can be considered a frontier model because there's no cheaper model that is as intelligent.
For any solution in the Pareto Frontier, there no "no-brainer" alternative, in the sense that there's no other option that is better in some way without giving up something else. It's the best of its "weight-class".
I think this is one of the easiest to understand.
https://arena.ai/leaderboard/text/pareto
That’s a good visualization, although I am a bit mistrustful of Arena’s scores. It does get around the fact that models are getting trained for the benchmarks, but the methodology of letting random people compare outputs side-by-side is a very shallow judgement method in my opinion.
EDIT: Indeed looking at the overall rankings for text again, the list is rather strange, a lot more about writing style than intelligence.
Very interesting rabbit hole for today. Thanks for mentioning Pareto frontier!
The subtitle on their website is: "Nativ puts frontier intelligence on your desk".
Also, the title is "Run AI models locally on your Mac," not "Run frontier open models locally on your Mac."
That’s not what frontier means.
There are two frontier labs - OpenAI and Anthropic, and maybe 4 frontier models currently. That term refers to model capacity and in general represents a model’s capability relative to the rest of the industry irrespective of price. That’s why even the Qwen announcement carries language about it being ‘near frontier’. Same with Kimi.
Whether a model is frontier or not has everything to do with capability and nothing to do with price.
I don't know that I agree with this specific use of frontier because it is confusable as you say.
But (off on a tangent) I do think that there are multiple frontiers generally — and I also think the open weights, small local model frontier is by far the most important and exciting one.
I keep mucking about with what Gemma 4 12B can do and every time I do I find myself thinking that all the energies in the AI world are going in entirely the wrong direction, because it is small, clever, efficient and remarkable.
If all of that research money were to be spent on improving AI models that fit inside a 16GB RAM machine with unified memory, I think really important progress could be made.
I enjoy using the Qwen 3.6 models (and BottleCap's new fine tune of the 27B) but the small Gemma 4 models are impressive in a way that I think is going quite unreported.
So while I don't think this website should use the word "frontier" here, even referring to Qwen 3.6 27B which is weirdly close, I think it could.
I’ve got a 32 gig m1 MacBook Pro. how would I go about trying Gemma like you mentioned? Would it run at an acceptable speed, and what could I do? Coding?
“docker run open-webui:ollama” is a simple way to start.
Don’t expect much for coding. But it’s great for general knowledge, rubber ducking, image classification…
1 reply →
On an M1 Max I have been using either Unsloth Studio (which is basically a web app) or LM Studio (nicer app on the Mac). You can use the Google AI Edge Gallery to play with the smaller Gemma models (but at the moment the QAT variants don't seem to be there unless I am missing something).
I think it's likely the 26B QAT model won't fit in your machine — you may be able to fit one of the UD_Q3 or UD_Q2 variants but whether you'll be able to run other things you want at the same time, I don't know.
(The QAT models are "quantization aware training" — AIUI the model weights have been assigned during training to survive four-bit quantization with less loss.)
So what I would recommend trying is this model:
https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF
Try UD Q4_K_M maybe.
(I don't think the M1 really gets much benefit from MLX, in case you were wondering, though I could be wrong)
My interest in this model is largely to really get to grips with what small models can actually do, especially with tool calling, because I think it helps comprehend what the value proposition of the cloud models is.
I have been very surprised by the quality and clarity of its answers. It's also helped me understand that much of a typical harness system prompt is likely to be unnecessary now; Gemma 4 seems to be pretty sensible out of the box.
You're absolutely not going to be able to get it to go off and build whole apps from a long prompt; it is not that good, but it does tool calling and thinking, and you should be able to explore pointing a coding harness at it if you turn on LM Studio or Unsloth Studio's API server. You could also use the Llama system tray app (formerly LlamaBarn) or just use llama-server from the llama.cpp distribution.
Probably Pi is going to be a better harness because it can have a minimal system prompt, though I've not tested it with Pi myself.
It seems to know PHP and SQL to a fairly decent depth (and I suspect JS and Python). It also has a unified vision model (it doesn't need a separate mmproj sidecar thingy) that is fairly fast, and it is quite impressive at image analysis.
So you could probably use it to generate image descriptions and tags, summarise text, generate wordpress snippets, that sort of thing.
It can capably answer questions like "Can you characterise this image and suggest further similar images I might like?" — I am currently using this to provoke me to take photos again.
Have a play with the E4B edge model, too — again, much more interesting than I expected.
The frontier is a multidimensional space defined by the “best” combination of traits a model can have in multiple dimensions: parameter size [smaller is better], various task metrics, and relative token generation speed on like hardware [faster is better], and active memory requirements [smaller is better] are all possible dimensions, and a model on the frontier of the current options space is one where getting better on one of those measures cannot be done without getting worse on at least one of the others.
The frontier is a curve. https://en.wikipedia.org/wiki/Pareto_front
That is one sense, but it is also use to refer to the frontier of capability. Especially in this context.
That's not what people are normally referring to when they say "frontier models". It means the most capable models full stop. Not the most capable that you can run locally.
I’ve always seen the frontier shown as a curve on a plot. Frontier ≠ SOTA
1 reply →
true but tfa's title says "frontier open models"
5 replies →
Had the same though. The gap between open-source and the frontier is closing in, especially with Kimi K3, but that is like >2T parameters. The Gemma 4 and other models you can actually run on an average Mac, is not in the same league.
It might be overused elsewhere but I don't think its use here is inappropriate given it's qualified: it doesn't say "frontier models", it says "frontier open models".
No, your thinking is 100% correct. It's called clickbait.
+1 came here to say this, I opened the link expecting some technical breakthrough. Misleading click bait title.