you can run qwen 3.6 35BA3B on a 12-16GB vram gpu and ot works pretty well.
https://www.youtube.com/watch?v=8F_5pdcD3HY&t=1s
even the 27B in some quants can fit.
https://www.reddit.com/r/LocalLLaMA/comments/1tkmgwj/qwen27b...
qwen IMO is far better for coding, esp agentic coding when combined with something like Pi, it comes probably close enough to Sonnet for a lot of use cases.
Gemma family is better for almost all other tasks you'd use a local llm for.
> For 16GB laptops, Qwen 3.5 9B is the undisputed champ.
Thanks
Qwen models are always good. The 35B A3 model is a MoE model which means it has higher performance in RAM constrained environments compared to the 27B dense model (which is better at coding).
I don't have experience to rate it's Hebrew or Greek performance but apparently it's not bad.
For smaller ones like my native Latvian, the output could be confused for good translation from across the room, the words do look like Latvian words. But the quality is Google translate circa 20 years ago, tops.
It could probably do a decent enough translation to English, if all you need is to get the gist of text. But for smaller European language outputs, nothing comes close to Gemini.
I'm not doing translations, rather querying Hebrew text with a Hebrew prompt.
(not recommendation, I've not used it .. yet)
Which is unsurprising in the AI space.
You get a wall of text showing you various random fine-tuned models by random people, and that is basically it.
Actual sane default requirements like "just give me the normal AI labs", "please filter for dense only" and "I want this exact context size at this quant" are not part of the tool, apparently. Neither is "compare these quants for me for the same model".
Or maybe it's just hidden enough that I did not find them before I've stopped caring.
Conway's law is at it again.
____
Edit:
I have since then had qwen3.6 ponder the codebase and think about my complaints.
Seems to require a major data model overhaul to actually fix those, so they're legit. Which I didn't doubt, but nice to have some extra fabricated confirmation after it initially refused and said "nooooo the readme says otherwise nooo hypfer is just a hater noo"
___
Edit 2:
It gets worse the longer I stare at it. This could've been a web calculator.
(Honestly I think Apple's "AI push" could do worse than just focus on a curated model library, a couple of Apple-standard Gemini distillations, an OS-level model manager and some sort of tweak of their containers system to do what Docker's sbx does. They could demystify a lot of this shit.)
Which quant of Gemma? For coding Qwen seems to be pretty far ahead, but generally Gemma seems to have a "vaster" set of knowledge, but armed with a search tool it doesn't really matter, and Qwen 3.6 been really great for all sorts of tool calling. I mostly do programming and related things though, fwiw.
> I was going off of peoples' opinions on reddit
It's extremely astroturfed all over the place, especially the larger subreddits, and especially the one related to a specific animal in a specific location. It's sad, as early on it was a great resource, but now it's mostly paid posts and a race to the bottom, with lots of piling, and all the knowledgeable people I used to recognize are nowhere to be found.
Screens:
* https://i.ibb.co/TBBV5nJk/kl-01.png (voice design)
* https://i.ibb.co/nNvvKDyV/kl-02.png (quotation attributions)
It's right there in the middle benchmark bar "LiveCode Bench" 72%.
(Though it is gaslighting me about PHP anonymous functions.)
I would not use it to write code (the MoE 26B writes really good PHP), but it appears to have absolutely good enough knowledge to write implementation plans, and I think that could be useful in a sort of agentic coding tutorial environment.
I test these models with simple things. My favourite mini test is asking an AI to write a "last login" tracker facility for wordpress with a sortable admin column, which is trivial code — only a few lines -- but touches on a reasonably deep bit of the WP API. If you ask it to prompt you with clarifying questions, those questions are quite revealing.
It can write the code. Not tested it but I am sure it works. It's not as elegant.
It is not as good at understanding nuanced instructions as either the 26B or the sparse Qwen 3.6. There are concise things you can say in a prompt to Qwen 3.6 that have it draw logical conclusions that fully impress me.
I am more impressed by it than I expected. I reckon this would be quite useful in a tutorial tool.
(I say this as a sort of qualified cynic; I think much of the AI circus is a farce. But if these things are to ever be useful for teaching without making people dependent on some cloud "intelligence tap", this is progress)
I don't have unified RAM tho and offloading to CPU is dog slow, which is why I'm interested in 7b-12b models.
Why do people with modern laptops have such little amounts of ram?
Fine if work's paying, but for personal devices (that might have been purchased before local models got good), people have what they have.
I’m a system administrator and I can do my job with no issues at 16GB. Most days 8GB would likely be enough, since I’m just using and abusing other systems anyway.
Java devs at my last job were still running 16GB in 2020. Admittedly that was a while ago. Still not a decade.
Close some Chrome tabs?
Probably doesn't matter these days with all-day batterys, but now the demand-supply curve is lopsided.