[senko]

I ran the Q4 quant (used with llama.cpp) though my "minesweeper" vibe-coding benchmark: https://senko.net/vibecode-bench/2026/minesweeper-gamma-4-12...

The result is decent, but it had a few bizzare/trivial syntax errors I had to fix manually: it would do an extra closing bracket or paren a few times, and wanted to separate function definitions with comma. Not sure what that was about, but otherwise the output run just fine.

So, with those qualifiers, I think it's a decent local coding model. It roughly compares with GPT-4.1 (!!), released 14 months ago, on the output: https://senko.net/vibecode-bench/2025/minesweeper-gpt-4.1.ht... (actually I'd call it better, but those syntax errors...)

I ran the quantized version (4-bit GGUF) on my consumer-grade card with 12G of VRAM and got 5t/s for output. Not for interactive use for coding, but fairly capable model.

To me, it's fascinating how much progress we got in over a year. GPT-4.1 was considered an extremely capable coding model. Now we got something with 12B of params performing roughly the same (in this specific benchmark, disclaimers, etc).

Lists of various models I tested: https://senko.net/vibecode-bench/

[0xbadcafebee]

It was almost certainly not trained for coding, as it's got both audio and vision input, is only 12B, and nowhere in the announcement is coding mentioned. It will likely not have good performance on coding in general, compared to other small models like Qwen 3.6 35B A3B, Gemma 4 26B A4B, Nvidia Nemotron 3 Nano 30B-A3B, gpt-oss-20b.

For 16GB laptops, Qwen 3.5 9B is the undisputed champ.

Gemma 4 31B is the top dog at small model coding, but is dense so it needs ~48GB unified RAM for full context. If you want decent coding on a laptop you need a lot of RAM. But this shouldn't be surprising, dev machines have always needed lots of resources.

[dirkg]

> For 16GB laptops, Qwen 3.5 9B is the undisputed champ.

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.

[ricardobayes]

You can run it, however those low quantized models (iQ2, iQ4, Q2) will very likely underperform the 9B versions at Q6/Q8.

[kanemcgrath]

Something about qwen models hold up really well even at low quants. for most other models anything under q5 is cooked, but on 35B-A3B I can get a lot of things done even at q3_xl. It is definitely better than full precision 9B

[selicos]

I want to try a hybrid setup of Gemma 4 E4B with lots of context for general, then Qwen 3.5 9B or larger for coding. Strix Halo set up this weekend, which may enable even larger Qwen models with tons of context.

[dofm]

The larger Gemma models are quite good at PHP. I would not be surprised if that was a training objective — it's one of the more consumer-focussed programming languages. They have very good knowledge of wordpress hooks.

[dotancohen]

  > For 16GB laptops, Qwen 3.5 9B is the undisputed champ.

You seem like the guy to ask. For a laptop with 12GB VRAM (RTX 5070) and 32 GB system RAM, what is a good multilingual (English, Hebrew, Greek) model for conversing with personal notes in Org mode format? I don't care how long updating the model or rag takes, and even inference can be reasonably slow, but the results of the query as they relate to my personal notes are important. I don't care about general knowledge, for those questions I can use e.g. ChatGPT.

Thanks

[akmarinov]

Joins us over on Reddit at r/LocalLlaMA to get 10 different opinions on that

[dotancohen]

I read there regularly. I find little value there between the memes. I was hoping to ask a knowledgeable person here.

[alfiedotwtf]

/r/localllama for a while now seems to prefer Gemma 4 E4B for creative writing (especially the uncensored GGUFs).

[plagiarist]

Do they prefer E4B over the larger models or is it a matter of what fits their machine? I assume 4B isn't large enough to get interesting writing but I don't know anything about it.

[nl]

Qwen 3.5 35B A3

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.

[sourcecodeplz]

Any Gemma 4 model, they are great at translations, multilingual

[silversmith]

For the biggest languages, Spanish, French, maybe.

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.

[dotancohen]

While Gemini 4 seems fine, Gemma 4 does not do Hebrew well. I've replaced it with Aya Expanse and am getting much better results, but there is still much improvement to be had.

I'm not doing translations, rather querying Hebrew text with a Hebrew prompt.

[emmelaich]

You may like https://www.llmfit.org/

(not recommendation, I've not used it .. yet)

[hypfer]

Just tried it and honestly it's a terrible experience lacking any sort of intent or reason.

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.

[hypfer]

Done:

https://github.com/Hypfer/will-it-fit-llama-cpp

https://hypfer.github.io/will-it-fit-llama-cpp/

[hparadiz]

We need benchmarks by engine, cli switch sets, and device with filters by cpu, gpu, and type. And if someone could please aggregate that in a way where people can upload results and just automatically see the best of any model for their device that would be a killer app.

[alfiedotwtf]

I've wanted to vibe code a tuning app, that pumps data through your CPU-GPU-RAM to try and determine the best parameters for each model, but I think it's just too much work compared to manually running by hand a one-liner and changing things here and there.

[dofm]

I have found these things to be fully exasperating, to be honest, even though I am seeking information about a pretty "known" machine — a 64GB M1 Max MBP.

(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.)

[tacomagick]

Gemma 4 26A4B

[kajecounterhack]

Have you found Gemma 4 31B better than Qwen 3.6 27B Q8? I just started using Qwen + Pi agent and it's great, but "which model works best" is still totally crowdsourced and I was going off of peoples' opinions on reddit. Would love to hear more opinions if people have them.

[embedding-shape]

> Have you found Gemma 4 31B better than Qwen 3.6 27B Q8?

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.

[xenophonf]

It took me way too long to realize you were referring to r/localllama.

[MoonWalk]

Why the obfuscation in the first place?

[embedding-shape]

Just a bit of flair. Also, bunch of people have "keyword watchers" setup for various terms, so when you mention certain things on HN, reddit and elsewhere, you get commentators who enter the conversation not because the context or larger conversation, but because the single term/thing they care deeply about was mentioned, and it just gets very boring to read the whole attackers/defenders comments over and over again. But ultimately I just did it like that because it was more fun to write it like that.

[zozbot234]

I'm not sure that GP is correct, many people in that forum tend to hate Qwen for closing up many of their more recent models and leaving the whole local inference community 'stranded' on their older releases.

[thangalin]

Yes. I'm using Gemma-4 31B (gemma-4-31B-it-assistant.Q4_K_M.gguf) with llama.cpp to attribute quotations throughout chapters of my sci-fi novel. I started with Qwen3, but couldn't get it to work. Qwen3 TTS Voice Design, on the other hand, is incredible (Qwen3-TTS-12Hz-1.7B-VoiceDesign). I'm using both for an audiobook generator that produces a variety of voices.

Screens:

* https://i.ibb.co/TBBV5nJk/kl-01.png (voice design)

* https://i.ibb.co/nNvvKDyV/kl-02.png (quotation attributions)

[khimaros]

building something similar: https://github.com/khimaros/autiobook

[qingcharles]

Gemma 4 31B is enormously impressive. You get 1000 requests/day for free on Google's API and another 1000/day off OpenRouter. Only problem is you get 503 like crazy.

[iso1631]

I find ram crazy. My thinkpad has 32G of ram, it's a t470 that's nearly a decade old

Why do people with modern laptops have such little amounts of ram?

[willy_k]

The ram that’s important for LLMs is gpu-accessible memory, meaning either systems with unified ram or VRAM, the latter of which is tied to the caliber of GPU one has.

[alfiedotwtf]

8Gb was the standard for a long time (before Apple went Silicon), because from what I understood, is that SDRAM needs to contantly power cycle the memory bus otherwise the bits will fade, and so by having more RAM, your battery would last a little less... this was around the time when 3 hours charge was unheard of, so every little bit helped.

Probably doesn't matter these days with all-day batterys, but now the demand-supply curve is lopsided.

[doubled112]

My job still issues 16GB laptops as standard. You need a business reason to get more. This has been going on since before the price hikes.

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?

[SturgeonsLaw]

Unified memory is soldered to the motherboard and needs to be ordered with the new laptop, for prices that are well above what the equivalent amount of SODIMM would cost.

Fine if work's paying, but for personal devices (that might have been purchased before local models got good), people have what they have.

[AshleyGrant]

It doesn't have to be soldered to the motherboard. I've got a Minisforum PC that has unified memory installed via dual SODIMM slots. I put 64 gigs of DDR5 sticks that cost me over $600 and can determine the split between the system and VRAM in the BIOS.

[senko]

Yeah, I agree 24B-36B sizes are better in general.

I don't have unified RAM tho and offloading to CPU is dog slow, which is why I'm interested in 7b-12b models.

[jmpeax]

> nowhere in the announcement is coding mentioned

It's right there in the middle benchmark bar "LiveCode Bench" 72%.

[ricardobayes]

Qwen 3.5 9B is great for coding, but somehow, based on a few hours of subjetive tests, the Gemma 4 12B seems even better.

[mark_l_watson]

I had odd Gemma 4 12B results: it was ‘almost excellent’ for writing code in a variety of languages if I was using a detailed one-shot prompt describing new code to write.

I had horrible luck with Gemma 4 12B with a variety of coding harnesses - but as usual Qwen 3.5 9B did OK.

EDIT: CORRECTION: I pulled a fresh copy of Gemma 4 12B and inference code and the tool use problems in my test harnesses are fixed. Gemma 4 12B is slow on my 16B MacBook Air, put produces OK results.

[dofm]

It does appear to have training for javascript and PHP, from what I can see, and pretty solid knowledge of wordpress and woocommerce. I would guess it has beginner-friendly knowledge of Python, too?

(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)

[sgt101]

31B won't run in 48GB for me - it needs 54.

[yassa9]

what quantization did u try ? u can use Q4 quantization, im pretty sure that 48GB would be enough

[sgt101]

8bits is fine.... I was talking full bore.

[superkuh]

>consumer-grade card with 12G of VRAM and got 5t/s

That speed for token output indicates to me that it somehow is using hybrid mode and involving cpu+system ram somehow. That ~5tk/s is about the ram bandwidth of DDR4 RAM versus that size model at 4bit. Any consumer GPU with 12 GB like a nvidia rtx 2080 or rtx 3060 should be doing 20+ tk/s with llama.cpp and CUDA backend.

[senko]

Good catch. I haven't looked deeply into it. This is with Vulkan backend on Linux which I understand should be roughly comparable to CUDA? Gfx is rtx 3060(ti?).

I should play a bit more with llama.cpp options and see what bappened there. Thanks!

[superkuh]

I've had it happen in the past with llama.cpp on linux that the CPU will present itself as a vulkan device GPU1 with "PHYSICAL_DEVICE_TYPE_CPU" and had a mix-up. Might want to try llama-server --list-devices and then append --device Vulkan0 or whatever.

[pja]

The 8 bit quant runs at 36tps using Vulkan on my AMD rx9070.

[zigzag312]

> It roughly compares with GPT-4.1 (!!), released 14 months ago

I think the mayor win for coding was reasoning. That's why such a small model can match GPT-4.1 in coding, but I suspect that GPT-4.1 still wins in general world knowledge due to bigger size.

[mdp2021]

> I suspect ... still wins in general world knowledge due to bigger size

Encyclopedic knowledge matters relatively little in perspective, given the expectable future developments: even the more knowledgeable of us will use that knowledge for reasoning and intuition (and we will have absorbed the intellectual keys during our training), but under our professional hat we should in theory be ready to go "I stand corrected" and "more precisely" with the actual data at hand.

I.e.: for the encyclopedic knowledge needed, the /understander/ will have a RAG subsystem and a corpus of knowledge to inquire upon processing queries.

(Corroboration: we can't delirate, and neither can the machine...)

[bitexploder]

Don't LLMs work on attention though? The closer in their hyperdimensional space you can land your problem to their inherent understand the better they are at understanding your problem domain. RAG loops can be very slow and agents may simply lack the knowledge to use them correctly.

[mdp2021]

But, in short, the ability to manage information, to process it properly, is more important in this regard than just having the information. "Having" more knowledge is not a guarantee to "using" it better.

And to improve reliability, if the machine can check, it will have to check. "Costly" cannot be an excuse.

[zigzag312]

Understanding of a specific problem space can be a prerequisite to be able to form a proper query (i.e. to ask the correct question).

Model doesn't know what it doesn't know.

[mdp2021]

Your suggestion is not clear: yes we reason and define relevant details (maybe through further information retrieval) to better construct queries - that is what Analytical school of thought taught and insisted on -, and even more crucial is that the subsequent delegated steps, of constructing replies, imply reasoning and information retrieval.

Said abilities - intellectual strength - are immensely more important than notions. The relation between network size and intellectual strength, vs network size and notions (original topic in this branch), is presumably not yet that clear. Intelligent models may not necessarily be embedded with explicit information of everything, though they will have to have ways to reach that upon contingent necessity (to solve specific problems). Like us.

[pu_pe]

I agree with you in general, but depending on the task I also find that a certain level of encyclopedic knowledge can be very valuable. For example, if you use it for coding, the model will likely not resort to search or RAGs when deciding whether to use a particular package or stack.

[coldcity_again]

A great position to take. Strong opinions, weakly held.

[McGlockenshire]

> my consumer-grade card with 12G of VRAM and got 5t/s for output

Thank you for giving me hope!

[UncleOxidant]

I've heard the assertion that the Gemma 4 models don't do well with lower quantization. I wonder if the "bizzare/trivial" syntax errors would go away at Q8?

[amelius]

> it would do an extra closing bracket or paren a few times

I had this with Gemini: in the middle of a C++ program it once said RParen instead of using )

It was easy to fix of course, but it makes you question what is going on inside its head.

[pja]

The Unsloth 8bit quant seems to manage this task without any syntax errors.

[frikk]

Thank you for sharing this. Do you think the syntactical issues could be addressed with fine tuning or some other kind of parameter tweaking? That's frustrating hah.

[profunctor]

With a harness you could feed the code to a linter and if there are errors feed that to a model automatically. It’s amazing that the models are good enough that I haven’t bothered doing this

[DeathArrow]

>The result is decent, but it had a few bizzare/trivial syntax errors I had to fix manually

Can you instruct it to use a lsp?

[borissk]

We are really getting close to singularity - the pace of LLM improvement is constantly accelerating.

[pseudosavant]

Models this small and this capable bode really well for the usefulness of a PC like the RTX Spark that Nvidia/Microsoft announced this week. 128GB of unified memory will likely be more than sufficient for effective local agentic coding, even if SOTA cloud models will still be even better.

Up until this point, I've found the cost/value to unequivocally favor using a cloud subscription, but I would be lying if I didn't worry that one day OpenAI is going to increase the price for my subscription by 5-10x. I rely on these tools enough that if there is a real viable local option, I'm going to take it.

[pseudollm]

> usefulness of the RTX Spark

Not really. There's a reason the announcement didn't include ANY benchmark (!) and didn't mention EXACTLY what is the memory bandwidth. It's going to be dog-slow unusable for large models, as tok/sec is basically bandwidth divided by active weights. Rumoured 300GB/s / 30GB active weights (decent model) = 10 tokens per second, which is really slow

[SwellJoe]

Yep, I have a Strix Halo and while it can run models bigger than Qwen 3.6 27b, it's not usable interactively when you do. ds4 patched for ROCm works, but at such a slow speed, it's not usable for coding agents.

The Nvidia boxes have only slightly more memory bandwidth, so I wouldn't expect them to be notably faster. At least not enough to make it useful interactively at that scale.

[zozbot234]

Why does everyone expect interactivity from local AI? It's not the best use of the hardware, especially not miniPC hardware. Long-term batched inference with larger and more capable models is much more feasible AIUI.

[int_19h]

I can't speak for others but IMO the only reason to run models locally right now is privacy - i.e. you don't trust any of the cloud providers to not look at your prompts. Price-wise the market is extremely competitive and cheap model serving favors large scale so anything that can be run locally can be run cheaper in the cloud. But if privacy is important, then it's important for everything, including traditional chatbot applications, which kinda do require interactivity.

[SwellJoe]

Even batched it's uncomfortably slow. I started to benchmark ds4 with my security vulnerability benchmark (after Qwen 3.6 dense and MoE and a bunch of cloud models), but it was going to tie up the Strix Halo for more than a day, so I decided not to run it as it would prevent me from doing other stuff with it during that time.

Even batched usage needs to be fast enough to deliver results in a reasonable time. Overnight runs are useful, 24 hour runs are...less so.

Anyway, most of the time people are talking about interactive use, and there's currently an upper bound on how large a model can be for local hosting on a reasonable budget (i.e. not a crazy amount more expensive than what a high end developer desktop or laptop costs). The sweet spot is probably currently the big Qwen 3.6 or Gemma 4 models, which are in the ~60GB range for 8-bit quantization plus a large context.

[hedgehog]

The 6-bit versions + 8-bit KV cache seems to save a good bit of memory without a significant loss of quality. The Qwen 35B is pretty fast in my testing, but MiniMax M2.7 230B is in some ways faster (way fewer tokens to arrive at an answer) even though it is much larger.

[dirkg]

The RTX/DGX Spark, Mac Ultras with 128GB unified ram are all ~$5k. Its still an expensive toy for rich people, it might as well be an H100 for 99.9% of the population (not devs with high paying jobs, of course).

the value of local models is allowing normal people to access AI without needing to subscribe to cloud services. this is esp imp for the rest of the world where even a 12GB gpu is extremely expensive.

there is no real viable local option that will come even close to Sonnet/Gemini Flash or the cheaper chinese models. Even if your pc costs <$2k you are never going to recoup the hw costs, and the results will be far worse.

[green7ea]

I'm using a Strix Halo laptop (~3k, 64GiB) and with Gemma 4 and Qwen 3.6, both at 8 bits, I'm seeing very impressive results.

As a work tool, this is reasonably priced. You can save a bit of money by opting for a non-laptop form factor.

[organsnyder]

My Framework Desktop with 128GB was about half that. I did luck out by buying before RAM prices went crazy, though.

I'm looking forward to the fallout when the data center bubble bursts. There's a good possibility we'll see a glut of hardware, either on the used market or from manufacturers that no longer have massive orders from OpenAI and the like.

[zozbot234]

RTX Spark is pretty much the DGX Spark in a laptop form factor, plus some lower-performing chips in the same series to be released later according to rumors. We know quite well how the top-of-the-line chip performs: it's very interesting for some application areas, less so for others.