What @mierdabird@lemmy.dbzer0.com said, but the adapters arent cheap. You’re going to end up spending more than the 1060 is worth.

A used desktop to slap it in, that you turn on as needed, might make sense? Doubly so if you can find one with an RTX 3060, which would open up 32B models with TabbyAPI instead of ollama. Some configure them to wake on LAN and boot an LLM server.

Yes, but its clearly a building block of Meta’s LLM training effort, and part of a pattern.

One implication I didn’t mention, and don’t have hard proof I can point to, is garbage in garbage out. Meta let AI slop and human garbage proliferate on Facebook, squandering basically the biggest advantage (besides cash) they have. It’s often speculated that, as it turns out, Twitter and Facebook training data is kinda crap.

…And they’re at it again. Zuckerberg pours cash into corporate trash and get slop back. It’s an internal disaster, like their own divisions.

On the other side, it’s often thought that Chinese models are so good for their size/compute because they’re ahem getting data from the Chinese government, and don’t need to worry about legal issues.

The research community already knows this.

Llama 4 (Meta’s flagship ‘AI’ project) was as bad release. That’s fine. This is interative research; not every experiment works out.

…But it was also a messy and dishonest one.

The release was pushed early and full of bugs. They lied about its performance, especially at long context, going so far as to game Chat Arena with a finetune. Instead of saying they’ll do better, they said they’re reorganizing their divisions to focus on ‘applications’ instead of fundamental research, aka exactly the wrong thing. They’ve hermmoraged good researchers and kept AI bros, far as I can tell from the outside.

Every top LLM trainer has controversies. Just recently Qwen (Alibaba) closed off their top base models just to spite Deepseek, so they can’t distill them. Deepseek is almost certainly training on Google Gemini traces. Google hoards their best research for API models and has chased being sycophantic like ChatGPT. X’s Grok is a joke, and muddied by Musk’s constant lies about, for instance, open sourcing it. Some great outfits like 01ai (the Yi series) faded into the night.

…But I haven’t seen self-destruction quite like Meta’s. Especially considering the ‘f you’ money and GPU farm they have.

You can still use the IGP, which might be faster in some cases.

Oh actually that’s a great card for LLM serving!

Use the llama.cpp server from source, it has better support for Pascal cards than anything else:

https://github.com/ggml-org/llama.cpp/blob/master/docs/multimodal.md

Gemma 3 is a hair too big (like 17-18GB), so I’d start with InternVL 14B Q5K XL: https://huggingface.co/unsloth/InternVL3-14B-Instruct-GGUF

Or Mixtral 24B IQ4_XS for more ‘text’ intelligence than vision: https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF

I’m a bit ‘behind’ on the vision model scene, so I can look around more if they don’t feel sufficient, or walk you through setting up the llama.cpp server, but basically it provides an endpoint which you can hit with the same API as ChatGPT.

1650

You mean GPU? Yeah, it’s good, I was strictly talking about purchasing a laptop for LLM usage, as most are less than ideal for the money. Laptop vram pools are relatively small and SO-DIMMS are usually very slow.

Things will get much better once the “Max” AMD SKUs proliferate.

Yeah, just paying for LLM APIs is dirt cheap, and they (supposedly) don’t scrape data. Again I’d recommend Openrouter and Cerebras! And you get your pick of models to try from them.

Even a framework 16 is not good for LLMs TBH. The Framework desktop is (as it uses a special AMD chip), but it’s very expensive. Honestly the whole hardware market is so screwed up, hence most ‘local LLM enthusiasts’ buy a used RTX 3090 and stick them in desktops or servers, as no one wants to produce something affordable apparently :/

I was a bit mistaken, these are the models you should consider:

https://huggingface.co/mlx-community/Qwen3-4B-4bit-DWQ

https://huggingface.co/AnteriorAI/gemma-3-4b-it-qat-q4_0-gguf

https://huggingface.co/unsloth/Jan-nano-GGUF (specifically the UD-Q4 or UD-Q5 file)

they are state-of-the-art at this size, as far as I know.

8GB?

You might be able to run Qwen3 4B: https://huggingface.co/mlx-community/Qwen3-4B-4bit-DWQ/tree/main

But honestly you don’t have enough RAM to spare, and even a small model might bog things down. I’d run Open Web UI or LM Studio with a free LLM API, like Gemini Flash, or pay a few bucks for something off openrouter. Or maybe Cerebras API.

…Unfortunely, LLMs are very RAM intensive, and >4GB (more realistically like 2GB) is not going to be a good experience :(

Actually, to go ahead and answer, the “fastest” path would be LM Studio (which supports MLX quants natively and is not time intensive to install), and a DWQ quantization (which is a newer, higher quality variant of MLX models).

Hopefully one of these models, depending on how much RAM you have:

https://huggingface.co/mlx-community/Qwen3-14B-4bit-DWQ-053125

https://huggingface.co/mlx-community/Magistral-Small-2506-4bit-DWQ

https://huggingface.co/mlx-community/Qwen3-30B-A3B-4bit-DWQ-0508

https://huggingface.co/mlx-community/GLM-4-32B-0414-4bit-DWQ

With a bit more time invested, you could try to set up Open Web UI as an alterantive interface (which has its own built in web search like Gemini): https://openwebui.com/

And then use LM Studio (or some other MLX backend, or even free online API models) as the ‘engine’

Alternatively, especially if you have a small RAM pool, Gemma 12B QAT Q4_0 is quite good, and you can run it with LM Studio or anything else that supports a GGUF. Not sure about 12B-ish thinking models off the top of my head, I’d have to look around.

Honestly perplexity, the online service, is pretty good.

As for local running, one question first: how much RAM does your Mac have? This is basically the factor for what model you can and should run.

I don’t understand.

Ollama is not actually docker, right? It’s running the same llama.cpp engine, it’s just embedded inside the wrapper app, not containerized. It has a docker preset you can use, yeah.

And basically every LLM project ships a docker container. I know for a fact llama.cpp, TabbyAPI, Aphrodite, Lemonade, vllm and sglang do. It’s basically standard. There’s all sorts of wrappers around them too.

You are 100% right about security though, in fact there’s a huge concern with compromised Python packages. This one almost got me: https://pytorch.org/blog/compromised-nightly-dependency/

This is actually a huge advantage for llama.cpp, as it’s free of python and external dependencies by design. This is very unlike ComfyUI which pulls in a gazillian external repos. Theoretically the main llama.cpp git could be compromised, but it’s a single, very well monitored point of failure there, and literally every “outside” architecture and feature is implemented from scratch, making it harder to sneak stuff in.

OK.

Then LM Studio. With Qwen3 30B IQ4_XS, low temperature MinP sampling.

That’s what I’m trying to say though, there is no one click solution, that’s kind of a lie. LLMs work a bajillion times better with just a little personal configuration. They are not magic boxes, they are specialized tools.

Random example: on a Mac? Grab an MLX distillation, it’ll be way faster and better.

Nvidia gaming PC? TabbyAPI with an exl3. Small GPU laptop? ik_llama.cpp APU? Lemonade. Raspberry Pi? That’s important to know!

What do you ask it to do? Set timers? Look at pictures? Cooking recipes? Search the web? Look at documents? Do you need stuff faster or accurate?

This is one reason why ollama is so suboptimal, with the other being just bad defaults (Q4_0 quants, 2048 context, no imatrix or anything outside GGUF, bad sampling last I checked, chat template errors, bugs with certain models, I can go on). A lot of people just try “ollama run” I guess, then assume local LLMs are bad when it doesn’t work right.

Totally depends on your hardware, and what you tend to ask it. What are you running? What do you use it for? Do you prefer speed over accuracy?

TBH you should fold this into localllama? Or open source AI?

I have very mixed (mostly bad) feelings on ollama. In a nutshell, they’re kinda Twitter attention grabbers that give zero credit/contribution to the underlying framework (llama.cpp). And that’s just the tip of the iceberg, they’ve made lots of controversial moves, and it seems like they’re headed for commercial enshittification.

They’re… slimy.

They like to pretend they’re the only way to run local LLMs and blot out any other discussion, which is why I feel kinda bad about a dedicated ollama community.

It’s also a highly suboptimal way for most people to run LLMs, especially if you’re willing to tweak.

I would always recommend Kobold.cpp, tabbyAPI, ik_llama.cpp, Aphrodite, LM Studio, the llama.cpp server, sglang, the AMD lemonade server, any number of backends over them. Literally anything but ollama.

…TL;DR I don’t the the idea of focusing on ollama at the expense of other backends. Running LLMs locally should be the community, not ollama specifically.

Yes, we know, you are preaching to the choir here, lemmy.ml.

But I’d rather we not back out of NATO, decouple trade and invade friendly neighbors all while the US continues to screw over far away countries first. At this point, backing out of NATO is not going to help our bad behavior.

After that, we can worry about not screwing with other countries so much, hopefully.

Good move TBH. Just like that, Trump likes NATO again.

Honestly the jawboning works way better than it should.

Thank Big Tech/social media, I guess?

Yes, and that was a cruel, stupid move on the US’s part.

…But even if cooperation continued, it still would have given Iran expertise. Further enrichment is not a huge step, especially behind the cover of real civilian power programs, and given the rhetoric the state broadcasts and their neighbor’s hostility, it seems likely.

And that’s fine IMO.

I’m hugely afraid of proliferation, but going to these lengths to worry about it while the rest of the world burns seems ridiculous.

To be fair, Iran wants a nuke down the line, and civilian uranium enrichment is a huge stepping stone. There’s lots of technical alternatives they could pursue if they really just want civilian power.

…And that’s kinda understandable. They have a neighbor that randomly bombs their civilians.

Fuck it, let them have one.

Heck, they should get a tiny bit of old Soviet+US stock in some kind of international deal, so they have credible deterrence with the guaranteed stability+security mechanisms (and oversight?) of their weapons.