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> I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!

Are you trying to say that local models are hard to use because... you're having issues handling files properly? I am not sure I get the argument.

I get the rest of the comment: local models require an investment upfront, and it is less convenient. It doesn't say that it is not cheaper, though.

> you're having issues handling files properly?

I guess they were using ollama, which does not tell you where it puts the models it downloads.

Filelight / ncdu are my friends for finding random 30GB directories containing cached models.

Personally, I prefer QDirStat. I just tried to use FileLight to compare, but the package seems to be broken on Lubuntu.

You two have just listed several tools needs to clean up after another tool: not a strong argument for “simpler”.

It took me about three hours total to set up a local model. I already have a GPU and I have fiber for the download. llama.cpp is not difficult to compile and has many backends. It can run parts of the model on different backends, like in the common case that the GPU doesn't have enough VRAM for everything. There are many step-by-step guides available.

Takes even less depending on your system. LM Studio or Lemonade and you are set up in minutes and now they can even tell you what models will fit with the memory you have.

And it would be in seconds if models weren’t that large and slow-ish to download! LM studio is such a noob friendly experience, pretty neat first experience!

At least for the most part, if you are downloading from huggingface, you should be able to saturate your connection. I know I usually can pretty easily even with a 5gig connection at home.

Hmm, let’s not talk about my German poor internet connection please :)

Three hours is a lot longer than one minute.

No shit, but the huge ordeal you described is an exaggeration.

"X is less than Y".

"X is literally more than Y".

"You are exaggerating how much X is!"

"It's still a lot more than Y."

PS: This whole thread reminded me of several managers I've worked with who were pathologically unable to estimate... anything, be it driving time or development effort.

They always focused on the "minimal aspect", ignoring everything before and after. Walking to the car park. Standing in line at the machine. Paying at the machine. Getting out of the car park in the car, surprisingly long during busy times. Driving through traffic. Any delays that could -- and regularly do -- occur. Finding parking. Actually parking. Walking from the car park. Etc.

"It's just a 5 minute drive!"

No, it isn't, not door to door.

In which case, are you sure about your one minute?

> "I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!"

I used to deal with these kinds of frustrations too.

    fd --unrestricted --size +1G

    fd --help

    -u, --unrestricted...
        Perform an unrestricted search, including ignored and hidden files. This is an alias for
       '--no-ignore --hidden'.

    -S, --size size
        Limit  results  based  on  the  size  of  files  using  the  format
        <+-><NUM><UNIT>

Huh what? Qwen3.5-35B-A3B runs just fine with maximum context, on an RTX SUPER 12 GB, with offloading of some expert layers to DDR4-3200.

Same story on an RTX 4060 Ti 16 GB. MTP is a serious boost to tg.

Downloading the model is a simple hf command that HuggingFace's web UI even gives you.

llama.cpp is trivial to use, and so is llama-swap, if you want to use other models too.

If you don't know what arguments to run it with, you download ggrun and use that.

Local LLMs are incredibly capable and don't need expensive hardware. A $500 GPU will do. Or even cheaper.

This is all trivial.

RTX Super 12GB costs $700. An openrouter account costs nothing.

A lot of people already have 12GB+ GPUs lying around for playing games, doing video editing, etc. I would not get a GPU or mac just to run LLMs personally, but if one wants to get such a device for other tasks too, it may make sense to eg choose a slightly higher (v)RAM variant if they want to run some bigger models. Then what you pay for the local llms is just the difference.

Full model or a 4-bit quant? I have a 5090 and I'm not sure whether I should use a quant that fits within the VRAM or a much bigger version where I'd have to offload a lot to 64GB RAM and a beefy CPU (but still a CPU)

I personally run the Q6 quant on my RX 9070 XT (16GB VRAM). On r/LocalLlama there was a post recently as well, which talked about the degradation of different quants (for the 27B version)[0]

[0]: https://www.reddit.com/r/LocalLLaMA/comments/1vef79c/quantiz...

> Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers.

Download LM studio, search models, click download, wait minutes, prompt and have fun

“If you have the prerequisite hardware, then… know which model you want out of thousands of a variants… and your drivers are up to date, then it is fast!”

> If you have the prerequisite hardware

Literally tens of millions of people of silicon MacBooks have sold send 2020 so it’s probably safe to say hundreds of millions of people have the necessary hardware. Not even getting into smartphones.

>which model you want

Have you personally searched for models in LM studio? It’s actually pretty straight forward and it tells you with a very clear icon if it will all fit in your GPU or if it will offload onto ram.

>drivers are up to date

Are you just making things up now? I run LM studio on an M1 MBpro (albeit very small model for small tasks with tool calls) and on a Linux (fedora) PC with an AMD GPU. In both cases i downloaded LM studio, quickly found models with their search, and started messing around. I am not a coder or engineer mind you, so clearly it isn’t that difficult.