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by jiggawatts12 hours ago |
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upvoteby palata12 hours ago|
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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.

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upvoteby gpugreg10 hours ago|
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> 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.

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upvoteby accrual8 hours ago|
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Filelight / ncdu are my friends for finding random 30GB directories containing cached models.
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upvoteby gpugreg7 hours ago|
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Personally, I prefer QDirStat. I just tried to use FileLight to compare, but the package seems to be broken on Lubuntu.
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upvoteby jiggawatts15 minutes ago|
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You two have just listed several tools needs to clean up after another tool: not a strong argument for “simpler”.
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upvoteby ahartmetz12 hours ago|
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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.
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upvoteby jiggawatts15 minutes ago|
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Three hours is a lot longer than one minute.
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upvoteby AbsurdCensor6 hours ago|
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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.
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upvoteby dgellow44 minutes ago|
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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!
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upvoteby peri-cl8 hours ago|
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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!"

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>
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upvoteby leansensei12 hours ago|
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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.

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upvoteby Anonyneko9 hours ago|
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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)
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upvoteby NekkoDroid2 hours ago|
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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...

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upvoteby tsss10 hours ago|
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RTX Super 12GB costs $700. An openrouter account costs nothing.
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upvoteby freehorse9 hours ago|
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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.
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upvoteby Forgeties796 hours ago|
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> 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

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upvoteby jiggawatts13 minutes ago|
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“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!”
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