alt Hacker NewsThis is the diametric opposite of the rent-vs-buy scenario that this entails.
Local: You need to invest $thousands into GPU and/or very-high-end CPU+Memory hardware.
Vendor: You can use any existing device, even a phone or tablet. A very low-end laptop is fine.
> takes literal minutes to get started
Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers.
Vendor: Free-tier available instantly on a web URL. Even local agents have free tiers from multiple vendors. Install is a single command and/or download and "next,next,next,finish" wizard that takes ~1 minute.
> you can just `rm -fr` it and forget the whole thing existed.
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!
Meanwhile I simply... stopped using Gemini. That was the entire process: I no longer actively use it. They stopped billing me for my token usage, because it is now zero. That's... it.
You have it totally backwards.
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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.
> "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.
> 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
> 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.