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PaperclipPerfector  2mo ago (text post)   25018 thread views

Culture War Roundup for the week of September 29, 2025

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You can run a homebrew LLM (7 billion parameters / 12bn / even 24bn) for nothing on any decent PC with a GPU. It will be lucid but really pretty dim.

You can rent a RunPod server pay-as-you-go and run a 70bn / 105bn / 200bn model for a few dollars an hour. It will be smarter but not quite GPT / Claude level. You can also pay 25 USD a month for Featherless, which is the same thing but less under your control.

Or you pay for the APIs.

I've run a few 4-byte quantized 70B models on a small home gaming machine pretty easily (Intel i3-13100, nVidia 3060, 48GB RAM). It's a little slow -- non-MoE models can go into a couple tokens-per-second, and MoE seldom go higher than 10 tps -- but there are some set-and-forget use cases where the difference isn't a big deal, and you're just a couple GPU generations away from it going faster.

Both ollama and lmstudio work pretty easy 'out-of-the-box'. You can dive down the deep end if you want, and start moving to vllm or others, but it's far from necessary for most use cases.

Scaling up without waiting can get expensive, though. Used server GPUs aren't ludicrously expensive and buy you more RAM (and thus more context/bigger models), but they're slower than current-gen (or even two-gens-old) gaming cards. Trying to break past 24GB VRAM gets into the kilobucks range, and while nVidia says that they're dropping a card that will change that in a few months, it'll probably be seconds before it get scalped. For LLMs, processing power is lower priority than total memory bandwidth, so you can get away with some goofy options like the Ryzen Max series and run 128 GB ""VRAM"" with a CPU, but setup is more annoying and throughput suffers a lot, and it's still not cheap.

I have a Ryzen Max 395+ with 128GB RAM and it runs pretty well; granted I don't use it for LLMs but the humongous amount of RAM is useful more often than one might think.

From what I've read on HN and Twitter, the Ryzen Maxs can run larger LLMs, but not very fast. The throughput for tokens/s is single digits at times.

God damn man! What do you use it for if not llms?

Prototyping things which I really should be pushing off to a cluster for computation but I can't be bothered with doing the SSHing. When it comes to prototyping not needing to do the extra steps of moving my latest version of the code over to the cluster saves me around 10-15 seconds for each iteration, which is enough of an annoyance I'm happy to pay to avoid it. My machine is the HP laptop that comes with AI MAX 395 so it's my main personal device. Not needing to worry at all about RAM management for my own code has been surprisingly freeing.

That and of course playing DOTA with all settings set to maximum, it's amazing how I can get Desktop quality DOTA performance on my laptop today.

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