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How does Lemmy feel about "open source" machine learning, akin to the Fediverse vs Social Media? (lemmy.world)

submitted 1 week ago* (last edited 1 week ago) by [email protected] to c/[email protected]

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Obviously there's not a lot of love for OpenAI and other corporate API generative AI here, but how does the community feel about self hosted models? Especially stuff like the Linux Foundation's Open Model Initiative?

I feel like a lot of people just don't know there are Apache/CC-BY-NC licensed "AI" they can run on sane desktops, right now, that are incredible. I'm thinking of the most recent Command-R, specifically. I can run it on one GPU, and it blows expensive API models away, and it's mine to use.

And there are efforts to kill the power cost of inference and training with stuff like matrix-multiplication free models, open source and legally licensed datasets, cheap training... and OpenAI and such want to shut down all of this because it breaks their monopoly, where they can just outspend everyone scaling , stealiing data and destroying the planet. And it's actually a threat to them.

Again, I feel like corporate social media vs fediverse is a good anology, where one is kinda destroying the planet and the other, while still niche, problematic and a WIP, kills a lot of the downsides.

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[–] [email protected] 2 points 1 week ago (1 children)

Oh fuck buying Nvidia new, I was going to see if it depressed 40xx prices or even further for 3090 but I’m not sure it would.

Neat didn’t know that about rope, as you can guess largely due to having fuck all memory to work with. Is AMD viable with LLMs now? Honestly if I can make it work with an AMD GPU I just may because I agree screw Nvidia.

[–] [email protected] 3 points 1 week ago (1 children)

For inference? AMD is more finicky to setup but totally fine once you do. 7900 XTX prices can be very good.

I feel like 3090s have bottomed out, as they are just getting more rare now, and 4090s are so freaking expensive to start with I'm not sure how much they'll come down.

Another feature you might not be aware of, that people use now, is quantized KV cache. With it, I can run a 19GB 35B model and still fit 131K context into vram, with basically no quality loss.

[–] [email protected] 1 points 1 week ago (1 children)

How are you people running cuda kernels?

[–] [email protected] 3 points 1 week ago

rocm

exllama, llama.cpp, vllm/aphrodite, (I think) sglang, they all support it now.