• cyd@lemmy.world
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    1 year ago

    Ideally, they’d just blow the entire $330M training an LLM, and release the weights. In reality, much of that money will probably go into paying salaries, various smaller research projects, etc.

      • cyd@lemmy.world
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        1 year ago

        The context is that LLMs need a big up front capital expenditure to get started, because of the processor time to train these giant neural networks. This is a huge barrier to the development of a fully open source LLM. Once such a foundation model is available, building on top of it is relatively cheaper; one can then envision an explosion of open source models targeting specific applications, which would be amazing.

        So if the bulk of this €300M could go into training, it would go a long way to plugging the gap. But in reality, a lot of that sum is going to be dissipated into other expenses, so there’s going to be a lot less than €300M for actual training.

        • interceder270@lemmy.world
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          1 year ago

          Is there any way we can decentralize the training of neural networks?

          I recall something being released awhile ago that let people use their computers for scientific computations. Couldn’t something similar be done for training AI?

          • Mahlzeit@feddit.de
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            1 year ago

            There is a project (AI Horde) that allows you to donate compute for inference. I’m not sure why the same doesn’t exist for training. I think the RAM/VRAM requirements just can’t be lowered/split.

            Another way to contribute is by helping with training data. LAION, which created the dataset behind Stable Diffusion, is a volunteer effort. Stable Diffusion itself was developed at a tax-funded public university in Germany. However, the cost of the processing for training, etc. was covered by a single rich guy.