• Corkyskog@sh.itjust.works
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    1 day ago

    Wait, so after you train, you don’t need all those fancy Nvidia chips?

    They should make one place where there is an overabundance of geo thermal energy and train all models there…

    • SavvyBeardedFish@reddthat.com
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      1 day ago

      Yes, so R&D and finalizing the model weight is done on NVIDIA GPUs (I guess you need an excessive amount of VRAM).

      Inference is probably gonna be offloaded to consumers in the end where the NPU is taking care of the inference cost (See Apple, Qualcomm etc)

    • Richard@lemmy.world
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      1 day ago

      Yes. You still need similar ones if you want to run the models really fast, but not nearly the same amount or cost. That’s how people run LLMs on their laptops. You don’t even need a GPU, a multi-core CPU is sufficient, just not very fast at it.

    • vinnymac@lemmy.world
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      1 day ago

      You do need great hardware, but it depends on your use case. If you want the full 671 billion parameter R1, you need to run it on specialized hardware that has enough RAM.

      If you want to run R1 on a phone, you could get the 1.5B parameter R1 running as well. But the quality of results and the speed of response diminish significantly depending on the model and the hardware you use.

      In Iceland they run their Bitcoin Mining facilities fully on geothermal energy. I wouldn’t be surprised to find the EU exploring there options regarding new data centers built on renewable energy for quite some time. For now it is a lot faster to train the models within existing data centers that already have the hardware while everyone is actively competing.

      Meanwhile governments and corporations are trying to pull money out their ass (cutting important programs) to move mountains and create AGI, of which we have no evidence this is the way to accomplish that.