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So what I'm reading is that they're either making a gentleman's agreement to let Chinese labs run circles around them or the easy gains are over and they're now running into a wall (europe.pub)
submitted 1 month ago by to c/technology@lemmy.ml
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[–] [S] 23 points 1 month ago (4 children)

That's also very possible. The US grid has very little spare capacity, and building out more will be a decades long project. So, if their newer models are more power hungry, then they might not be economically viable even with all the investor money being thrown at them.

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  • [–] 5 points 1 month ago (3 children)

    I expect more power efficient chips that are ai specific to come out in the next few years. Eventually you'll be able to run good models on your phone. Not sure about ram requirements or anything like that if the model could be shrunk down somehow. There's definitely huge gains in optimizing efficiency to be had. Right now is the equivalent of an old IBM mainframe trying to do a spreadsheet. We might even giggle at the thought of gigabytes of ram in the future with having multiple terabytes as standard on personal devices.

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  • [–] [S] 12 points 1 month ago* (2 children)

    I expect we'll start seeing stuff like Taalas where they print the model to the chip and other specialized chips like Xuantie C950 going forward. Neither of these requires DRAM, and Taalas is particularly clever since they just print the model right to an ASIC chip. So, the whole renting out LLMs business model isn't going to last long I suspect.

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  • [–] 7 points 1 month ago (1 child)

    So a repeat of the crypto crash for graphics cards when ASICs ate their lunch. Mind you, that's only for inference (although a super fast QWEN 3.8 would meet a lot of peoples needs).

    The argument for datacentres is for training the models, but then they'll need to prove that they haven't hit a diminishing returns wall, which will be hard if, as seems likely, they have. Also the Chinese have been doing it in a cave, with a box of scraps (figuratively), and gotten at least 90+% as good results.

    Seems like the recent advances have been in the frameworks, which don't need no stinking (literally if fossil fueled) datacentres.

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  • [–] [S] 7 points 1 month ago

    Right, I'd argue that China proves you don't need massive data centers for training. And yeah, I think something like Qwen 3.8 is more than enough for tasks most people do. There are a lot of tricks you can do as well with the harness, where there's a lot of attention is shifting now. And it's a lot cheaper and faster to develop better harnesses than train new models. I expect we'll start seeing a shift towards neurosymbolic systems before long where the LLM acts as a stochastic component within a symbolic logic engine.

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