• Buffalox@lemmy.world
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    1 month ago

    Chinese companies are heavily incentivized to use Chinese chips instead of American since Trump blocked trade with China.
    China used to parallel import the chips they needed, and even repackage them with more onboard RAM, making more powerful Nvidia solutions available in China than in the rest of the world.
    But Trumps behavior towards China made the Chinese government decide to limit the use of American technologies for AI.
    There was a point where Nvidia exports to China was basically at a standstill, because China forbade the purchase of a new cut down Nvidia chip made for the Chinese market to circumvent American trade restrictions.

    China is building their own complete stack now, replacing everything with Chinese technologies, right from the AI chips to the entire AI software framework.

    So not only does Nvidia and other American companies lose hardware sales, the entire stack will be threatened with a Chinese alternative, that will likely compete with American options on the international market in the future. If Cuda loses its current dominance, it will be easier for competitors to take marketshare from Nvidia.

    Hopefully this will be good for consumers worldwide.

  • RabbitBBQ@lemmy.world
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    1 month ago

    China is so far ahead of the United States in manufacturing and technology that the United States will never be able to catch up.

    • Melvin_Ferd@lemmy.world
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      1 month ago

      Any new technology instantly gets turned into some new boogeyman instead of being embraced. Works out great for China when western people become luddite at a very critical window of opportunity from emerging technology. You’d almost think China has a strong ability to flood western sites with propaganda.

      • super_user_do@feddit.it
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        1 month ago

        Those are two completely different contexts. Every new technology in the west is usually meant to restrict existing freedoms while in China they never historically had said freedoms, so to them it’s just an improvement in productivity while to us is not as much of an improvement at the cost of completely usurping the existing rights that made the west great

        • Melvin_Ferd@lemmy.world
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          1 month ago

          Is your argument that Chinese government is not using technology to restrict freedoms?

          How is that being upvoted

          I would say that decades of western Hollywood has primed the population to mistrust any new technology and only see potential dystopian ends in exchange for entertainment. Combined with China spreading propaganda to encourage those views in western nations. Sites like lemmy and Facebook are heavily used as generators to spread mistrust.

  • ag10n@lemmy.world
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    1 month ago

    You can run it on CPU alone. Not surprising they’re building their own AI ecosystem

    • brucethemoose@lemmy.world
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      1 month ago

      Not at scale. Even on the new architecture, one really needs some kind of accelerator to make it economical for servers.

      Bitnet-like models might change the calculus, but no major trainer had tried that yet.

      • ag10n@lemmy.world
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        1 month ago

        Yes, you can run it at scale. Which is why it uses Huawei hardware.

        You can run it on anything, scaled or not

        • theunknownmuncher@lemmy.world
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          1 month ago

          Nope! You don’t know what you’re talking about. At all. But you can have fun running a 1.6 trillion parameter model on CPU at basically 0 tokens per second at scale, MoE or not.

            • theunknownmuncher@lemmy.world
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              1 month ago

              You’ve proved my point that you don’t know what you’re talking about by blindly linking to the git repo. Couldn’t find any source that supports your claim? I wonder why.

              Sure you can serve one request at a time to one patient user at a slow token per second rate, which makes running locally viable, but there is no RAM that has the bandwidth to run this model at scale. Even flash would be incredibly slow on CPU with multiple requests. You’d need the high bandwidth of VRAM and to run across multiple GPUs in a scalable way, it requires extremely high bandwidth interconnects between GPUs.

              • ag10n@lemmy.world
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                1 month ago

                Thank you for proving my point. It can be run on a cpu

                “It’s slow, it’s inefficient” it still runs

                It’s a foundational model just like R1 was.

                • theunknownmuncher@lemmy.world
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                  1 month ago

                  Yes, you can run it at scale.

                  at scale

                  Shift those goalposts! We went from “at scale” to “it still runs”

    • theunknownmuncher@lemmy.world
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      1 month ago

      I mean, sure. You could also run it by drawing marks in sand. It doesn’t make any sense to do either, though.