AMD has quietly acquired the engineering team and core assets of Canadian startup Untether AI, signaling a shift for AI model training, long dominated by GPUs.

It looks like AI has followed Crypto chip wise in going CPU > GPU > ASIC

GPUs, while dominant in training large models, are often too power-hungry and costly for efficient inference at scale. This is opening new opportunities for specialized inference hardware, a market where startups like Untether AI were early pioneers.

In April, then-CEO Chris Walker had highlighted rising demand for Untether’s chips as enterprises sought alternatives to high-power GPUs. “There’s a strong appetite for processors that don’t consume as much energy as Nvidia’s energy-hungry GPUs that are pushing racks to 120 kilowatts,” Walker told CRN. Walker left Untether AI in May.

Hopefully the training part of AI goes to ASIC’s to reduce costs and energy use but GPU’s continue to improve inference and increase VRAM sizes to the point that AI requires nothing special to run it locally

  • brucethemoose@lemmy.worldEnglish
    7·
    24 days ago

    They aren’t specialized though!

    There are a lot of directions “AI” could go:

    • Is autoregressive bitnet going to take off? In that case, the compute becomes extremely light, and the thing to optimize for is memory bandwidth and cache.
    • Or diffusion or something with fewer passes like that? In that case, we go the opposite direction, throw bandwidth out the window, and optimize for matmul compute.
    • What if it’s both? In that case, one wants a truckload of ternary adders and not too much else.
    • Or what if some other form of sparsity takes over, as (given the effectiveness of quantization and MoE), there’s clearly a ton of sparsity to take advantage of. Nvidia already bet on this, but it hasn’t taken off yet.

    There’s all sorts of wild directions the sector could go. Having the flexibility of an ASIC die would be a huge benefit for AMD, as they don’t have to ‘commit’ to any particular direction like Nvidia’s monolithic dies. If a new trend takes off, they can take an existing die and swap out the ASIC relatively quickly, without taping out a whole new GPU.

    • AdrianTheFrog@lemmy.worldEnglish
      2·
      23 days ago

      What about an asic makes it more flexible or easy to swap out? I thought it was just a general term for any application specific chip.

      I think NVIDIA’s strategy is to have high throughout and compute for fp4,8,16,32,bf8,sparsity,etc and not really commit to much. I guess if the application is very well known there’s a bit of overhead in that.

      I’m not in industry or anything so I’m not very confident in this but I don’t really see how an ASIC would be that different overall

      • brucethemoose@lemmy.worldEnglish
        3·
        23 days ago

        Because it’s a separate physical die.

        Taping out, aka simply designing a large GPU chip for production is at least a 9 figure cost. Hence Nvidia/AMD offer a relatively small selection of physical dies in products, as each die has a huge fixed cost. But AMD has specifically taken the approach of splitting up chips into smaller sections, and linking them together by placing them right next to each other, stacking them, and so on.

        Hence, if AMD, say, acquire a niche ASIC company, theoretically they can slap a variant of their design next to existing GPUs, or even next to existing CPUs, and have it share the memory bus, general compute, and other functions, without paying the full 9 figures for a massive new chip. There’s still testing costs, but it’s not so prohibitive.