4 ms·

I’m just wondering how this translates to computer manufacturers like Apple. Could we have these kinds of chips built directly into computers within three years

• xattt 7mo ago
  Is it possible to supplement the model with a diff for updates on modular memory, or would severely impact perf?
  • baq 7mo ago
    this design at 7 transistors per weight is 99.9% burnt in the silicon forever.
  • mips_avatar 7mo ago
    I imagine you could do something like a LORA
• arisAlexis 7mo ago
  and run an outdated model for 3 years while progress is exponential? what is the point of that
  • RobertDeNiro 7mo ago
    It won’t stay exponential forever.
  • r0b05 7mo ago
    Yeah, the space moves so quickly that I would not want to couple the hardware with a model that might be outdated in a month. There are some interesting talking points but a general purpose programmable asic makes more sense to me.
  • ivan_gammel 7mo ago
    When output is good enough, other considerations become more important. Most people on this planet cannot afford even an AI subscription, and cost of tokens is prohibitive to many low margin businesses. Privacy and personalization matter too, data sovereignty is a hot topic. Besides, we already see how focus has shifted to orchestration, which can be done on CPU and is cheap - software optimizations may compensate hardware deficiencies, so it’s not going to be frozen. I think the market for local hardware inference is bigger than for clouds, and it’s going to repeat Android vs iOS story.
    • bigyabai 7mo ago
      This is the same justification that was used to ship the (now almost entirely defunct) NPUs on Apple and Android devices alike. The A18 iPhone chip has 15b transistors for the GPU and CPU; the Taalas ASIC has 53b transistors dedicated to inference alone. If it's anything like NPUs, almost all vendors will bypass the baked-in silicon to use GPU acceleration past a certain point. It makes much more sense to ship a CUDA-style flexible GPGPU architecture.
      • ivan_gammel 7mo ago
        Why are you thinking about phones specifically? Most heavy users are on laptops and workstations. On smartphones there might be a few more innovations necessary (low latency AI computing on the edge?)
        • bigyabai 7mo ago
          Many laptops and workstations also fell for the NPU meme, which in retrospect was a mistake compared to reworking your GPU architecture. Those NPUs are all dark silicon now, just like these Taalas chips will be in 12-24 months. Dedicated inference ASICs are a dead end. You can't reprogram them, you can't finetune them, and they won't keep any of their resale value. Outside cruise missiles it's hard to imagine where such a disposable technology would be desirable.
          • 7mo ago
  • padjo 7mo ago
    Is progress still exponential? Feels like its flattening to me, it is hard to quantify but if you could get Opus 4.2 to work at the speed of the Taalas demo and run locally I feel like I'd get an awful lot done.
  • selcuka 7mo ago
    > what is the point of that Planned obsolescence? /s Jokes aside, they can make the "LLM chip" removable. I know almost nothing is replaceable in MacBooks, but this could be an exception.
  • sowbug 7mo ago
    Bake in a Genius Bar employee, trained on your model's hardware, whose entire reason for existence is to fix your computer when it breaks. If it takes an extra 50 cents of die space but saves Apple a dollar of support costs over the lifetime of the device, it's worth it.