5 ms·

Strange to see no mention of TPUs...

• talldayo 2y ago
  TPUs are like the NPU of the training world. You take a bunch of extra time, money and dedicated silicon and end up with an ASIC that barely competes on equal terms with a similarly priced GPU. Unless you've got access to Nvidia's TSMC supply, you're probably not going to make a dent on their demand. Additionally - TPUs are completely useless if AI goes out of style, unlike CUDA GPUs. The great thing about Nvidia's hardware right now is that you can truly use the GPU for whatever you want. Maybe AI falls through in 2026, and now those GPUs can be used for protein folding or crypto mining. Maybe crypto mining and protein folding falls through - you can still use most of those GPUs for raster renders and gaming too! TPUs are just TPUs - if AI demand goes away, your dedicated tensor hardware is dead weight.
  • ilove196884 2y ago
    Also TPU v1,v2 and v3 were ASICs, but since v4 they have added some new features so they have a lower performance/watt which is quite near Nvidia's power draw. I think Hopper is at 700W and TPU are around 600W.
    • YetAnotherNick 2y ago
      Power draw doesn't matter in cloud. All that matters is performance/price for the task in hand.
      • jeffbee 2y ago
        In the cloud, opex == energy.
        • talldayo 2y ago
          Which is why you don't send 4 TPUs to do 1 GPU's job.
          • jeffbee 2y ago
            If the TPUs cost less, that implies that they draw less power. If they cost more then nobody will use them.
• ilove196884 2y ago
  They are overhyped and not as performant as Nvidia regardless of marketing.
  • deleted 2y ago
    [deleted]