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• GeoffreyA - Monday, March 18, 2024 - link

  Hmm, I wonder what Mr. Nobody-needs-to-learn-programming-any-more has got to say... Reply

• GeoffreyA - Monday, March 18, 2024 - link

  "To help the world build these bigger systems, Nvidia has to build them first"
  
  Our Benefactor, Nvidia. Reply

• Dante Verizon - Monday, March 18, 2024 - link

  A lot of AI-related propaganda that I don't care about, but it was surprising to learn that they're still using 4nm and not 3nm. Hmm. What would have happened? Apple? Reply

• stadisticado - Tuesday, March 19, 2024 - link

  Yield. Its a full reticle die. Even if N3 is healthy, it might not be healthy enough to make the unit economics work. Reply

• LordSojar - Tuesday, March 19, 2024 - link

  Propaganda is the keyword that you aren't paying close attention. It's okay... you'll get it eventually. Reply

• Terry_Craig - Monday, March 18, 2024 - link

  The performance shown FP8 and below is with sparsity, which is why such big and pompous numbers are announced in the slides. Ugly. The actual performance is half of it lol Reply

• TomWomack - Monday, March 18, 2024 - link

  I think from the photo it’s nine rack units of switches and 18 units of GB200 nodes, rather than nine and eighteen whole racks! Odd to have ten nodes above the switches and eight below. Reply

• Ryan Smith - Monday, March 18, 2024 - link

  Correct. That's 18 RUs of GB200 nodes, and another 9 RUs of NVSwitches. Reply

• James5mith - Tuesday, March 19, 2024 - link

  "04:51PM EDT - Traijning GPT-MoE-18.T would take 90 days on a 8000 GPU GH100 system consuming 15W"
  
  Now THAT is the kind of efficiency that nVidia should be striving for. lol Reply

• tomjames1966 - Tuesday, March 19, 2024 - link

  With the data rates coming from these GPUS (10Tbps?!) aren't these things going to require a metric tonne of 800G transceivers? Or will 400G cut it? Reply