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It's fun when then you read last Nvidia tweet [1] suggesting that still their tech is better, based on pure vibes as anything in the (Gen)AI-era. [1] https://x

by mrbungie 11mo ago

It's fun when then you read last Nvidia tweet [1] suggesting that still their tech is better, based on pure vibes as anything in the (Gen)AI-era. [1] https://x.com/nvidianewsroom/status/1993364210948936055 https://x.com/nvidianewsroom/status/1993364210948936055

almostgotcaught 11mo ago
> NVIDIA is a generation ahead of the industry a generation is 6 months
- wmf 11mo ago
  For GPUs a generation is 1-2 years.
  - almostgotcaught 11mo ago
    no https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces...
    - Arainach 11mo ago
      What in that article makes you think a generation is shorter? * Turing: September 2018 * Ampere: May 2020 * Hopper: March 2022 * Lovelace (designed to work with Hopper): October 2022 * Blackwell: November 2024 * Next: December 2025 or later With a single exception for Lovelace (arguably not a generation), there are multiple years between generations.
bigyabai 11mo ago
> based on pure vibes The tweet gives their justification; CUDA isn't ASIC. Nvidia GPUs were popular for crypto mining, protein folding, and now AI inference too. TPUs are tensor ASICs. FWIW I'm inclined to agree with Nvidia here. Scaling up a systolic array is impressive but nothing new.
- foota 11mo ago
  Sure, but their company's 4.3 trillion valuation isn't based on how good their GPUs are for general purpose computing, it's based on how good they are at AI.
qcnguy 11mo ago
Not vibes. TPUs have fallen behind or had to be redesigned from scratch many times as neural architectures and workloads evolved, whereas the more general purpose GPUs kept on trucking and building on their prior investments. There's a good reason so much research is done on Nvidia clusters and not TPU clusters. TPU has often turned out to be over-specialized and Nvidia are pointing that out.
- pests 11mo ago
  You say that like I d a bad thing. Nvidia architectures keep changing and getting more advanced as well, with specialized tensor operations, different accumulators and caches, etc. I see no issue with progress.
  - oivey 11mo ago
    That’s missing the point. Things like tensor cores were added in parallel with improvements to existing computer and CUDA kernels from 10 years ago generally run without modification. Hardware architecture may change, but Nvidia has largely avoided changing how you interact with it.
    - saagarjha 11mo ago
      Modern CUDA programs that hit roofline look absolutely nothing like those from 10 or even 5 years ago. Or even 2 if you’re on Blackwell.
      - bigyabai 11mo ago
        They don't have to, CUDA is a high-level API in this respect. The hardware will conform to the demands of the market and the software will support whatever the compute capability defines, Nvidia is clearer than most about this.
      - qcnguy 11mo ago
        But for research you often don't have to max out the hardware right away. And the question is what do programs that max out Ironwood look like vs TPU programs written 5 years ago?

almostgotcaught 11mo ago

> NVIDIA is a generation ahead of the industry a generation is 6 months

wmf 11mo ago
For GPUs a generation is 1-2 years.
- almostgotcaught 11mo ago
  no https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces...
  - Arainach 11mo ago
    What in that article makes you think a generation is shorter? * Turing: September 2018 * Ampere: May 2020 * Hopper: March 2022 * Lovelace (designed to work with Hopper): October 2022 * Blackwell: November 2024 * Next: December 2025 or later With a single exception for Lovelace (arguably not a generation), there are multiple years between generations.

bigyabai 11mo ago

> based on pure vibes The tweet gives their justification; CUDA isn't ASIC. Nvidia GPUs were popular for crypto mining, protein folding, and now AI inference too. TPUs are tensor ASICs. FWIW I'm inclined to agree with Nvidia here. Scaling up a systolic array is impressive but nothing new.

foota 11mo ago
Sure, but their company's 4.3 trillion valuation isn't based on how good their GPUs are for general purpose computing, it's based on how good they are at AI.

qcnguy 11mo ago

Not vibes. TPUs have fallen behind or had to be redesigned from scratch many times as neural architectures and workloads evolved, whereas the more general purpose GPUs kept on trucking and building on their prior investments. There's a good reason so much research is done on Nvidia clusters and not TPU clusters. TPU has often turned out to be over-specialized and Nvidia are pointing that out.

pests 11mo ago
You say that like I d a bad thing. Nvidia architectures keep changing and getting more advanced as well, with specialized tensor operations, different accumulators and caches, etc. I see no issue with progress.
- oivey 11mo ago
  That’s missing the point. Things like tensor cores were added in parallel with improvements to existing computer and CUDA kernels from 10 years ago generally run without modification. Hardware architecture may change, but Nvidia has largely avoided changing how you interact with it.
  - saagarjha 11mo ago
    Modern CUDA programs that hit roofline look absolutely nothing like those from 10 or even 5 years ago. Or even 2 if you’re on Blackwell.
    - bigyabai 11mo ago
      They don't have to, CUDA is a high-level API in this respect. The hardware will conform to the demands of the market and the software will support whatever the compute capability defines, Nvidia is clearer than most about this.
    - qcnguy 11mo ago
      But for research you often don't have to max out the hardware right away. And the question is what do programs that max out Ironwood look like vs TPU programs written 5 years ago?