4 ms·

Not quite the right conclusion. Many of Nvidia's "gaming" cards (e.g., higher-end GTX cards) are in fact used NOT for gaming but for deep learning by numerous

by cs702 10y ago

Not quite the right conclusion. Many of Nvidia's "gaming" cards (e.g., higher-end GTX cards) are in fact used NOT for gaming but for deep learning by numerous AI researchers, developers, and startups with small budgets who buy them through retail channels.

johansch 10y ago
That's indeed possible. But really, if you were to guesstimate the ratio of GPU usage between these items sold to consumers that end up being used by: a) AI/startups b) gamers what would you guess? My guess is somewhere between 1:50 and and 1:500.
- smallnamespace 10y ago
  The difference is that AI/startup users may buy hundreds of GPUs at once.
  - bonzini 10y ago
    I think he is already including that in his 1:50 or 1:500 ratio.
    - johansch 10y ago
      Indeed.
- protomok 10y ago
  Revenue growth is far more interesting to tech investors than revenue over the last few years. Deep learning has a massive potential for revenue growth and total revenue will likely eclipse gaming within the next 10 years. Imagine a world where every car has semi-autonomous technology, the average security camera is doing object detection, localization, more sophisticated language translation, smart drones, etc. The potential for deep learning is undeniable and Nvidia (currently) is leading this race.
  - Eliezer 10y ago
    Except that you mainly need the massive compute for the training, not the commodity execution of the network!
    - petra 10y ago
      Any idea what kind of compute a self-driving car(with a trained network) will need ?
      - dharma1 10y ago
        Depends massively on what the software is doing, there is still no standardisation on what a self-driving car means. Nvidia is pushing PX2 but I have a feeling they are too expensive for most cars - don't know the exact price but "a few thousand dollars" was quoted for the Teslas that use them. Some specs - http://wccftech.com/nvidia-drive-px2-pascal-gtc-2016/ http://wccftech.com/nvidia-drive-px2-pascal-gtc-2016/ At the same time Qualcomm is pushing their much less performant chips for self-driving cars, but at a much lower price point (hundreds of dollars)
      - nl 10y ago
        Something pretty close to this: http://www.nvidia.com/object/drive-px.html http://www.nvidia.com/object/drive-px.html (I believe they are currently recommending the 4 GPU version shown on at the bottom of that page under "DRIVE PX 2 FOR FULLY AUTONOMOUS DRIVING") The key here isn't that this isn't a full power desktop GPU, but it will run inference on a CUDA-compatible kernel. That give NVidia a complete end-to-end modelling, training and driving platform. No one else has that.
  - copenja 10y ago
    Surely you don't expect cameras and drones to have massive, loud, heavy, power draining, $650 dollar graphics cards in them? It seems if they are used in development the sales don't scale anywhere near directly with the number of units sold. Maybe I'm missing something?
    - 10y ago

by cs702 10y ago

johansch 10y ago

That's indeed possible. But really, if you were to guesstimate the ratio of GPU usage between these items sold to consumers that end up being used by: a) AI/startups b) gamers what would you guess? My guess is somewhere between 1:50 and and 1:500.

smallnamespace 10y ago
The difference is that AI/startup users may buy hundreds of GPUs at once.
- bonzini 10y ago
  I think he is already including that in his 1:50 or 1:500 ratio.
  - johansch 10y ago
    Indeed.
protomok 10y ago
Revenue growth is far more interesting to tech investors than revenue over the last few years. Deep learning has a massive potential for revenue growth and total revenue will likely eclipse gaming within the next 10 years. Imagine a world where every car has semi-autonomous technology, the average security camera is doing object detection, localization, more sophisticated language translation, smart drones, etc. The potential for deep learning is undeniable and Nvidia (currently) is leading this race.
- Eliezer 10y ago
  Except that you mainly need the massive compute for the training, not the commodity execution of the network!
  - petra 10y ago
    Any idea what kind of compute a self-driving car(with a trained network) will need ?
    - dharma1 10y ago
      Depends massively on what the software is doing, there is still no standardisation on what a self-driving car means. Nvidia is pushing PX2 but I have a feeling they are too expensive for most cars - don't know the exact price but "a few thousand dollars" was quoted for the Teslas that use them. Some specs - http://wccftech.com/nvidia-drive-px2-pascal-gtc-2016/ http://wccftech.com/nvidia-drive-px2-pascal-gtc-2016/ At the same time Qualcomm is pushing their much less performant chips for self-driving cars, but at a much lower price point (hundreds of dollars)
    - nl 10y ago
      Something pretty close to this: http://www.nvidia.com/object/drive-px.html http://www.nvidia.com/object/drive-px.html (I believe they are currently recommending the 4 GPU version shown on at the bottom of that page under "DRIVE PX 2 FOR FULLY AUTONOMOUS DRIVING") The key here isn't that this isn't a full power desktop GPU, but it will run inference on a CUDA-compatible kernel. That give NVidia a complete end-to-end modelling, training and driving platform. No one else has that.
- copenja 10y ago
  Surely you don't expect cameras and drones to have massive, loud, heavy, power draining, $650 dollar graphics cards in them? It seems if they are used in development the sales don't scale anywhere near directly with the number of units sold. Maybe I'm missing something?
  - 10y ago