5 ms·

Its because NVIDIA requires Teslas in the cloud, which while more powerful than 2080 ti's, cost roughly 6-8x as much.

Analemma_ 7y ago
How does that work? If I buy a 2080 Ti and then decide to use it in an internet-hosted box that I rent out to other people, the first-sale doctrine should tell Nvidia to piss off if they have a problem with it.
- ptomato 7y ago
  Because nvidia's driver licensing terms require you not to be using the consumer stuff in a datacenter.
  - lliamander 7y ago
    What if you're running Linux with the nouveau driver?
    - sseveran 7y ago
      It's irrelevant. You need the CUDA SDK and the CuDNN SDK both of which have license agreements. Edit: You may also need the official Nvidia driver. I have never run anything with the OSS drivers.
      - lliamander 7y ago
        Gotcha
- angry_octet 7y ago
  When you are operating at any kind of scale you need the Telsa packaging (airflow, connectors, power) and testing (running many in a system). The consumer desktop cards cause lots of trouble. Also the consumer cards have artificially small memory sizes (e.g. 11GB max) which painfully constrains DL jobs. No manufacturer is allowed to build Tesla-like cards. Theoretically AMD could crush the nVidia profit margins by releasing cheap data center boards, but their developer support is rubbish and they want that high margin cash too.
  - fit2rule 7y ago
    .. what this means, is that Apple is competing against the Titan/NVIDIA hardware designers to make better compute power available at scale, and in a way which makes sense across consumer/pro boundaries. In that context, the Mac Pro doesn't sound too bad a proposition. I say that as someone who recently built a dual-Titan/AMD Ryzen system, and while the pain of the build is almost gone away .. I do lust after that sexy Apple box, being plug 'n play and all ..
    - angry_octet 7y ago
      Intel competes with nVidia on high end GPU hardware and so far (KL and KF) are doing an abysmal job of it, despite using their CPU control to lock nVidia out of the CPU bus. Apple can't really compete except in mobile GPUs, which is all about power constraints set to single digits Watts, vs >100W for server. That isn't all bad for Apple, there is lots of inference to be done, but by refusing nVidia hardware they impose a developer hurdle. I'm looking forward to people putting 2080s into the new Mac Pro and seeing how reliable it is.
  - zenography 7y ago
    Nvidia wouldn't have prohibited people from using GeForce cards in data centers if people weren't do it.
    - angry_octet 7y ago
      People do try, but it isn't the basis for a successful computing platform. Works for block chain (though AMD delivers more flops/$ for simple sha-ing grunt work) and specific workloads where you have time and expertise but not much cash. As for nVidia banning it, if you've the cash you can buy cards, just becomes painful. nVidia were doing it more because the GPGPU demand was choking the supply to graphics customers, and later dumping obsolete (for crunching) cards on the market en masse, interfering with the mid-tier card sales channel.
- ZeroCool2u 7y ago
  I believe they get around this by applying the restrictions to the software licensing for the CUDA libraries you have to download separately when installing things like TensorFlow.

How does that work? If I buy a 2080 Ti and then decide to use it in an internet-hosted box that I rent out to other people, the first-sale doctrine should tell Nvidia to piss off if they have a problem with it.

ptomato 7y ago
Because nvidia's driver licensing terms require you not to be using the consumer stuff in a datacenter.
- lliamander 7y ago
  What if you're running Linux with the nouveau driver?
  - sseveran 7y ago
    It's irrelevant. You need the CUDA SDK and the CuDNN SDK both of which have license agreements. Edit: You may also need the official Nvidia driver. I have never run anything with the OSS drivers.
    - lliamander 7y ago
      Gotcha
angry_octet 7y ago
When you are operating at any kind of scale you need the Telsa packaging (airflow, connectors, power) and testing (running many in a system). The consumer desktop cards cause lots of trouble. Also the consumer cards have artificially small memory sizes (e.g. 11GB max) which painfully constrains DL jobs. No manufacturer is allowed to build Tesla-like cards. Theoretically AMD could crush the nVidia profit margins by releasing cheap data center boards, but their developer support is rubbish and they want that high margin cash too.
- fit2rule 7y ago
  .. what this means, is that Apple is competing against the Titan/NVIDIA hardware designers to make better compute power available at scale, and in a way which makes sense across consumer/pro boundaries. In that context, the Mac Pro doesn't sound too bad a proposition. I say that as someone who recently built a dual-Titan/AMD Ryzen system, and while the pain of the build is almost gone away .. I do lust after that sexy Apple box, being plug 'n play and all ..
  - angry_octet 7y ago
    Intel competes with nVidia on high end GPU hardware and so far (KL and KF) are doing an abysmal job of it, despite using their CPU control to lock nVidia out of the CPU bus. Apple can't really compete except in mobile GPUs, which is all about power constraints set to single digits Watts, vs >100W for server. That isn't all bad for Apple, there is lots of inference to be done, but by refusing nVidia hardware they impose a developer hurdle. I'm looking forward to people putting 2080s into the new Mac Pro and seeing how reliable it is.
- zenography 7y ago
  Nvidia wouldn't have prohibited people from using GeForce cards in data centers if people weren't do it.
  - angry_octet 7y ago
    People do try, but it isn't the basis for a successful computing platform. Works for block chain (though AMD delivers more flops/$ for simple sha-ing grunt work) and specific workloads where you have time and expertise but not much cash. As for nVidia banning it, if you've the cash you can buy cards, just becomes painful. nVidia were doing it more because the GPGPU demand was choking the supply to graphics customers, and later dumping obsolete (for crunching) cards on the market en masse, interfering with the mid-tier card sales channel.
ZeroCool2u 7y ago
I believe they get around this by applying the restrictions to the software licensing for the CUDA libraries you have to download separately when installing things like TensorFlow.