4 ms·

Quick question: Why? What does NVIDIA have to gain by forbidding people from using GeForce for ML in datacenters?

cma 9y ago
Price discrimination. Because they aren't getting effective competition from AMD right now they can sell to multiple tiers of customer for different prices. AMD is competitive in crypto, so they exempted crypto from the ban.
- tgtweak 9y ago
  No, it's only because the gp106/gp104 purpose built mining cards use geforce drivers.
  - cma 9y ago
    Seems like there would just be an exemption for them otherwise.
dragontamer 9y ago
The Titan V is $3000 (GeForce) The Tesla V100 is $17,000: http://www.nextwarehouse.com/item/?2782569_g10e http://www.nextwarehouse.com/item/?2782569_g10e We're talking the difference of ~5.5x the price here for otherwise similar cards.
- andars 9y ago
  But you can already not buy a datacenter's worth of GeForce; they have limitations on the quantity per customer. That seems like a far more effective measure than this clause.
  - kuschku 9y ago
    And yet, many datacenters run hundreds or thousands of GeForce cards. Hetzner, for example, rents out an i7-6700, GTX 1080, 64GBRAM for 117€/month
- shaklee3 9y ago
  The V100 is $8000 retail. The site you linked is incorrect.
  - andars 9y ago
    It probably depends a lot. There is the SXM vs. PCIe distinction plus discounts for bulk and all kinds of negotiations.
    - shaklee3 9y ago
      No, it's $8000 if you want to buy one now. https://www.thinkmate.com/product/nvidia/900-2g500-0000-000 https://www.thinkmate.com/product/nvidia/900-2g500-0000-000
- krautt 9y ago
  I've spent some time training tensorflow cnns on nvidia 1080gtx. It works pretty well, but i couldn't reliably train production models for work on it. it's just too flaky. I mentioned this to our 'trustworthy' dell rep who suggested that a good v100 suite would surely solve all the reliability problems, what with it's greatly increased memory bus, or something...
  - jamesblonde 9y ago
    You mean your algorithm is too flaky. The hardware is fine. A V100 won't fix your convergence problems.
    - krautt 9y ago
      yes. salesman bullshit. i think it was tensorflow that was flaky.
  - suresk 9y ago
    CNN models being "flaky" on GeForce hardware isn't something I've heard of? NVIDIA has made some deliberate decisions to make GeForce cards less attractive for deep learning in terms of performance, but I don't think making them produce incorrect results is in their best interest. What hardware did you test this against?
- Patrick_Devine 9y ago
  When I was running my p-2-p startup I wanted to try capitalizing on this. It's the reason why GPU instances cost an arm and a leg.
dnautics 9y ago
NVIDIA is planning on standing up its own deep learning datacenters to compete with amazon, GCP, Azure, because instead of selling a commodity, they'd rather sell a service (better margins).
dcchambers 9y ago
They want to push their expensive enterprise cards (Tesla series) which are exempt from this rule (applies only to "GeForc" cards) and they are probably getting ready to push their own ML service with their own datacenters.

cma 9y ago

Price discrimination. Because they aren't getting effective competition from AMD right now they can sell to multiple tiers of customer for different prices. AMD is competitive in crypto, so they exempted crypto from the ban.

tgtweak 9y ago
No, it's only because the gp106/gp104 purpose built mining cards use geforce drivers.
- cma 9y ago
  Seems like there would just be an exemption for them otherwise.

dragontamer 9y ago

The Titan V is $3000 (GeForce) The Tesla V100 is $17,000: http://www.nextwarehouse.com/item/?2782569_g10e http://www.nextwarehouse.com/item/?2782569_g10e We're talking the difference of ~5.5x the price here for otherwise similar cards.

andars 9y ago
But you can already not buy a datacenter's worth of GeForce; they have limitations on the quantity per customer. That seems like a far more effective measure than this clause.
- kuschku 9y ago
  And yet, many datacenters run hundreds or thousands of GeForce cards. Hetzner, for example, rents out an i7-6700, GTX 1080, 64GBRAM for 117€/month
shaklee3 9y ago
The V100 is $8000 retail. The site you linked is incorrect.
- andars 9y ago
  It probably depends a lot. There is the SXM vs. PCIe distinction plus discounts for bulk and all kinds of negotiations.
  - shaklee3 9y ago
    No, it's $8000 if you want to buy one now. https://www.thinkmate.com/product/nvidia/900-2g500-0000-000 https://www.thinkmate.com/product/nvidia/900-2g500-0000-000
krautt 9y ago
I've spent some time training tensorflow cnns on nvidia 1080gtx. It works pretty well, but i couldn't reliably train production models for work on it. it's just too flaky. I mentioned this to our 'trustworthy' dell rep who suggested that a good v100 suite would surely solve all the reliability problems, what with it's greatly increased memory bus, or something...
- jamesblonde 9y ago
  You mean your algorithm is too flaky. The hardware is fine. A V100 won't fix your convergence problems.
  - krautt 9y ago
    yes. salesman bullshit. i think it was tensorflow that was flaky.
- suresk 9y ago
  CNN models being "flaky" on GeForce hardware isn't something I've heard of? NVIDIA has made some deliberate decisions to make GeForce cards less attractive for deep learning in terms of performance, but I don't think making them produce incorrect results is in their best interest. What hardware did you test this against?
Patrick_Devine 9y ago
When I was running my p-2-p startup I wanted to try capitalizing on this. It's the reason why GPU instances cost an arm and a leg.

dnautics 9y ago

NVIDIA is planning on standing up its own deep learning datacenters to compete with amazon, GCP, Azure, because instead of selling a commodity, they'd rather sell a service (better margins).

dcchambers 9y ago

They want to push their expensive enterprise cards (Tesla series) which are exempt from this rule (applies only to "GeForc" cards) and they are probably getting ready to push their own ML service with their own datacenters.