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It's nice to see NVidia's behavior here is more widely known now. To this day, I do not understand the justification for asserting that one cannot install GeFo

It's nice to see NVidia's behavior here is more widely known now. To this day, I do not understand the justification for asserting that one cannot install GeForce GPUs wherever one wants to install them. I really wish that Nvidia felt more secure that killer Tesla features like NVLINK and the Tensor Cores were sufficient product differentiation to make purchasing Tesla GPUs worth the price. And that's because to the best of my knowledge, most of the CUDA ecosystem out there was developed on GeForce GPUs. There is currently no GeForce equivalent of Volta at a time when the underlying programming model has undergone some traumatic changes that really alter the way to write efficient code going forward. If the only way to access Volta GPUs turns out to be AWS instances at $25/hour or $150,000 DGX-1V servers plus hosting costs, I suspect a lot of existing CUDA code will bitrot. Imagine a near future where AMD Vega GPUs are faster than GTX 1080 TI at FP16 training and inference for deep learning. Without some sort of successor to that GPU, I really think that could happen because Nvidia went out of its way to cripple FP16 performance on GeForce.

TomV1971 9y ago
You start by talking about Nvidia’s behavior but nowhere in the article is there any mention of that, so you’re leaving us to guess what you’re talking about.
- hfern 9y ago
  Perhaps he is talking about this? > NVIDIA specifically requests that server OEMs not use their GTX cards in servers. Of course, this simply means resellers install the cards before delivering them to customers.
shaklee3 9y ago
If this article is to be believed, they are not happy about people doing this: https://www.pcgamesn.com/nvidia-geforce-server https://www.pcgamesn.com/nvidia-geforce-server And while there is no GeForce equivalent to Volta today, that will not be true in the near future. At some point they will come out with a new GeForce line of cards. In the past, the GeForce generation was either before the Teslas, or just slightly after. I also don't agree that there is nothing competing with the voltage right now on the GeForce line. The 1080ti is not the same performance, but if you are willing to have multiple cards, two of them are just as good or better than the V100.
- dharma1 9y ago
  I hope Volta based gaming cards will happen sometime in 2018 and they don't gimp it too much. V100 kills it with mixed precision compute for deep learning because of the Tensor Cores. If you can get mixed precision training working, you can get 120TFLOPS out of it. You'll need 10x 1080ti's to reach that
ah- 9y ago
Has there been much progress towards making AMD cards work with popular deep learning libraries? There's https://rocm.github.io/index.html https://rocm.github.io/index.html but it's not quite clear to me how far they got and how usable it is today.
- arcanus 9y ago
  There is a vendor supported tensorflow for AMD devices: https://github.com/ROCmSoftwarePlatform/hiptensorflow https://github.com/ROCmSoftwarePlatform/hiptensorflow
wmf 9y ago
Price discrimination works and if NVidia didn't use it they'd probably jack up prices on GeForces instead.
- jrockway 9y ago
  As long as AMD exists, I doubt it.
dharma1 9y ago
1080Ti doesn't have double speed FP16, Vega 64 should be more than twice as fast for half precision training/inference. The problem is that AMD is has been lacking in framework support and fast optimised kernels. This seemed promising - https://news.ycombinator.com/item?id=15516166 https://news.ycombinator.com/item?id=15516166
- scottlegrand2 9y ago
  It's worse than that, the FP16 MAD in GTX 1080 TI is significantly slower than converting 2 FP16 numbers to FP32, performing FP32 math on them, and accumulating results in FP32. Had it been the same speed, I don't think I would be anywhere near as annoyed as I am with this crippling. That said, at Vega's 26 or so FP16 TFLOPS, it wouldn't be hard for NVIDIA to release a GeForce Volta with 30-40 tensor core TFLOPS, that both stomped on Vega and remained significantly inferior to V100. Given how hard it is to program Volta optimally, I'm surprised they haven't done so already, if only as a Titan XV Edition that can only be purchased from their website.
  - dharma1 9y ago
    Yeah, will be interesting to see if they can bring the price of GPUs with tensor cores down, from what I've read it's too expensive to make them right now for consumer market. BTW - do you happen to know what DL frameworks currently support mixed precision training with Volta tensor cores? Curious to see if AWS V100 instances can really do 120 TFLOPS as advertised. I think latest versions of CUDA/CuDNN support Volta now?

TomV1971 9y ago

You start by talking about Nvidia’s behavior but nowhere in the article is there any mention of that, so you’re leaving us to guess what you’re talking about.

hfern 9y ago
Perhaps he is talking about this? > NVIDIA specifically requests that server OEMs not use their GTX cards in servers. Of course, this simply means resellers install the cards before delivering them to customers.

shaklee3 9y ago

If this article is to be believed, they are not happy about people doing this: https://www.pcgamesn.com/nvidia-geforce-server https://www.pcgamesn.com/nvidia-geforce-server And while there is no GeForce equivalent to Volta today, that will not be true in the near future. At some point they will come out with a new GeForce line of cards. In the past, the GeForce generation was either before the Teslas, or just slightly after. I also don't agree that there is nothing competing with the voltage right now on the GeForce line. The 1080ti is not the same performance, but if you are willing to have multiple cards, two of them are just as good or better than the V100.

dharma1 9y ago
I hope Volta based gaming cards will happen sometime in 2018 and they don't gimp it too much. V100 kills it with mixed precision compute for deep learning because of the Tensor Cores. If you can get mixed precision training working, you can get 120TFLOPS out of it. You'll need 10x 1080ti's to reach that

ah- 9y ago

Has there been much progress towards making AMD cards work with popular deep learning libraries? There's https://rocm.github.io/index.html https://rocm.github.io/index.html but it's not quite clear to me how far they got and how usable it is today.

arcanus 9y ago
There is a vendor supported tensorflow for AMD devices: https://github.com/ROCmSoftwarePlatform/hiptensorflow https://github.com/ROCmSoftwarePlatform/hiptensorflow

wmf 9y ago

Price discrimination works and if NVidia didn't use it they'd probably jack up prices on GeForces instead.

jrockway 9y ago
As long as AMD exists, I doubt it.

dharma1 9y ago

1080Ti doesn't have double speed FP16, Vega 64 should be more than twice as fast for half precision training/inference. The problem is that AMD is has been lacking in framework support and fast optimised kernels. This seemed promising - https://news.ycombinator.com/item?id=15516166 https://news.ycombinator.com/item?id=15516166

scottlegrand2 9y ago
It's worse than that, the FP16 MAD in GTX 1080 TI is significantly slower than converting 2 FP16 numbers to FP32, performing FP32 math on them, and accumulating results in FP32. Had it been the same speed, I don't think I would be anywhere near as annoyed as I am with this crippling. That said, at Vega's 26 or so FP16 TFLOPS, it wouldn't be hard for NVIDIA to release a GeForce Volta with 30-40 tensor core TFLOPS, that both stomped on Vega and remained significantly inferior to V100. Given how hard it is to program Volta optimally, I'm surprised they haven't done so already, if only as a Titan XV Edition that can only be purchased from their website.
- dharma1 9y ago
  Yeah, will be interesting to see if they can bring the price of GPUs with tensor cores down, from what I've read it's too expensive to make them right now for consumer market. BTW - do you happen to know what DL frameworks currently support mixed precision training with Volta tensor cores? Curious to see if AWS V100 instances can really do 120 TFLOPS as advertised. I think latest versions of CUDA/CuDNN support Volta now?