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The commodity GeForce Nvidia cards versus the Enterprise Tesla NVidia cards is history repeating itself, with enterprise disks vs commodity disks. We all know w

The commodity GeForce Nvidia cards versus the Enterprise Tesla NVidia cards is history repeating itself, with enterprise disks vs commodity disks. We all know who won in the end (commodity disks, of course). The 1080Ti card is about 60% of the performance of the P100, but costs 700 dollars instead of 5k dollars. Of course, people will try and build these boxes, they have a much higher ROI compared to Nvidia's DGX-1. So what do NVidia do? Try and stop vendors from selling them! https://www.pcgamesn.com/nvidia-geforce-server https://www.pcgamesn.com/nvidia-geforce-server

microcolonel 9y ago
There are some other knobs that NVIDIA will surely try (has surely tried?) to tweak as well, such as making the consumer devices deliberately less power efficient, fusing off chip features which make compute workloads more efficient, or sabotaging the drivers (either in general or on a per-application basis). With disks there was always just as much pressure on the consumer side to keep energy down, cost down, and capacity up, which means that there was no natural segmentation, and no straightforward unnatural segmentation.
- llukas 9y ago
  I think you're vastly underestimating amount of work that goes into those things. What if instead what you suggest they do not invest as hard in gaming parts? Design, QA, QC and support cost money after all.
Jabbles 9y ago
Is the initial cost of purchase the largest factor? Or are running costs enough to tip the balance?
- jamesblonde 9y ago
  Read the article. Running costs can be quite high (up to 1000 dollars/months with colocation/energy), but they are still massively cheaper than running 24x7 the same GPUs in the cloud.
  - Bromskloss 9y ago
    I took the question to be about the choice between the two products.
shaklee3 9y ago
Where did you get that the 1080ti is 60% of the performance of the p100? It is slightly higher performance than the p100, but rated with a lower mtbf. Of course, you end up with many crippled features, like RDMA and dynamic parallelism, which most people probably don't need anyways.
- jamesblonde 9y ago
  Deep learning training is basically 2 phases: the forward pass and the backwards pass (backpropagation). There are no loops. As such, most DL training is memory bound. Memory bandwidth of 1080TI is 484.4 GB/s Memory bandwidth of p100 is 720.9 GB/s Memory bandwidth of v100 is 900.1 GB/s
  - shaklee3 9y ago
    For applications that are not memory bound, the 1080ti is better. Also, if we are talking about deep learning, inference can be handled with lower precision. The p100 does not have int8 support and hardware, but the 1080ti does. So presumably for the same memory bandwidth you get twice as much data.
reitzensteinm 9y ago
It's probably better to analogize it to Xeon, which prints Intel roughly as much cash as the tax revenues of Croatia. There are plenty of things to arbitrarily segment in a GPU - HBM, large memory sizes, tensor cores, FP64. The problem with hard disk segmentation wasn't the idea of segmenting at all, it's that there were no good ways of doing it. A higher MTBF? You're better off buying more, cheaper disks. Density (eg from helium)? Might be worth a 20-50% premium in $/gb, but not 200-500% $/transistor as with Xeon.
deleted 9y ago
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microcolonel 9y ago

There are some other knobs that NVIDIA will surely try (has surely tried?) to tweak as well, such as making the consumer devices deliberately less power efficient, fusing off chip features which make compute workloads more efficient, or sabotaging the drivers (either in general or on a per-application basis). With disks there was always just as much pressure on the consumer side to keep energy down, cost down, and capacity up, which means that there was no natural segmentation, and no straightforward unnatural segmentation.

llukas 9y ago
I think you're vastly underestimating amount of work that goes into those things. What if instead what you suggest they do not invest as hard in gaming parts? Design, QA, QC and support cost money after all.

Jabbles 9y ago

Is the initial cost of purchase the largest factor? Or are running costs enough to tip the balance?

jamesblonde 9y ago
Read the article. Running costs can be quite high (up to 1000 dollars/months with colocation/energy), but they are still massively cheaper than running 24x7 the same GPUs in the cloud.
- Bromskloss 9y ago
  I took the question to be about the choice between the two products.

shaklee3 9y ago

Where did you get that the 1080ti is 60% of the performance of the p100? It is slightly higher performance than the p100, but rated with a lower mtbf. Of course, you end up with many crippled features, like RDMA and dynamic parallelism, which most people probably don't need anyways.

jamesblonde 9y ago
Deep learning training is basically 2 phases: the forward pass and the backwards pass (backpropagation). There are no loops. As such, most DL training is memory bound. Memory bandwidth of 1080TI is 484.4 GB/s Memory bandwidth of p100 is 720.9 GB/s Memory bandwidth of v100 is 900.1 GB/s
- shaklee3 9y ago
  For applications that are not memory bound, the 1080ti is better. Also, if we are talking about deep learning, inference can be handled with lower precision. The p100 does not have int8 support and hardware, but the 1080ti does. So presumably for the same memory bandwidth you get twice as much data.

reitzensteinm 9y ago

It's probably better to analogize it to Xeon, which prints Intel roughly as much cash as the tax revenues of Croatia. There are plenty of things to arbitrarily segment in a GPU - HBM, large memory sizes, tensor cores, FP64. The problem with hard disk segmentation wasn't the idea of segmenting at all, it's that there were no good ways of doing it. A higher MTBF? You're better off buying more, cheaper disks. Density (eg from helium)? Might be worth a 20-50% premium in $/gb, but not 200-500% $/transistor as with Xeon.

deleted 9y ago

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