Costs $129,000 and needs 3.2 kilowatts to run.
The Nvidia DGX-1 Deep Learning Supercomputer in a Box
21–30 of 106 posts
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#22The unified memory architecture with the Pascal GP100 is pretty sweet. That will make it easier to work with large data sets.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#23How does this compare to some of the systems provided by cloud providers? Seems like requiring an on-site capability is a hurdle for integration if you already have your data on a cloud provider.
[1] https://aws.amazon.com/machine-learning/ [2] https://azure.microsoft.com/en-us/services/machine-learning/
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#24Check out the specs here: http://images.nvidia.com/content/technologies/deep-learning/... though I'm most curious about what motherboard is in there to support NVLink and NVHS. Good overview of Pascal here: https://devblogs.nvidia.com/parallelforall/inside-pascal/ 1 question: will we see NVLink become an open standard for use in/with other coprocessors? 1 gripe: they give relative performance data as compared to a CP…
You mean you're not surprised that a machine with 8 GPUs, apparently costing $129k USD (from comment below), can outperform a single CPU? :) (Of course, a better metric is that it's getting ~56x the performance at probably ~10x the TDP, but that's not surprising for a GPU with the current state of deep learning code.) To their credit, the thermal and power engineering needed to get that dense a compute deployment is…
To be fair, they are comparing it to a dual-socket CPU; which is twice as fair as comparing to a single!!
What I was getting more at was: I want to know the relative performance compared to another 8 Tesla box. I know comparing apples isn't good marketing, but c'mon.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#25I wonder how much faster the new Tesla P100 is compared to the Tesla K40 in training neural networks. The K40s were the best available GPUs for training deep neural networks.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#26Note the P100 is 20 Tflops for half precision (16 bit). For general purpose GPU (I use them for EM simulation) I assume one would want 32-bit, which is 10 Tflops. But still looks much much better for 64-bit computations than the previous generation
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#27I am looking forward to OpenCL catching up with CUDA in maturity and adoption, so that NVidia's monopoly in Silicon for deep learning will come to an end.
What hardware could OpenCL even run on that would come remotely close to what this system has to offer?
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#28How does this compare to some of the systems provided by cloud providers? Seems like requiring an on-site capability is a hurdle for integration if you already have your data on a cloud provider. [1] https://aws.amazon.com/machine-learning/ [2] https://azure.microsoft.com/en-us/services/machine-learning/
I would argue that this box is probably targeted at cloud providers. The Nvidia GRID boards are similar--they're not for consumers, but for GPU/Gaming-as-a-service providers.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#29I am looking forward to OpenCL catching up with CUDA in maturity and adoption, so that NVidia's monopoly in Silicon for deep learning will come to an end.
What monopoly? You totally have a choice, it's just that NVIDIA made a large bet on GPGPU and it is paying off for them. You don't see AMD heavily pushing their cards for compute purposes or developing computational developer relations.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#30I am looking forward to OpenCL catching up with CUDA in maturity and adoption, so that NVidia's monopoly in Silicon for deep learning will come to an end.
What monopoly? You totally have a choice, it's just that NVIDIA made a large bet on GPGPU and it is paying off for them. You don't see AMD heavily pushing their cards for compute purposes or developing computational developer relations.
You often don't have a choice because a large amount of GPGPU software is written using CUDA, which is Nvidia-specific.