Earlier quoted context omitted.
They're not. All that was mentioned in the talk was that this chip is coming soon.
There's a big green "Order Now" button about 1/3 of the way down the page.
And no delivery date.
The Nvidia DGX-1 Deep Learning Supercomputer in a Box
41–50 of 106 posts
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#42Costs $129,000 and needs 3.2 kilowatts to run.
To me, $129k isn't surprising since it is only going to be bought by researchers with big budgets. Small-timers will still build 3x GTX980 systems for under $5k. 3.2 KILOwatts sounded insane to me, but I suppose you'll have your own server rack to put it in if you can afford to buy one of these.
3.2kw isn't that insane considering what you're getting out of it. A coffee pot is 1kw, a toaster is 1.2kw, an electric broiler is 3.6kw. Running costs would be a very tiny part of any budget. Ends up being $9.216/day assuming peak costs, peak usage, and 24h operation.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#43Note 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
Curious. Why do you post here when every other comment is random posturing?
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#44Costs $129,000 and needs 3.2 kilowatts to run.
3.2KW isn't that insane for a server, you can buy high end desktop PSU's of 1.6KW (I'm running a 1200W one) if you are using multiple GPU's, a high end CPU, 32-64GB of memory and loads of storage coupled with overclocking and the substantial cooling required it's not that hard to get to around 1KW power consumption on a high end gaming rig these days.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#45was hoping they would announce Pascal GTX's. Oh well. Computex I guess
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#46Earlier quoted context omitted.
What hardware could OpenCL even run on that would come remotely close to what this system has to offer?
OpenCL runs on Nvidia GPUs, so you could do an apples to apples comparison on this system.
You can, but IIRC Nvidia actually compiles OpenCL to CUDA in the Driver.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#47Check 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…
It looks like it uses a separate daughterboard that houses the GPUs + NVLink, connected to the main motherboard using quad Infiniband EDR (400Gbps) + RDMA. http://images.anandtech.com/doci/10225/SSP_85.JPG
The diagram is confusing, but the GPUs are connected to the NVLink matrix which is connected to the motherboard via the PLX PCIe switches. The quad IB/dual 10GbE are separate IO attached to the motherboard.
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#48Earlier quoted context omitted.
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.
They gave a pseudo-comparison to other GPUs in the keynote
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#49Earlier quoted context omitted.
Wow, cheap, great for startups!
As of last year prices for general HPC resources were running around $3/GFLOP[1], or about $500,000 for 170TFlops if my math is correct. Sounds like this is a significant cost savings if it fits your use case.
Uh, using what hardware? The 980 Ti is about 11 TFLOP in half-precision (apples to apples). So 16x 980 Ti cards would take up twice as much rack space for $11k. Your estimate (and NVIDIA's pricing) is off by more than an order of magnitude...
Re: The Nvidia DGX-1 Deep Learning Supercomputer in a Box
#50$129k for this machine. In the keynote its interesting that they mentioned the product line being: "Tesla M40 for hyperscale, K80 for multi-app HPC, P100 for scales very high, and DGX-1 for the early adopters".
The GP100/P100 with the 16nm process probably gives a considerable performance/power advantage over the Tesla... but this gives me the feeling that we may not see consumer or workstation-level Pascal boards for a while.