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No mention of k80's k40's...
K80s and K40s have a K. That's two generations old. We are currently on P and about to be V. If you think K80s are good, you are far far behind the times in machine learning. A single 1080Ti even in a 4U server outperforms 2 K80s.
We found that the GeForces tend to burn out when under heavy load, whereas we've not had a single Tesla series ever burn out.
Interestingly, AMD is claiming that their upcoming Vega Frontier Editions chips outperform Nvidia's current P100 Deep Learning chip. http://hexus.net/media/uploaded/2017/5/30f5633b-1bbf-49b7-9f...
I think signs are pointing to Vega being a HPC beast. But what they really need is the software ecosystem and support, and so far that hasn't been there. So while the new hardware looks cool, I'm really waiting for an announcement that OpenCL tooling got a lot better, or that CUDA is getting first-party support from AMD, anything to tempt those customers away from nvidia.
What's more interesting is the disruptive change to the underlying programming model for Volta (thread-within-thread) that will break a lot of existing CUDA code without refactoring. So if there's no GeForce Volta option, I suspect a lot of that code won't get refactored. So Vega just needs to not suck and to deliver decent TensorFlow/Torch/Caffe performance to succeed as an alternative to NVIDIAopoly.
tldr: GTX1080ti They're fairly easy to get now too, in the beginning it was rather hard to get them.
tldr: GTX1080ti They're fairly easy to get now too, in the beginning it was rather hard to get them.
You can get an 8GB GTX 1080 for 2/3rds the price and it offers 80% of the performance. If you don't need the 11GB of memory, it's a steal. If you're building a rig with multiple cards, 1080 might be an economical way to get great performance. If you're looking for maximal compute density, 1080 Ti is the way to go.
I'm guessing you don't... you use the cloud, right?
Earlier quoted context omitted.
You can get an 8GB GTX 1080 for 2/3rds the price and it offers 80% of the performance. If you don't need the 11GB of memory, it's a steal. If you're building a rig with multiple cards, 1080 might be an economical way to get great performance. If you're looking for maximal compute density, 1080 Ti is the way to go.
It will also require much more power and cooling this system will rise a cost and reduce stability of the system.
What impact does Google's Tensor Processing Unit have on the answer to this question?
https://www.nvidia.com/en-us/data-center/volta-gpu-architect...
Earlier quoted context omitted.
For a hobbyist, it probably makes sense to just use some GPU instances on AWS. But it doesn't really take that long for it to become cost-effective to buy a GPU. It only takes a couple of months of full-time use before it becomes cost-effective to purchase the GPU. Even if you're building the entire machine from scratch it takes under a year. (At least, that was my estimate when I was building a rig earlier this year…
Not to mention, I can imagine it's frustrating uploading many gigs of data to amazon.
Assuming 2.5MB/s Upload capacity with local data, you've uploaded it to your deep learning machine in half a day (100000 / 2.5 / 3600 ~ 11 hours) - which is not that much, as most of your time will be used for development and fine-tuning of your deep learning tool chain anyway. In most cases the data is accessible via a public-facing service, and assuming 1GBit/s bandwidth you've downloaded 100G in 13 minutes (100000 / 125 / 60 ~ 13 minutes).