21–30 of 132 posts
AMD will enter the game soon once they get their software working, Intel will follow.
I suspect that Nvidia will respond with its own specialized machine learning and inference chips to match the cost/performance ratio. As long as Nvidia can maintain high manufacturing volumes and small performance edge, they can still make good profits.
Earlier quoted context omitted.
AMD - Where does their hardware stand in the race for ML? What changes would AMD need to make to be competitive?
Their hardware is fine. Their software is starting to get good too now. They're finishing MIOpen, a set of CUDA compatible libraries with which you can use Tensorflow (TF uses the builtin CUDA libs too, not only CUDA itself, as does CNTK). ROCm provides a CUDA implementation for AMD systems.
Can't you just buy some 1080s for cheaper than this. I understand there is electricity and hosting costs, but cloud computing seems expensive compared to buying equipment.
Earlier quoted context omitted.
It's more a comparison of AWS vs. Google Cloud pricing than Nvidia vs. TPUv2.
Strongly disagree. If Google is able to offer at about 1/2 the cost using their own silicon versus AWS using Nvidia that is all about the silicon difference. But we also have the V1 TPU paper and can see the TPUs are able to use less joules per inference compared to an older Nvidia architecture. Was not that close. Just makes sense Google V2 TPUs would do the same. Hope Google does a V3 TPU and then will share a V2 T…
>For GPUs, there are further interesting options to consider next to buying. For example, Cirrascale offers monthly rentals of a server with four V100 GPUs for around $7.5k (~$10.3 per hour). However, further benchmarks are required to allow a direct comparison since the hardware differs from that on AWS (type of CPU, memory, NVLink support etc.). Can't you just buy some 1080s for cheaper than this. I understand ther…
I wonder whether NVLink would make any difference for Resnet-50. Does anyone know whether these implementations require any inter-GPU communication?
Hi, author here. The motivation for this article came out of the HN discussion on a previous post ( https://news.ycombinator.com/item?id=16447096 ). There was a lot of valuable feedback - thanks for that. Happy to answer questions!
I found it interesting that they are so close together in performance - I mean what are the odds that they end up within 2% of each other?
That is not all that close is it?
Earlier quoted context omitted.
Strongly disagree. If Google is able to offer at about 1/2 the cost using their own silicon versus AWS using Nvidia that is all about the silicon difference. But we also have the V1 TPU paper and can see the TPUs are able to use less joules per inference compared to an older Nvidia architecture. Was not that close. Just makes sense Google V2 TPUs would do the same. Hope Google does a V3 TPU and then will share a V2 T…
Maybe, maybe not. They have the advantage that they make the hardware, so they're not paying as much retail as nvidia is charging them for their cards. I don't think there's any way you can say the TPU is cheaper compared to buying your own system. If Google decides to release it to the public, that's a different story. Also, keep in mind that Google allows you to mix and match the CPU core count to GPU, whereas AWS…
Yes I can say it is a lot cheaper. That is what this article is all about.
You can do about twice the images per dollar using the TPUs with GCP versus using Nvidia with AWS.
Or what am I missing?
BTW, Google has released to the general public. What are you talking about?
"Google’s AI chips are now open for public use"
https://venturebeat.com/2018/02/12/googles-ai-chips-are-now-...
A bit odd that the TPUs are provisioned on such a weaker machine compared to the V100s, especially when there were comparisons which included augmentation and other processing outside of the TPU.
Great work, RiseML. This benchmark is sincerely appreciated. I wonder whether NVLink would make any difference for Resnet-50. Does anyone know whether these implementations require any inter-GPU communication?