Two of my colleagues use high-end AMD GPUs to train RNNs and transformers with tensorflow-rocm. There are still some nasty bugs (e.g. [1]), so it is currently not for everyone. However, given how far they have come compared to 1-2 years ago, it is very likely that in a year or so, they are a real competitor to NVIDIA for compute. That competition was long needed.
[1] https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/...
I wonder if Google is in any way helping AMD in the TensorFlow and ROCm?
What happen when Intel join the GPU race in 2020. Making their own ROCm again?
This is incorrect. The RTX 6000 has 24GB of VRAM and is $4000, and the RTX 8000 has 48GB of VRAM (double the amount) and is $5500. Is it worth the price increase? For a lot of people I know it is.
Also, the RTX Titan is $2500 and is identical to the RTX 6000 (at the chip level) and also with 24GB of VRAM, with the only difference being software enabling of additional H.264/5 encoding features on the Quadro. Definitely not worth the cost increase, especially for anyone doing ML.
[1]: http://fortune.com/2018/01/07/nvidia-consumer-video-cards/
Your knowledge is incomplete. T4 has been available in google cloud for many months.
These are great improvements but are virtually worthless if linode didn't change their behavior.
As far as standards go, we use Linode and all of our customers (some of them quite demanding about internal security details) have been satisfied with the various acronyms they are accredited with... Although I understand this does not necessarily guarantee anything about response behavior, so interested to hear about past incidents.
Not sure if that was isolated.
There was something more recent, too.
Anyway, happy Linode customer for quite a few years now. My stuff works, no fuss.
We made some improvements to our disclosure / Bug Bounty program last year and launched this on HackerOne. The community and quality of submissions has been great. More information: https://blog.linode.com/2018/05/16/linodes-new-bug-bounty-pr...
We've also been making ongoing improvements to our application security and security infrastructure through the implementation of a DevSecOps culture. This is something we take very seriously.
One of the biggest questions is why the Quadro RTX 6000? Few things:
1. Cost it has the same performance as the 8000. The difference is 8 more GB of RAM that comes at a steep premium. Cost is important to us as it allows us to be at a more affordable price point.
2. We have all heard or used the Tesla V100, and it's a great card. The biggest issue is that it's expensive. So one of the things that caught our eye is the RTX 6000 has a fast Single-Precision Performance, Tensor Performance, and INT8 performance. Plus the Quadro RTX supports INT4. https://www.nvidia.com/content/dam/en-zz/Solutions/design-vi... https://images.nvidia.com/content/technologies/volta/pdf/tes... Yes, these are manufactures numbers, but it caused us pause. As always, your mileage may vary.
3. RT cores. This is the first time (TMK) that a cloud provider is bringing RT cores into the market. There are many use cases for RT that have yet to be explored. What will we come up with as a community?!
Now with all that being said, there is a downside, FP64 aka double precision. The Tesla V100 does this very well, whereas the Quadro RTX 6000 does poorly in comparison. We think although those workloads are important, the goal was to find a solution that fits a vast majority of the use cases.
So is the marketing true to get the most out of MI/AI/Etc? Do you need a Tesla to get the best performance? Or is the Tesla starting to show its age? Give the cards a try I think you'll find these new RTX Quadros with Turning architecture are not the same as the Quadros of the past.