141–150 of 392 posts
Kinda disappointed that there is no improvement—in fact a small reduction—of the raw GFLOPS computing power, compared to Volta: https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces...
Hmm, 24%-42% performance increase (14/16TFlops vs 11.3TFlops) for 70-90% price increase... And prices were already inflated by crypto that is now collapsing. Not sure who is the target market for this tech honestly. Still only 11GB RAM, even if 50% faster, making it a nonsense purchase for Deep Learning enthusiasts (state-of-art models are already larger). Unless somebody invented RTX-based coin of course, then this…
I think it will still be popular for DL. The models themselves will fit and batches can be distributed over multiple cards during training.
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Where are you getting a 70-90% price increase? 1080s are ~$450, will now be $799. 1070s are ~$400, will now be $599. (I'm going off the top hits on Newegg)
For the 1080, by your numbers, $799/$450 represents a ~78% price increase.
Putting AI/ML aside, I have a question for graphics experts or/and gamers: what is the practical [1] ceiling of the current champion, the 1080ti? I was under impression that that card already was much more than most people needed. In other words, is the new generation only useful for 4K gaming? [1] By "practical" I mean on 1440p@144Hz monitors.
For me, it's around ~45-50fps@4k for a good set of games, and often ~100-120fps@144hz for me. 1000$ is a steep price for the 2080TI, although depending on the 2080 performance it might be the only card which can and will continue to reliably hit 60fps@4k. Not upping ram with such a huge price increase is absolutely absurd.
Do you mean @1440p?
I feel like there is a hard limit on rendering tech right now. We saw 4k upgrades to the PS4/xbox and next gen consoles might be a way off. I thought the gaming industry was waiting for hardware that would let you render Marvel movie style graphics. Things that just barely hit that uncanny divide; that would give the player comic book film style immersions. The demo video they posted does look pretty impressive. Is t…
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>Not sure who is the target market for this tech honestly. These are gaming GPUs being announced at a games conference to an audience of gamers and games journalists. The focus of Huang's talk was how accelerated ray tracing will improve the graphical fidelity of games. GPU compute is a spin-off from gaming. Gamers remain the primary market. They generate the volume that allows Nvidia to sell a supercomputer on a car…
The point was different - it is way too expensive for regular gamers, 2080Ti likely won't be able to do 4k@60Hz like 1080Ti couldn't either and 10 games featuring RTX in the nearly future is not a sufficient draw, especially when some of the effects now look subjectively worse with RTX ON (see those shadows from multiple lights above dancing people from Jenson's demo). So the question remains - who is the real target…
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Then most likely they would not be successful in their businesses; training older models or newer ones with a batch of 1 is not a recipe for success these days when you more like need 100s of GPUs running slight modifications to models in parallel to find the one that works.
Most people I know who publish in NIPS/ICLR train their models using between 1-4 GPUs under their desk or 8 GPUs in a server. I would argue that these folks make competitive models and "are successful in their business" with only 1-8 GPUs. While having access to hundreds of GPUs helps you explore model space more quickly, it's not a hard pre-requisite to success in Deep Learning.
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Then most likely they would not be successful in their businesses; training older models or newer ones with a batch of 1 is not a recipe for success these days when you more like need 100s of GPUs running slight modifications to models in parallel to find the one that works.
Most people I know who publish in NIPS/ICLR train their models using between 1-4 GPUs under their desk or 8 GPUs in a server. I would argue that these folks make competitive models and "are successful in their business" with only 1-8 GPUs. While having access to hundreds of GPUs helps you explore model space more quickly, it's not a hard pre-requisite to success in Deep Learning.
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2080 Ti FE, 1259 Euros. All US companies follow this simple formula: $ to Euro 1:1, add 25% and then add 50-60 Euros for Free Shipping.
It's the 2-year warranty, translation of manuals and a need to have office in all major EU markets due to different legal stuff that makes it all pricier.