Nvidia Announces Titan Xp Graphics Card(techcrunch.com) |
Nvidia Announces Titan Xp Graphics Card(techcrunch.com) |
Makes it great for selling old laptops for the same price as the new one.
Bravo Nvidia LOL
I had to check if my hacker news app wasn't updated with the newest post list since April Fools.
"Here's the new iPad. It's newer than the old one, but good luck trying to tell the difference. So to clear things up, we've named it the "iPad". You can thank us later.".
https://en.wikipedia.org/wiki/GeForce_10_series#GeForce_10_....
edit: The Tesla K20 is also in competition in my view (despite the much higher cost) due to its focus on higher double-precision performance.
General thoughts: Don't expect to get _any_ information out of NVidia unless you are running everything on their hardware compatibility lists (i.e. server-case) Do not mix & match consumer-rated gear with 'professional' gear. (i.e. If you put the K80 in a system with a GTX1080, then the Nvidia drivers restrict the number of available processing cores to 2 per device)
Air-flow: The Tesla's run HOT even with a blower attached, and/or installed in the recommended case.
NVENC: the Pascal-based cards performance is incredibly faster AND better then the Kepler-based cards.
For anybody else doing Video encoding work: Grab an Nvidia TK1/jetson dev-kit. This little card is a MONSTER and can handle everything we throw at it without breaking a sweat.
>We have reached out to Nvidia for a statement about compatibility down the line with lesser 10-series cards, and I’m happy to report that Nvidia states that all Pascal-based GPUs will be Mac-enabled via upcoming drivers. This means that you will be able to use a GTX 1080, for instance, on a Mac system via an eGPU setup, or with a Hackintosh build.
https://9to5mac.com/2017/04/06/nvidia-titan-xp-beta-pascal-d...
If your workload can be efficiently split between multiple cards then a $1400 pair of 1080tis will vastly outperform a $1200 Titan Xp - 16% more money for nearly double the throughput.
I was more curious if Titans had some lower precision data type or better dataset packing than vanilla Pascals, or something similar that would help with ML.
I was about to ask "What is it we expect from GPU companies in the next ten years? Why/how will they dominate computing/innovate in ways that we care/compute?"
you answered my question. I think now is the time to invest in Nvidia and any other GPU manufacturer as the ML/DL/AI field is on the precipice to explode computing in the next 15 years. (15 years happens much faster than you might think)
That said, NVDA's stock just went 7% down due to an analyst's downgrade (which in the long run is relatively meaningless), so if you'd want to buy NVDA stock and hold it long term now might be a good time.
Disclosure: I am long NVDA, and my stock picking track record is atrocious.
https://www.newegg.com/Product/Product.aspx?Item=N82E1681413...
If you're doing memory intensive stuff, NVIDIA wants you to spend a whole lot more.
I mean its certainly better than the Titan X (Maxwell) which could only address less than half it's memory while running at 60Hz.
It just seems like an effort to inflate the price of the product without adding much value.
You're in the game and look at a house, then you turn around an look at a tree, so you need the geometry and texture of the tree, but no longer of the house. Then you look down and a chicken walks into frame, so you now need that, you kill the chicken and suddenly need the dying chicken animation etc.
Almost never you need all data for a single frame. That would be way too much work for the render pipeline anyway.
(Will believe the new Mac Pro when I see it, but It's likely to have AMD cards)
http://www.nvidia.co.uk/download/driverResults.aspx/117771/e...
I've the original Titan X Pascal and Tensorflow works great with some old driver version, I even forgot which.
The GTX 1060 is also a good card if you're looking to game at 1080p (which most people still are). It can be found for around $200.
you might be able to find some 2013 Titans for $400.
This is one of my biggest feature requests for Apple. I want a tiny little laptop with an integrated GPU when I'm on the road. But when I'm home I also want to be able to run simulations, play games on a big screen & do VR. And for that I want a desktop class GPU when I'm at home. And I want that GPU to be upgradable - CPU speed isn't improving anywhere near as fast as GPU speed, so it makes sense to keep the rest of my system across multiple GPU generations.
The laptops are already there. The RAM fiasco aside, the current laptops are fine little machines. And with thunderbolt 3 they should have no problem supporting external GPUs.
All thats missing is an official apple egpu enclosure and software support! People on the internet have already gotten them working via injecting kexts into the kernel. But first party support would make the whole thing way better, and way more stable. C'mon apple! We're so close! Take my money!
*Still rocking a Mid 2010 Mac Pro since the 2013 model was underwhelming and nothing has been released since.
Comparable on paper perhaps, but Nvidia architectures tend to get more actual gaming performance per FLOP than AMD architectures do.
i.e. the AMD RX480 (5.8 TFLOPS) is 30% faster than the NV GTX1060 (4.4 TFLOPS) on paper, but in practice they perform more or less the same.
Even in titles where AMD performs especially well the advantage is around 10-15% in favor of the RX480 - still less than the specs would suggest.
In case you are just checking on your comment thread, do check the other threads as there's interesting performance comparisons being discussed.
Of course that since my foremost interest is computation, then NVIDIA it is. But if you just want to game, AMD gives a better bang for the buck.
This is a very good explanation/speculation which deals with the NV driver optimization for DX11 where they break up the draw calls between threads because the scheduler is software based where AMD is hardware based and can't do the same. In DX12 this isn't needed so AMD scheduler being hardware based can be better utilized.
Check https://www.newegg.com/Product/ProductList.aspx?N=100007709%... for instance. While some have their own separate model, it's not usually what people buying the card look at.
The stock price reflects the market's overall expectation of all future cash flows discounted to the present value - all the way out to infinity.
When you buy at stock, you are implicitly betting that reality will exceed those expectations.
So, you just said that massive growth is priced in, and the fact you hold the stock implies that you are betting on even more massive growth - relative to expectations.
The main risks to this thesis are that 1. DL could turn out to be a short-lived trend or 2. a new semiconductor technology emerges that is 10x better suited for DL/ML (Google's new architecture?)
I think brain-space is the safer investment if we are not talking about getting rich from the stock but enrichening the cyber-sphere from that which can be developed in the ML/DL/AI space...
Once of the biggest requests from Mac Pro users was the ability to at least have the option to use NVIDIA cards. I had a Macbook pro (2013 i think) which had a dedicated NVIDIA graphics card in it, and then i upgraded to a 2015 model Macbook pro with the same specs (100% decked out) and noticed a huge drop in graphics performence due to the fact that I was stuck with the AMD graphics card (as Apple had dropped NVIDIA as their dedicated graphics card provider during those years). I really regretted upgrading my Mac and wish i could have my older one back which had the NVIDIA card.
Huh? Not sure what exactly do you mean by "number of processing cores"?
I use two development boxes on a regular basis with Teslas side-by-side with GeForce cards and they all work just fine.
That was not CUDA, the task I was working on specifically (and only) used the NVENC encoder (via ffmpeg). I don't know if the situation has changed but these were my observations.
All of my tests were done in 2015, so the situation might be different now.
The k80 could output upto 4 "streams" (aka outputs or threads) at once. A 780Ti can only do 2. According to nvidia-smi the K80 "appears" to be 2 GPU's on one card. You can actually designate which GPU you want to process ffmpeg streams on.
As soon as you had both devices installed in the same PC, the Nvidia drivers disabled the output of the K80 so that it too would only output upto 2 streams per GPU.
IIRC, there was even a status message that got displayed when installing the Nvidia binary blob:
paraphrasing from memory from 3 years ago
Warning consumer card detected. Limiting available GPU's
Here is a copy/paste dump of my findings at that time. (The formatting is screwy with the nvidia-smi optput.)
=====================================================
Four threads running this:
ffmpeg -i 1784457.mp4 -c:v nvenc -c:a aac -strict experimental -gpu 0 -b:v 21700k -b:a 128k -y delete_me<#>.mp4
gives us ~5-6fps
and uses 3105MiB / 11519MiB of GPU RAM (755MiB for each thread)
------------------------------------------------------------
One thread running this:
ffmpeg -i 1784457.mp4 -c:v nvenc -c:a aac -strict experimental -gpu 0 -b:v 21700k -b:a 128k -y delete_me1.mp4
gives us ~16-18fps
and uses 755MiB of GPU RAM
------------------------------------------------------------
Four threads spread out using both 'GPUs':
ffmpeg -i 1784457.mp4 -c:v nvenc -c:a aac -strict experimental -gpu 0 -b:v 21700k -b:a 128k -y delete_me1.mp4
ffmpeg -i 1784457.mp4 -c:v nvenc -c:a aac -strict experimental -gpu 0 -b:v 21700k -b:a 128k -y delete_me2.mp4
ffmpeg -i 1784457.mp4 -c:v nvenc -c:a aac -strict experimental -gpu 1 -b:v 21700k -b:a 128k -y delete_me3.mp4
ffmpeg -i 1784457.mp4 -c:v nvenc -c:a aac -strict experimental -gpu 1 -b:v 21700k -b:a 128k -y delete_me4.mp4
gives us ~11fps
nvidia-smi results:
Fri May 1 12:38:21 2015 +------------------------------------------------------+ | NVIDIA-SMI 346.46 Driver Version: 346.46 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K80 Off | 0000:03:00.0 Off | 0 | | N/A 69C P0 67W / 149W | 1581MiB / 11519MiB | 4% Default | +-------------------------------+----------------------+----------------------+ | 1 Tesla K80 Off | 0000:04:00.0 Off | 0 | | N/A 58C P0 78W / 149W | 1581MiB / 11519MiB | 6% Default | +-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 2472 C ffmpeg 755MiB | | 0 2477 C ffmpeg 755MiB | | 1 2480 C ffmpeg 755MiB | | 1 2483 C ffmpeg 755MiB | +-----------------------------------------------------------------------------+
aside: That was my personal golden age of gaming... Intel had an OC-48 to SC-5 building... so playing UO on 6 machines simultaneously when everyone else was modeming at 56K made us like gods against lag in that game... I still think fondly of that time.
https://www.newegg.com/Product/Product.aspx?Item=N82E1681412...