Titan v 1080: Nvidia GTX 1080 vs Titan X

GeForce GTX 1080 Ti vs TITAN RTX Graphics cards Comparison

When choosing between GeForce GTX 1080 Ti and TITAN RTX, it is worth examining the specifications of the models in detail. Do they meet the recommended requirements of modern games and software? Storage capacity, form factor, TDP, available ports, warranty and manufacturer support are all important. For example, the size of a PC case can limit the maximum thickness and length of the card. Often, instead of the factory overclocked card and RGB backlight, it is better to choose a reference model with a more efficient GPU. And make sure that your current power supply unit has the correct connection pins (using adapters is not recommended). This GPUs compare tool is meant to help you to choose the best graphics card for your build. Let’s find out the difference between GeForce GTX 1080 Ti and TITAN RTX.

GeForce GTX 1080 Ti

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TITAN RTX

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Main Specs

  GeForce GTX 1080 Ti TITAN RTX
Power consumption (TDP) 250 Watt 280 Watt
Interface PCIe 3. 0 x16 PCIe 3.0 x16
Supplementary power connectors 1x 6-pin + 1x 8-pin 2x 8-pin
Memory type GDDR5X GDDR6
Maximum RAM amount 11 GB 24 GB
Display Connectors 1x HDMI, 3x DisplayPort 1x HDMI, 3x DisplayPort, 1x USB Type-C
Recommended system power (PSU) 600 Watt
 

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  • TITAN RTX has 12% more power consumption, than GeForce GTX 1080 Ti.
  • Both video cards are using PCIe 3.0 x16 interface connection to a motherboard.
  • TITAN RTX has 13 GB more memory, than GeForce GTX 1080 Ti.
  • Both cards are used in Desktops.
  • GeForce GTX 1080 Ti is build with Pascal architecture, and TITAN RTX — with Turing.
  • Core clock speed of GeForce GTX 1080 Ti is 131 MHz higher, than TITAN RTX.
  • GeForce GTX 1080 Ti is manufactured by 16 nm process technology, and TITAN RTX — by 12 nm process technology.
  • TITAN RTX is 257 mm longer, than GeForce GTX 1080 Ti.
  • Memory clock speed of TITAN RTX is 3000 MHz higher, than GeForce GTX 1080 Ti.

Game benchmarks

Assassin’s Creed OdysseyBattlefield 5Call of Duty: WarzoneCounter-Strike: Global OffensiveCyberpunk 2077Dota 2Far Cry 5FortniteForza Horizon 4Grand Theft Auto VMetro ExodusMinecraftPLAYERUNKNOWN’S BATTLEGROUNDSRed Dead Redemption 2The Witcher 3: Wild HuntWorld of Tanks
high / 1080p 75−80 80−85
ultra / 1080p 55−60 60−65
QHD / 1440p 45−50 50−55
4K / 2160p 30−35 35−40
low / 720p 100−105 100−110
medium / 1080p 90−95 90−95
The average gaming FPS of TITAN RTX in Assassin’s Creed Odyssey is 5% more, than GeForce GTX 1080 Ti.
high / 1080p 120−130 120−130
ultra / 1080p 110−120 120−130
QHD / 1440p 90−95 95−100
4K / 2160p 55−60 55−60
low / 720p 160−170 160−170
medium / 1080p 130−140 140−150
The average gaming FPS of TITAN RTX in Battlefield 5 is 3% more, than GeForce GTX 1080 Ti.
low / 768p 45−50 45−50
GeForce GTX 1080 Ti and TITAN RTX have the same average FPS in Call of Duty: Warzone.
low / 768p 250−260 250−260
medium / 768p 230−240 230−240
QHD / 1440p 150−160 150−160
4K / 2160p 100−110 100−110
high / 768p 220−230 220−230
GeForce GTX 1080 Ti and TITAN RTX have the same average FPS in Counter-Strike: Global Offensive.
low / 768p 70−75 70−75
medium / 1080p 50−55 50−55
GeForce GTX 1080 Ti and TITAN RTX have the same average FPS in Cyberpunk 2077.
low / 768p 120−130 120−130
medium / 768p 110−120 110−120
GeForce GTX 1080 Ti and TITAN RTX have the same average FPS in Dota 2.
high / 1080p 100−110 100−110
ultra / 1080p 95−100 100−110
QHD / 1440p 80−85 85−90
4K / 2160p 40−45 45−50
low / 720p 120−130 130−140
medium / 1080p 100−110 110−120
The average gaming FPS of TITAN RTX in Far Cry 5 is 7% more, than GeForce GTX 1080 Ti.
high / 1080p 160−170 160−170
ultra / 1080p 130−140 130−140
QHD / 1440p 90−95 95−100
4K / 2160p 21−24 21−24
low / 720p 270−280 270−280
medium / 1080p 190−200 190−200
The average gaming FPS of TITAN RTX in Fortnite is 0% more, than GeForce GTX 1080 Ti.
high / 1080p 130−140 140−150
ultra / 1080p 110−120 110−120
QHD / 1440p 95−100 100−110
4K / 2160p 60−65 65−70
low / 720p 170−180 170−180
medium / 1080p 140−150 140−150
The average gaming FPS of TITAN RTX in Forza Horizon 4 is 3% more, than GeForce GTX 1080 Ti.
low / 768p 170−180 170−180
medium / 768p 160−170 160−170
high / 1080p 140−150 140−150
ultra / 1080p 85−90 90−95
QHD / 1440p 75−80 80−85
The average gaming FPS of TITAN RTX in Grand Theft Auto V is 1% more, than GeForce GTX 1080 Ti.
high / 1080p 70−75 75−80
ultra / 1080p 60−65 65−70
QHD / 1440p 45−50 50−55
4K / 2160p 30−33 30−35
low / 720p 140−150 140−150
medium / 1080p 95−100 100−110
The average gaming FPS of TITAN RTX in Metro Exodus is 5% more, than GeForce GTX 1080 Ti.
low / 768p 120−130 120−130
GeForce GTX 1080 Ti and TITAN RTX have the same average FPS in Minecraft.
high / 1080p 140−150 150−160
ultra / 1080p 110−120 120−130
4K / 2160p 18−20 18−20
low / 720p 160−170 170−180
medium / 1080p 160−170 170−180
The average gaming FPS of TITAN RTX in PLAYERUNKNOWN’S BATTLEGROUNDS is 6% more, than GeForce GTX 1080 Ti.
high / 1080p 70−75 75−80
ultra / 1080p 45−50 45−50
QHD / 1440p 35−40 35−40
4K / 2160p 24−27 24−27
low / 720p 130−140 140−150
medium / 1080p 100−110 110−120
The average gaming FPS of TITAN RTX in Red Dead Redemption 2 is 5% more, than GeForce GTX 1080 Ti.
low / 768p 290−300 300−350
medium / 768p 220−230 230−240
high / 1080p 140−150 150−160
ultra / 1080p 75−80 85−90
4K / 2160p 50−55 55−60
The average gaming FPS of TITAN RTX in The Witcher 3: Wild Hunt is 8% more, than GeForce GTX 1080 Ti.
low / 768p 350−400 400−450
ultra / 1080p 160−170 200−210
The average gaming FPS of TITAN RTX in World of Tanks is 16% more, than GeForce GTX 1080 Ti.

Full Specs

  GeForce GTX 1080 Ti TITAN RTX
Architecture Pascal Turing
Code name GP102 TU102
Type Desktop Desktop
Release date 28 February 2017 18 December 2018
Pipelines 3584 4608
Core clock speed 1481 MHz 1350 MHz
Boost Clock 1600 MHz 1770 MHz
Transistor count 11,800 million 18,600 million
Manufacturing process technology 16 nm 12 nm
Texture fill rate 354. 4 509.8
Floating-point performance 11,340 gflops
Length 10.5″ (26.7 cm) 267 mm
Memory bus width 352 Bit 384 Bit
Memory clock speed 11000 MHz 14000 MHz
Memory bandwidth 484.4 GB/s 672.0 GB/s
Shared memory
G-SYNC support +
DirectX 12 (12_1) 12 Ultimate (12_1)
Shader Model 6.4 6.5
OpenGL 4.5 4.6
OpenCL 1.2 2.0
Vulkan 1. 2.131 1.2.131
CUDA + 7.5
Monero / XMR (CryptoNight) 0.76 kh/s
SLI options +
Multi monitor support +
Bitcoin / BTC (SHA256) 1427 Mh/s
Decred / DCR (Decred) 4.6 Gh/s
Ethereum / ETH (DaggerHashimoto) 35 Mh/s
Zcash / ZEC (Equihash) 630 Sol/s
GPU Boost 3.0
Siacoin / SC (Sia) 2.96 Gh/s
 

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Titan V vs 1080 Ti — Head-to-head battle of the best desktop GPUs on CNNs.

Is Titan V worth it? 110 TFLOPS! no brainer, right? | by Yusaku Sako

NVIDIA’s Titan V is the latest “desktop” GPU built upon the Volta architecture boasting 110 “deep learning” TFLOPS in the spec sheet. That’s an incredible number. Compare that with 1080 Ti, the current king for the “desktop” GPU, that puts out 11 “normal” TFLOPS with 11GB of GDDR5 memory at a very reasonable $699 sticker price.

While the promise of Titan V sounds very exciting, the $2999 price point is a bit hard to swallow. But with 10x TFLOPS, should you be purchasing Titan V instead? Is that like getting 10 1080 Ti’s? Or are you better off buying 4 1080 Ti’s for the same money?

On a side note, the DGX Station is currently on sale for $49900 (normally priced at $69900). It comes with 4 watercooled server-grade Tesla V100’s which are slightly better spec’d than the desktop-grade Titan V (though the tower looks BOSS and I’m guessing it comes with support and SLA.) But most of us (heck, I bet a lot of university and AI research labs) don’t have that kind of money to throw around. Even if you did, not sure it if makes much economical sense. BTW, if you are operating a data center, NVIDIA has recently updated their EULA to prohibit “desktop” grade GeForce and Titan GPUs from being used in data centers.

So the question is, is Titan V worth it if you are looking to build your own GPU rig?

Let me start by saying that Titan V and its server-grade big brother Tesla V100 are pretty new. V100 came out in May 2017, and Titan V just came out this month (Dec 2017.) Most of deep learning frameworks have been rushing to add Volta support to make sure that all the potential performance can be exploited.

For example, PyTorch have only recently come out with the 0.3.0 release which adds support for CUDA 9 and Volta GPUs. I have been playing around with this setup and the PyTorch community has been awesome (especially Soumith Chintala — thanks dude!) at helping me out. Now that I can run PyTorch on Titan V, I’ve written some benchmark code to demonstrate performance differences. So far, the results only include those of PyTorch 0.3.0 but I’m planning on adding results for other frameworks as well (EDIT: now the benchmark results contain the numbers from TensorFlow 1.4.0 and Caffe2 0.8.1 as well — see the link for more details), all on CUDA 9.0.176 and CuDNN 7.0.0.5, to even out the playing field.

deep-learning-benchmark — Deep Learning Benchmark for comparing the performance of DL frameworks, GPUs, and single vs…

github.com

Performance comparison of Titan V vs 1080 Ti

So let’s check out the results above.
Titan V and 1080 Ti were compared head-to-head against the same settings (in addition, these cards are both on the 16x PCIE slots on the same computer.)

The timing numbers shown above are for the forward pass through the CNN (“eval”) and forward and backward pass (“train”), measured in milliseconds. Those are average numbers computed over 20 passes after some warmup runs. I’ve run this many times, and these numbers are pretty stable.

Interesting points to note:

  • Obviously, Titan V is faster than 1080 Ti. However, if you simply compare the 32-bit (“single” precision) runs, Titan V is only ~20% faster than 1080 Ti.
  • Titan V’s 16-bit (“half” precision) runs are non-trivially faster than those of the 32-bit counterpart. 1080 Ti benefited from going half-precision, but the performance gains are pretty modest compared with Titan V.

These numbers do not really scream “OH MY GOODNESS TITAN V IS A NO BRAINER.”

Is NVIDIA lying about the 10x TFLOPS? I’m sure their marketing folks are good honest people, but there are multiple factors at play here. One, I’m sure there is still room for improvement from the software side to extract every bit of the Volta’s super fast Tensor cores. But even then, if most code paths do not conform to the conditions that allow for maximum theoretical performance (see A FEW RULES in “Programming Tensor Cores in CUDA 9 by NVIDIA), there’s only so much you can do. Time will tell how much improvements we will see at the framework/CUDA/CuDNN level to exploit the Volta GPU capabilities as much as they can, but the initial numbers that I have observed on popular CNN’s (I do computer vision stuff mostly hence my focus on CNN’s) don’t seem to justify getting a Titan V, especially if you were to get one now as software plays catch up.

Although it is fun to play around with the latest tech and be an early adopter, if you are focused on doing AI research or building products on your personal GPU rig economically, I would suggest buying 1080 Ti’s as of this writing (until NVIDIA comes out with an even better alternative in the near future; they tend to surprise us with new releases.) Also consider that a single Titan V is only 12GB of memory while 1080 Ti has marginally smaller memory at 11GB. But if you buy 4 of them for the same money (at least on GPUs… you also need a beefier power supply, motherboards that can support 4 GPUs, more RAM, and better cooling so on), you will end up with way more GPU memory (44GB vs 12GB. ) Having said that I think a more practical thing to do, if you want to get more than one GPU, is to get 2x 1080 Ti’s. This way, the GPUs do not have to be stacked right on top of another (more airflow for cooling — when these cards thermal throttle, you could lose performance significantly… you can do water cooling but that adds significantly to the cost, effort, and some risk.) Also with only two cards, the power draw is 250Wx2 from the GPUs so you can get away with having a cheaper power supply. At any rate, I find it extremely valuable to have multiple GPUs and split experiments across them so that I can iterate quickly, so this is what I would recommend as a good compromise.
Another option for addressing the heat issue of stacked, air-cooled GPUs, if you must have 3–4 of them, don’t want to water cool, and don’t care about aesthetic or noise, is to get some PCIE extenders/risers and do what this winning Kaggler did:

A 4x 1080 Ti machine by Vladimir Iglovikov, one of the winning team members of Carvana Image Masking Challenge

I hope you find this post helpful.