The Turing Trio: TU102, TU104, & TU106

Altogether, NVIDIA will be kicking off the Turing generation with a trio of GPUs: TU102, TU104, and TU106. Notably, this is a much larger product stack than any past NVIDIA consumer launch. Typically NVIDIA releases just a single GPU on launch day – usually the 104 model – and then additional GPUs filter in months down the line as production ramps up. Instead NVIDIA will be releasing products based on two different GPUs this month, and then following that up with a third GPU one month later.

Since all three GPUs are Turing, all three GPUs share the same basic design, and in the same ratios. Meaning that TU104 and TU106 are proportionally cut-down versions of TU102, without any radical changes between the GPUs like we’ve seen between GP100 and GP102/104/106. This also means that TU104 and TU106 are proportionally powerful GPUs; NVIDIA hasn’t stripped TU106’s RT and tensor cores to a bare minimum to save on the transistor count, for example.

NVIDIA Turing GPU Comparison
  TU102 TU104 TU106 GP102
CUDA Cores 4608 3072 2304 3840
SMs 72 48 36 30
Texture Units 288 192 144 240
RT Cores 72 48 36 N/A
Tensor Cores 576 384 288 N/A
ROPs 96 64 64 96
Memory Bus Width 384-bit 256-bit 256-bit 384-bit
L2 Cache 6MB 4MB 4MB 3MB
Register File (Total) 18MB 12MB 9MB 7.5MB
Architecture Turing Turing Turing Pascal
Manufacturing Process TSMC 12nm "FFN" TSMC 12nm "FFN" TSMC 12nm "FFN" TSMC 16nm
Die Size 754mm2 545mm2 445mm2 471mm2

The flagship for the Turing family is a part that we’ve quickly become familiar with, and that is TU102. The largest of the Turing GPUs, this is the GPU NVIDIA is tapping for two of their new Quadro RTX cards, as well as the GeForce RTX 2080 Ti.

A fully-enabled TU102 is comprised of 72 SMs, organized into 6 GPCs. And since the number of RT cores and tensor cores is proportional to the SM count, we’re looking at 72 RT cores and 576 tensor cores. Paired with this harder is 12 ROP/memory controller partitions, giving the GPU a native 384-bit memory bus and 96 ROPs for pixel blending.

Relative to NVIDIA’s GV100 GPU, TU102 is smaller and contains fewer SM and tensor cores, but the difference is not quite as great as you might think. The 18.6B transistor, 754mm2 chip still packs 85% of GV100’s common hardware even with its physically small size, and then this value doesn’t include all the die space taking up by Turing’s new features such as the RT cores and the newer generation tensor cores. So this is still an incredibly big chip, and is especially notable since NVIDIA will be putting it in a consumer card.

Outside of the standard Turing graphics hardware, of particular note here is that because TU102 pulls double-duty with high-end Quadro cards, it’s the only GPU to feature two NVLink connections. Each NVLink connection – which NVIDIA technically classifies as an 8x NVlink – is capable of offering 25GB/sec of bandwidth in each direction, for a total of 50GB/sec of bandwidth in each direction when the links are used in aggregate.

The middle child of the stack and the traditional frontrunner in NVIDIA’s GPU release cadence is the TU104. This part is a smaller, slimmer Turing GPU that shaves off some SMs in the name of coming in at a smaller die size. Altogether it offers 48 SMs and 8 ROP/MC partitions, making it roughly two-thirds of a TP102.

In exchange the chip is also notably smaller. Not small by any means, but smaller. The part comes in at 13.6B transistors, which are laid out in a 545mm2 die. This still makes it the largest x04 chip by a landslide, coming in much larger than even the late-generation 28nm GM204 in 2014. Still, NVIDIA shaved off over 200mm2 relative to TU102, so there are significant die savings here compared to using an actual cut-down TU102.

The TU104 will be going into just one consumer card, at least for now. This is the GeForce RTX 2080. It will also be going into its Quadro counterpart, the Quadro RTX 5000. Notably, only the Quadro is getting a fully enabled chip, while RTX 2080 ships with a couple of disabled SMs. This GPU also features NVLink support, but this time it’s a single x8 link, half of what TU102 offered.

Finally, rounding out the trio is the mysterious TU106 GPU, which prior to today had not been disclosed by NVIDIA. Rather than using a cut-down TU104 in the GeForce RTX 2070, they’re going to use an entirely different GPU.

TU106 in turn is a smaller chip than TU104, but perhaps not as much as you think. NVIDIA is still including 36 SMs and the same 8 ROP/MC partitions, so in terms of pixel throughput and bandwidth TU106 is actually identical to TU104 on paper. It’s only when looking at the processor elements that we see that we’ve ended up with what’s essentially 75% of a TU104. On which note however, it’s rather interesting that NVIDIA opted to halve the GPC count here; TU106 packs 12 SMs to a GPC, versus 8 to a GPC in TU104.

The payoff for NVIDIA here is that TU106 once again brings down NVIDIA’s large die sizes. The chip features 10.8 billion transistors, which at 445mm2 still makes it a beefy chip. But this is at least finally smaller than the GP102 used in the Pascal Titan Xp cards.

The net result of all of this is that NVIDIA has a very interesting GPU launch stack, one unlike anything we’ve seen before. No two GeForce cards share the same GPU; there is a GPU for each and every card right now. And we’re seeing NVIDIA launch two GPUs right out the door, including the massive TU102, with the TU106 to follow close behind. So it’s a very different setup than the norm for NVIDIA.

Turing In Practice: GeForce RTX 2080 Ti, 2080, & 2070

These 3 GPUs, in turn, form the foundation of the GeForce RTX 2080 Ti, RTX 2080, and RTX 2070. As previously announced by NVIDIA, the first two cards will go on sale next week, on September 20th. Meanwhile the RTX 2070 will ship a bit later, with sales starting in October.

We’ll be giving the RTX 2080 Ti and RTX 2080 a full work-through next week in our review of those cards. In the meantime, here’s a recap of their specifications and pricing.

NVIDIA GeForce x80 Ti Specification Comparison
  RTX 2080 Ti
Founder's Edition
RTX 2080 Ti GTX 1080 Ti GTX 980 Ti
CUDA Cores 4352 4352 3584 2816
ROPs 88 88 88 96
Core Clock 1350MHz 1350MHz 1481MHz 1000MHz
Boost Clock 1635MHz 1545MHz 1582MHz 1075MHz
Memory Clock 14Gbps GDDR6 14Gbps GDDR6 11Gbps GDDR5X 7Gbps GDDR5
Memory Bus Width 352-bit 352-bit 352-bit 384-bit
Single Precision Perf. 14.2 TFLOPs 13.4 TFLOPs 11.3 TFLOPs 6.1 TFLOPs
"RTX-OPS" 78T 78T N/A N/A
TDP 260W 250W 250W 250W
GPU TU102 TU102 GP102 GM200
Architecture Turing Turing Pascal Maxwell
Manufacturing Process TSMC 12nm "FFN" TSMC 12nm "FFN" TSMC 16nm TSMC 28nm
Launch Date 09/20/2018 09/20/2018 03/10/2017 06/01/2015
Launch Price $1199 $999 MSRP: $699
Founders: $699

The king of NVIDIA’s new product stack, the GeForce RTX 2080 Ti is without a doubt an interesting card. NVIDIA’s consumer flagship sports 4352 Turing CUDA cores and 544 tensor cores, as well as 68 RT cores. Like its Quadro counterpart, this card is rated for 10 GigaRays/second, and for traditional compute we’re looking at 13.4 TFLOPS based on these specifications. Note that on paper this is only 19% higher than GTX 1080 Ti, which is why NVIDIA’s architectural changes and efficiency improvements are going to carry the day here, rather than brute forcing the matter with more hardware.

Clockspeeds have actually dropped from generation to generation here. Whereas the GTX 1080 Ti started at 1.48GHz and had an official boost clock rating of 1.58GHz (and in practice boosting higher still), RTX 2080 Ti starts at 1.35GHz and boosts to 1.55GHz, while we don’t know anything about the practical boost limits. So assuming NVIDIA is being as equally conservative as the last generation, then this means the average clockspeeds have dropped slightly.

Moving on, for the ROP and memory subsystem we’re looking at a partially-enabled configuration here as well. RTX 2080 Ti offers 88 of 96 ROPs, which is a result of NVIDIA disabling one of the 12 ROP/MC partitions. Even then, relative to the GTX 1080 Ti and thanks to GDDR6, memory clockspeeds have been boosted from 11Gbps to 14Gbps, a 27% increase. And since the memory bus width itself remains identical at 352-bits wide, this means the final memory bandwidth increase is also 27%.

Past this, things start diverging a bit. NVIDIA is once again offering their reference-grade Founders Edition cards, and unlike with the GeForce 10 series, the 20 series FE cards have slightly different specifications than their base specification compatriots. Specifically, NVIDIA has cranked up the clockspeed and the resulting TDP a bit, giving the 2080 Ti FE an on-paper 6% performance advantage, and also a 10W higher TDP. For the standard cards then, the TDP is the x80 Ti-traditional 250W, while the FE card moves to 260W.

NVIDIA GeForce x80 Specification Comparison
  RTX 2080
Founder's Edition
RTX 2080 GTX 1080 GTX 980
CUDA Cores 2944 2944 2560 2048
ROPs 64 64 64 64
Core Clock 1515MHz 1515MHz 1607MHz 1126MHz
Boost Clock 1800MHz 1710MHz 1733MHz 1216MHz
Memory Clock 14Gbps GDDR6 14Gbps GDDR6 10Gbps GDDR5X 7Gbps GDDR5
Memory Bus Width 256-bit 256-bit 256-bit 256-bit
Single Precision Perf. 10.6 TFLOPs 10.1 TFLOPs 8.9 TFLOPs 5.0 TFLOPs
"RTX-OPS" 60T 60T N/A N/A
TDP 225W 215W 180W 165W
GPU TU104 TU104 GP104 GM204
Architecture Turing Turing Pascal Maxwell
Manufacturing Process TSMC 12nm "FFN" TSMC 12nm "FFN" TSMC 16nm TSMC 28nm
Launch Date 09/20/2018 09/20/2018 05/27/2016 09/18/2014
Launch Price $799 $699 MSRP: $599
Founders $699

Moving down the line, we have the GeForce RTX 2080. Based on TU104, this card offers 2944 CUDA cores paired with 368 tensor cores. Like the RTX 1080 Ti, clockspeeds have dropped a bit from generation to generation, as the base clock is down to 1515MHz and the boost clock to 1710MHz. All told we’re looking at a pure CUDA core compute throughput of 10.1 TFLOPs, about 13% higher than the GTX 1080. Or if we compare it to the RTX 2080 Ti, we’d see around 75% of the expected compute/tensor performance, which is only a bit larger than the jump we saw between the GTX 1080 and GTX 1080 Ti.

Meanwhile the card does come with a fully enabled memory bus, meaning we’re looking at 8GB of GDDR6 running at 14Gbps, on top of a 256-bit memory bus. Relative to the GTX 1080 this is an even more significant 40% increase in memory bandwidth.

As for TDPs, they’ve gone up for this band of cards. The stock RTX 2080 will have a 215W TDP, up 30W from the GTX 1080’s 180W TDP, and an even bigger increase if we look at GTX 980’s 165W TDP. It’s no secret that NVIDIA is fighting a losing battle with Moore’s Law here, and barring massive efficiency improvements, there is a need to increase TDPs to keep up overall performance. TU104 is a big chip, and without a full node shrink, it would seem that NVIDIA has to pay a power penalty instead. In the meantime this higher TDP also negates some of the RTX 2080 Ti’s power disadvantage, as now that gap is just 35W instead of 65W.

NVIDIA GeForce x70 Specification Comparison
  RTX 2070
Founder's Edition
RTX 2070 GTX 1070 GTX 970
CUDA Cores 2304 2304 1920 1664
ROPs 64 64 64 64
Core Clock 1410MHz 1410MHz 1506MHz 1050MHz
Boost Clock 1710MHz 1620MHz 1683MHz 1178MHz
Memory Clock 14Gbps GDDR6 14Gbps GDDR6 8Gbps GDDR5 7Gbps GDDR5
Memory Bus Width 256-bit 256-bit 256-bit 256-bit
Single Precision Perf. 7.9 TFLOPs 7.5 TFLOPs 6.5 TFLOPs 3.9 TFLOPs
"RTX-OPS" 45T 45T N/A N/A
TDP 185W 175W 150W 145W
GPU TU106 TU106 GP104 GM204
Architecture Turing Turing Pascal Maxwell
Manufacturing Process TSMC 12nm "FFN" TSMC 12nm "FFN" TSMC 16nm TSMC 28nm
Launch Date 09/20/2018 09/20/2018 06/10/2016 09/18/2014
Launch Price $599 $499 MSRP: $379
Founders $449

The final member of the new GeForce RTX family is the GeForce RTX 2070. Traditional for its roots, this is the “value” enthusiast card, giving up some of the RTX 2080’s performance in exchange for a lower price. Though with prices starting at $499, “value” and “cheap” are not the same thing.

With TU106, NVIDIA has shaved off a number of SMs. The end result is that the RTX 2070 offers 2304 CUDA cores and 288 tensor cores. Meanwhile ray tracing performance is rated at 6 GigaRays/second. Meanwhile in an interesting twist, this is the only consumer part launching with a fully-enabled GPU: TU106 offers 36 SMs, and NVIDIA is using all 36 of them.

Like the other RTX cards, clockspeeds have dropped a bit versus the previous generation; the base clock now starts at 1410MHz, and the boost clock is 1620MHz. On paper then, compute throughput works out to 7.5 TFLOPs, up 15% from GTX 1070. Or if we compare it to the 2080, the 2070 should deliver around 75% of its bigger sibling’s compute performance, which makes the jumps between the 2070, 2080, and 2080 Ti all very symmetrical. This is also an ever so slightly smaller gap than what was between the GTX 1080 and GTX 1070.

Also like the RTX 2080, this appears to be a fully enabled memory configuration. Meaning we’re looking at 8GB of GDDR6 running at 14Gbps, on top of a 256-bit memory bus. Relative to the GTX 1070 this is the single greatest bandwidth increase of all of the RTX cards; the 2070 will enjoy a 75% increase in memory bandwidth over its Pascal predecessor, as the GTX 1070 never did use GDDR5X.

TDPs have gone up here as well. The RTX 2070 is rated for 175W, up from 150W for the GTX 1070, and 145W for the GTX 970. As with the RTX 2080, it looks like NVIDIA is paying for their performance and new features via higher power consumption in lieu of a full node shrink.

Turing Tensor Cores: Leveraging Deep Learning Inference for Gaming Feeding the Beast (2018): GDDR6 & Memory Compression


View All Comments

  • Tamz_msc - Saturday, September 15, 2018 - link

    "Besides, what you said isn't true even limiting the discussion to what was covered in this article. The Turing Tensor cores allow for a greater range of precisions."

    You mean lower precision, right? INT8 and INT4 are lower range. From a higher-level view Volta is very similar to Turing, just like the OP described.
  • Yojimbo - Saturday, September 15, 2018 - link

    "greater range of precisions"

    INT8, INT4, FP16, etc., are precisions. The range of precisions an architecture can handle is the set of all precisions it can handle. Turing Tensor Cores can handle INT4, INT8, and FP16, whereas Volta Tensor Cores can handle FP16. So Turing can handle a greater range of precisions.
  • Bulat Ziganshin - Friday, September 14, 2018 - link

    I would pray for 2060 w/o all this RT/FP16 stuff Reply
  • Spunjji - Monday, September 17, 2018 - link

    Seems likely given how nutso these die sizes are. I expect we won't see it until after Pascal inventory is cleared, though. Reply
  • Da W - Friday, September 14, 2018 - link

    Well still playing on my 3-screen Haswell + GTX780 rig, and being pretty satisfied of it, i'll probably just get a cheap GTX 1070 or 1080 for my new Ryzen rig and wait if ray tracing really gets adopted in 1 or 2 years. Seems to me lots of transistors invested for not many games. If history told us anything, it's not because a technology is great that it will get adopted, especially if it asks LOADS more developper time for the game companies.

    Not sure AMD won't come up with something either down the line. They've been given for dead for over 2 decades, guess where they are now!
  • Holliday75 - Monday, September 17, 2018 - link

    I am waiting as well. This is the first attempt to change the game. Next gen or two is where it will be fined tuned and worth purchasing. This feels like a 4k TV purchase. Waste of money. Reply
  • abufrejoval - Friday, September 14, 2018 - link

    I wonder how much Turing is about staking out territorial claims vs. dark silicon also coming to GPUs...

    Obviously Nvidia wants to protect its CUDA machine learning and HPC empire against custom ASIC competitors which finally also include Intel with their Configurable Spatial Accellerator, as well as Cambricon, Google's TPU ASICs and far too many others for comfort.

    But while many seem to bemoan that tensor core or rasterizing real-estate is a waste for gaming and just about raising the purchase prices with overhyped features nobody needs, I wonder if apart from the partial truth in that the other motivating driver is simply that the inability to translate additional transistors into additional performance as additional bandwidth requires step changes in GDDR6 lanes (with unshrinkable pad areas and amplifiers) and hits foundry reticle sizes.

    So they had transistors left over (wonder where those came from without a die shrink: I/O voltage reduction, layout optimizations, really bigger chips?), that could not be turned into direct DX1x performance gains due to bandwidth and TDP constraints and going to a richer functional base with Tensor Cores and raytrace assists would eat alternate bandwidth or TDP budgets, not additional ones.

    Any truth in those assumptions?
  • abufrejoval - Friday, September 14, 2018 - link

    ok, much bigger chips...
    And no rip-off: They are worth what they are charging if only for the inference accelleration.
  • Yojimbo - Saturday, September 15, 2018 - link

    I am not convinced the Tensor Cores take up a lot of real estate. And they are tightly integrated into NVIDIA's SMs. Designing two SMs, one with Tensor Cores and one without Tensor Cores would be a lot more expensive than leaving them in. Plus, NVIDIA sees deep learning as important for gaming.

    Your argument about FLOPS per bandwidth does have validity. It's just that neither Tensor Cores nor RT cores were just thrown in there because they had transistors left over. Look at the die sizes of these new GPUs compared to Pascal GPUs. If they built a smaller chip that performed the same in legacy games then they could sell them more cheaply, and so sell more of them, while making the same profit on each one. That would mean higher margins and greater profits.

    The RTX and Tensor Cores are a strategic initiative. I think in making the decision to include them NVIDIA judged that those two technologies would have a positive impact on the future of gaming. The reason they made that judgment may include the dwindling FLOPS/memory bandwidth trend.
  • bernstein - Friday, September 14, 2018 - link

    really interesting time in gpu's right now... remember a decade ago when intel teased a x86-gpu that promised to do real-time raytracing?

    yet turing may turn out to provide an abysmal price/perf ratio.
    - about half the transistors will only be used in a few upcoming games, they could be used to possibly double performance in rasterization-only games (7nm amd navi anyone?)
    - but if (hybrid-)raytracing takes off quickly, turing will be crushed by 7nm gpu's dedicating way more transistors to the task, as it's performance is still skewed heavily towards rasterization
    - ai inferencing seems like a safe bet, again i'd wager that DLSS will only ever work with the vast minority of games released each day on steam, so it's usefulness will depends on whether developers make other use of the available silicon... (better AI opponents anyone?)

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