With Nvidia’s Volta architecture having been skipped on the consumer GeForce front, only finding its way into the prosumer-grade Titan V, workstation-grade Quadro GV100, and the Xavier generation of Tegra SoCs that focus on self-driving cars, the next green team architecture gamers can expect to use after Pascal is Turing, which will be powering the recently announced GeForce RTX 2000 series of graphics cards.

Today, we’re able at last to bring you full details of the Turing architecture and the specifications of the RTX 2080 Ti, RTX 2080, and RTX 2070, although performance data is still bound by NDA. As a reminder, the RTX 2080 Ti and RTX 2080 launch this month, while the RTX 2070 isn’t launching until October.

As you’ll see, there’s plenty to cover; this is a summary of information provided by Nvidia in an 83-page whitepaper and a full day of press-only presentations, so you may want to grab a cup of [insert caffeinated beverage of choice] before getting into the nitty gritty. However, we reckon a good place to start is with the full specifications of the three upcoming cards. After all, who doesn’t love a bloody good spec table?

(Note that Nvidia Founders Edition card specifications will differ slightly to these reference specs on account of their higher boost clocks, which are used to inform the TFLOPS, TIPS, RTX-OPS, and TDP calculations.)

Nvidia GeForce RTX 2080 Ti Nvidia GeForce GTX 1080 Ti Nvidia GeForce RTX 2080 Nvidia GeForce GTX 1080 Nvidia GeForce RTX 2070 Nvidia GeForce GTX 1070
Architecture Turing Pascal Turing Pascal Turing Pascal
Codename TU102 GP102 TU104 GP104 TU106 GP104
Base Clock 1,350MHz 1,480MHz 1,515MHz 1,607MHz 1,410MHz 1,506MHz
Boost Clock 1,545MHz 1,582MHz 1,710MHz 1,733MHz 1,620MHz 1,683MHz
Layout 6 GPCs, 68 SMs 6 GPCs, 28 SMs 6 GPCs, 46 SMs 4 GPCs, 20 SMs 3 GPCs, 36 SMs 3 GPCs, 15 SMs
CUDA Cores 4,352 3,584 2,944 2,560 2,304 1,920
Tensor Cores 544 N/A 368 N/A 288 N/A
RT Cores 68 N/A 46 N/A 36 N/A
Rasterisers 6 6 6 4 3 3
Texture Units 272 224 184 160 144 120
ROPs 88 88 64 64 64 64
Peak TFLOPS (FP32) 13.4 11.3 10 8.9 7.5 6.5
Peak TIPS (INT32) 13.4 N/A 10 N/A 7.5 N/A
Peak FP16 Tensor TFLOPS (FP16 Accumulate) 107.6 N/A 80.5 N/A 59.7 N/A
Giga Rays/sec 10 1.1 8 0.89 6 0.65
RTX-OPS 76 11.3 57 8.9 42 6.5
Transistors 18.6 billion 12 billion 13.6 billion 7.2 billion 10.8 billion 7.2 billion
Die Size 754mm2 471mm2 545mm2 314mm2 445mm2 314mm2
Process 12nm FFN 16nm 12nm FFN 16nm 12nm FFN 16nm
Memory Data Rate 14Gbps 11Gbps 14Gbps 10Gbps 14Gbps 8Gbps
Memory Interface 352-bit 352-bit 256-bit 256-bit 256-bit 256-bit
Memory Bandwidth 616GB/s 484GB/s 448GB/s 320GB/s 448GB/s 256GB/s
TDP 250W 250W 215W 180W 175W 150W

We have included the relevant Pascal parts for reference, but comparing raw specs across different architectures rarely leads to a great deal of insight, and Turing is a pretty big change relative to Pascal. While Nvidia is still using CUDA cores for classic floating point shader workloads, the addition of a separate and independent datapath for integer math means the Turing Streaming Multiprocessor is now very different in design. You’ll also notice a move from GDDR5 and GDDR5X memory to GDDR6, which has resulted in a redesign of Turing’s memory system. Add to this the dedicated hardware for AI-related workloads (Tensor Cores) and ray tracing workloads (RT Cores), and you end up with a vastly different family of GPUs, as evidenced by the number of ‘N/A’ entries in the spec table for Pascal parts.

Additional new hardware features include updated display and video engines along with VirtualLink support and a move to NVLink for multi-GPU configurations. Add to that new advanced shading techniques which Nvidia is leveraging to boost performance and give developers new flexibility, and you start to see why Nvidia refers to Turing as ‘the biggest architectural leap forward in over a decade’. How it actually performs in games remains to be seen, but for now we’ll be digging into exactly what informs that claim.


Just quickly, you may have seen and been confused by the new term RTX-OPS. This is a term invented by Nvidia, but there is logic in how the figure is derived. It simply serves to more accurately compare the differences between (and only between) Nvidia RTX GPUs when truly taking into account the new capabilities of their GPUs (based on how much work each part does in generating a “typical” frame) in a way that the classic TFLOPS figure simply misses. There’s maths behind it, but it’s not especially interesting since it’s really just a marketing/categorisation tool, and at least you know now what its purpose is.

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October 14 2021 | 15:04