NVIDIA Defines RTX-OPS
NVIDIA is so far ahead of AMD right now that it's in totally new territory with the Turing GPU architecture, so it has had to use its own "RTX-OPS" when trying to define performance of Tensor-related compute tasks. We won't be using RTX-OPS in our day-to-day benchmarks or RTX 20 series reviews, but it definitely deserves its own page to explain WTF is RTX-OPS.
The way NVIDIA works out RTX-OPS, the company explains that the "peak operations of each type based is derated on how often it's used". NVIDIA cites:
- Tensor operations are used 20% of the time
- CUDA cores are used 80% of the time
- RT cores are used 40% of the time (half of 80%)
- INT32 pipes are used 28% of the time (35% of 80%)
For example, RTX-OPS = TENSOR * 20% + FP32 * 80% + RTOPS * 40% + INT32 * 28%.
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- Page 1 [Introduction & Pricing/Availability]
- Page 2 [Specs: Turing GPUs]
- Page 3 [Specs: GDDR6 Memory]
- Page 4 [New Look, New Cooler, New GeForce]
- Page 5 [Detailed Look]
- Page 6 [Turing: NVLink Multi-GPU Tech]
- Page 7 [Turing: RT Cores & Tensor Cores]
- Page 8 [DLSS: Deep Learning Magic & NGX]
- Page 9 [AI To Power The Future Of Gaming]
- Page 10 [WTF IS RTX-OPS]
- Page 11 [GPU Boost 4.0 & NVIDIA Scanner]
- Page 12 [Test System Specs]
- Page 13 [Benchmarks - Synthetic]
- Page 14 [Benchmarks - 1080p]
- Page 15 [Benchmarks - 1440p]
- Page 16 [Benchmarks - 3440x1440]
- Page 17 [Benchmarks - 4K]
- Page 18 [Benchmarks - 8K]
- Page 19 [Overclocking]
- Page 20 [Heat, Power, Noise]
- Page 21 [Unrivaled Performance]
- Page 22 [More Coming Soon & Final Thoughts 1.0]
- Page 23 [Should You Buy It? & Final Thoughts 2.0]
- Page 24 [What Will AMD Do & Final Thoughts 3.0]
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