AMD Radeon and NVIDIA GeForce FP32/FP64 GFLOPS Table



AMD Radeon and NVIDIA GeForce FP32/FP64 GFLOPS Table

Here is the GFLOPS comparative table of recent AMD Radeon and NVIDIA GeForce GPUs in FP32 (single precision floating point) and FP64 (double precision floating point). I compiled on a single table the values I found from various articles and reviews over the web (hardware.fr, hexus.net, wikipedia).

GPU FP32 GFLOPS FP64 GFLOPS Ratio
Radeon R9 295X2 11264 1408 FP64 = 1/8 FP32
Radeon HD 7990 7782 1946 FP64 = 1/4 FP32
GeForce GTX Titan Black 5645 1881 FP64 = 1/3 FP32
GeForce GTX 690 5622 234 FP64 = 1/24 FP32
Radeon R9 290X 5632 704 FP64 = 1/8 FP32
GeForce GTX 780 Ti 5345 223 FP64 = 1/24 FP32
Radeon HD 6990 5099 1276 FP64 = 1/4 FP32
GeForce GTX 980 4981 156 FP64 = 1/32 FP32
Radeon R9 290 4849 606 FP64 = 1/8 FP32
GeForce GTX Titan 4709 1523 FP64 = 1/3 FP32
Radeon HD 7970 GHz 4301 1075 FP64 = 1/4 FP32
GeForce GTX 970 3920 122 FP64 = 1/32 FP32
GeForce GTX 780 4156 173 FP64 = 1/24 FP32
Radeon R9 280X 4096 1024 FP64 = 1/4 FP32
Radeon R9 280 3344 836 FP64 = 1/4 FP32
Radeon HD 7950 Boost 3315 828 FP64 = 1/4 FP32
GeForce GTX 770 3210 134 FP64 = 1/24 FP32
GeForce GTX 680 3090 129 FP64 = 1/24 FP32
Radeon HD 7950 2867 717 FP64 = 1/4 FP32
Radeon HD 5870 2720 544 FP64 = 1/5 FP32
Radeon HD 6970 2703 675 FP64 = 1/4 FP32
Radeon R9 270X 2688 168 FP64 = 1/16 FP32
Radeon HD 7870 2560 160 FP64 = 1/16 FP32
GeForce GTX 590 2488 311 FP64 = 1/8 FP32
GeForce GTX 670 2460 102 FP64 = 1/24 FP32
GeForce GTX 660 Ti 2460 102 FP64 = 1/24 FP32
Radeon R9 270 2368 148 FP64 = 1/16 FP32
GeForce GTX 760 2258 94 FP64 = 1/24 FP32
Radeon HD 6950 2253 563 FP64 = 1/4 FP32
Radeon HD 5850 2088 417 FP64 = 1/5 FP32
Radeon R7 260X 1971 123 FP64 = 1/16 FP32
Radeon R7 265 1894 118 FP64 = 1/16 FP32
GeForce GTX 660 1882 78 FP64 = 1/24 FP32
Radeon HD 7790 1792 128 FP64 = 1/14 FP32
Radeon HD 7850 1761 110 FP64 = 1/16 FP32
GeForce GTX 580 1581 197 FP64 = 1/8 FP32
Radeon R7 260 1536 96 FP64 = 1/16 FP32
GeForce GTX 650 Ti Boost 1505 62 FP64 = 1/24 FP32
GeForce GTX 650 Ti 1425 60 FP64 = 1/24 FP32
GeForce GTX 570 1405 175 FP64 = 1/8 FP32
GeForce GTX 750 Ti 1388 43 FP64 = 1/32 FP32
Radeon HD 7770 GHz 1280 80 FP64 = 1/16 FP32
Radeon R7 250X 1280 80 FP64 = 1/16 FP32
GeForce GTX 750 1110 34 FP64 = 1/32 FP32
GeForce GTX 650 812 33 FP64 = 1/24 FP32
Radeon R7 250 806 50 FP64 = 1/16 FP32
Radeon R7 240 500 31 FP64 = 1/16 FP32



Workstations cards:

GPU FP32 GFLOPS FP64 GFLOPS Ratio
FirePro W9100 5240 2620 FP64 = 1/2 FP32






10 thoughts on “AMD Radeon and NVIDIA GeForce FP32/FP64 GFLOPS Table”

  1. JeGX Post Author

    The values don’t come from a benchmark tool, it’s just a compilation from articles / reviews.

  2. DrBalthar

    So basically theoretical peak performance instead of actual peak performance

  3. Promilus

    @DrBalthar – define “actual peak performance”
    I believe you are referring to typical/average real life performance but that one depends strongly by app you test it with.

  4. DrBalthar

    Well not really you can write very simple code that just does a MAD operation (as that’s the one the usually use for advertising FLOPs).

  5. Promilus

    “Well not really you can write very simple code that just does a MAD operation (as that’s the one the usually use for advertising FLOPs”)
    No, it was FPMADD but since all new GPUs uses FMA which does basically the very same thing but with more accurate final result it’s just as good. And btw it wouldn’t be best possible benchmark and “peak” performance since many apps uses only certain type of calculations. Bitcoin miners iirc used plenty of integer and bit operations which were much higher on AMD GPUs than NVidia’s – define universal “actual peak performance” then? There’s no such thing.

  6. DrBalthar

    The F in FLOP stands for Floating point so integer and bit operation are irrelevant. Using just FMA, FPNADD still would be the most fair test as it is the only operation used so there wouldn’t be any difference or cheating. Actual peak performance doesn’t mean real life application performance. It just means actually validated numbers and not just marketing material.

  7. Promilus

    @DrBalthar – sorry but I disagree. Test results of full FMA coverage on all ALUs would be the very same artificial as those of “theoretical peak performance” – it just doesn’t translate into real-world performance. Some apps uses more data parallelism, some more task parallelism, some uses both float and integer intensively and other are focused on fp64 only. Some shares a lot of calculation with CPU and some runs solely on GPU. FP MADD/FMA test results would mean absolutely nothing. For the very same reason there’s no “actual peak performance” benchmark for x86-64 CPUs. But you do have some peek of a real world performance with Linpack benchmark. Check top500.org, there’re both Rmax (linpack results) and Rpeak (theoretical performance) numbers. In mixed GPU and CPU clusters it’s ratio is close to 60-70%. On CPU only clusters it reaches 80-95%.

  8. komar

    gtx 690@ ES BIOS 450W PT150%- +150 mhz + 605 mhz gddr5

    baseclock 1065 mhz – boost 1215 mhz – 7.2ghz GDDR5

    AIda : 6.498 Tflops/s FP32 / 0.275 Tflops/s FP64

  9. Roger Dahl

    This is awesome. Thank you.

    It’s necessary to take these numbers with a grain of salt, and it’s difficult to estimate just how much salt is needed for any given architecture.

    I think a copy of the table that is sorted on FP64 would be useful as well.

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