"As for "getting my facts straight" I was using actual Titan X numbers I have seen for real applications (e.g. nbody simulation)... it only gets around 4000GFLO

"As for "getting my facts straight" I was using actual Titan X numbers I have seen for real applications (e.g. nbody simulation)... it only gets around 4000GFLOPs single precision compared to their advertised 7000." Nonsense. I regularly get ~5.5 TFLOPS out of them running cuBLAS SGEMMs in neural networks. You can get that down to ~1.4 if you do your best to choose stupid small values for m, n, and k, but that's a relatively minor bug and I think it's fixed in cuDNN's latest kernels. But even if it isn't, if you're willing to download Scott Grey's maxas: https://github.com/NervanaSystems/maxas https://github.com/NervanaSystems/maxas, you can hit 6.4 TFLOPS with his hand-coded SGEMM. Similarly, one can do the same for convolutional layers with Andrew Lavin's maxDNN: https://github.com/eBay/maxDNN https://github.com/eBay/maxDNN Your hubris is amusing, but I reiterate that until you have a shipping chip, all you have is a powerpoint processor. I'm sure you disagree. Good luck with that. But do come back when you have numbers (real numbers on real DNNs like AlexNet, VGG, or Googlenet as opposed to synthetic fantasy networks that fit your architecture well). See Nervana for a company doing this well so far. "If you know about NVIDIA, you should know they have a long history of bullshitting numbers." I know them very well and, well, pot.kettle.bs... IMO they're going to stay the leader in parallel computing technology right until Intel stops sniffing its own tailpipe and/or AMD hires a better driver team (they have promising HW). And both of them need to study NVIDIA's engagement with the academic community and one-up it rather than deny its efficacy.