![]() ![]() So without further ado, this is NV’s all new shiny beast: Note: in this post I used ccminer-tpruvot, make sure to check out part 2 for some NiceHash results! We had only 1 algo benchmarked before, now we have 4 more. I just had a few hours to play with this card, so I thought you’d find these numbers useful. This quick post doesn’t try to address what already have been addressed. Next, we train the Google Neural Machine Translation recurrent neural network using the OpenSeq2Seq toolkit, again in TensorFlow.Note: we already have reviews for this cards. To compare generational improvements at a like level of accuracy, you’d need to pit Titan RTX with mixed precision (644 images/sec) against Titan Xp in FP32 mode (233 images/sec). But their accuracy is inferior to what Volta and Turing enable. Remember, GP102 doesn’t support FP16 inputs with FP32 accumulates, so it operates in native FP16 mode. It’s also worth mentioning the mixed precision results for Titan Xp. Switching to FP32 mode erases some of the discrepancy between Nvidia’s two TU102-based boards. Most of the matrix multiplication pipeline is the same on Titan RTX and GeForce RTX 2080 Ti, creating a closer contest than the theoretical specs would suggest. Why isn’t the difference greater? The FP32 accumulate operation is only a small part of the training computation. GeForce RTX 2080 Ti’s half-rate mixed-precision mode causes it to shed quite a bit of performance compared to Titan RTX. Of course, Titan V remains a formidable graphics card, and it’s able to trade blows with Titan RTX using FP16 and FP32. This conveys a sizeable advantage over cards with less on-board memory. ![]() In both charts, Titan RTX can handle larger batches than the other cards thanks to its 24GB of GDDR6. The larger the batch, the faster you’re able to get through all of ImageNet’s 14+ million images, given ample GPU performance, GPU memory, and system memory. In brief, batch size determines the number of input images fed to the network concurrently. The numbers in each chart’s legend represent batch sizes. ![]()
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