Some of the key features provided by the Turing architecture include Tensor Cores for acceleration of deep learning inference workflows and new RT Cores for real-time ray tracing acceleration and batch rendering. a b Smith, Ryan (10 May 2017). "The Nvidia GPU Technology Conference 2017 Keynote Live Blog". Anandtech . Retrieved 10 May 2017. Smith, Ryan (14 May 2020). "NVIDIA Ampere Unleashed: NVIDIA Announces New GPU Architecture, A100 GPU, and Accelerator". AnandTech. We will run batch sizes of 16, 32, 64, 128 and change from FP16 to FP32. Our graphs show combined totals. The Red Kayak and Cactus sequences include significant chaotic and circular motion, respectively. NVENC shows a clear advantage over libx264 in these scenes which contain complex inter-predicition, as shown on figures 7 and 8. Figure 7. PSNR RD curve for Red Kayak sequence in 1080p resolution. Figure 8. PSNR RD curve for Cactus sequence in 1080p resolution.

a b c d e f With ECC on, a portion of the dedicated memory is used for ECC bits, so the available user memory is reduced by 12.5%. (e.g. 4 GB total memory yields 3.5 GB of user available memory.) The Tesla also shows great power efficiency, outperforming libx264 2-4x in High Quality mode and up to 5x in Low Latency mode while keeping the CPU load low. Conclusion Pivoting to the performance perspective, using three NVIDIA Titan RTX‘s which is fairly easy to power and cool in a modern 2U server, one can get about fourteen times the performance of a single NVIDIA Tesla T4. That means we have: Nvidia Announces Tesla M40 & M4 Server Cards - Data Center Machine Learning". Anandtech.com . Retrieved 11 December 2015. Some GPUs like the new Super cards as well as the GeForce RTX 2060, RTX 2070, RTX 2080 and RTX 2080 Ti will not show higher batch size runs because of limited memory. NVIDIA Tesla T4 ResNet 50 Training FP16Turing GPUs come equipped with powerful NVENC video encoding units which delivers higher video compression efficiency compared to sophisticated software encoders like libx264, due to the combination of higher performance and lower energy consumption. The ideal solution for transcoding needs to be cost effective (dollars/stream) and power efficient (watts/stream). Let’s look at performance and power consumption results averaged across multiple test sequences, as presented by figures 13 and 14. Figure 13. Number of streams encoded simultaneously at 30 FPS in High Quality mode Figure 14. Number of streams encoded simultaneously at 30 FPS in Low Latency mode. The results are in inference latency (in seconds.) If we take the batch size / Latency, that will equal the Throughput (images/sec) which we plot on our charts. These performance tests set the encode parameters to those shown in table 2: Encoding Parameters Preset While Resnet-50 is a Convolutional Neural Network (CNN) that is typically used for image classification, Recurrent Neural Networks (RNN) such as Google Neural Machine Translation (GNMT) are used for applications such as real-time language translations.

Hand, Randall (23 August 2010). "NVidia Tesla M2050 & M2070/M2070Q Specs OnlineVizWorld.com". VizWorld.com . Retrieved 11 December 2015.All NVIDIA GPUs starting with Kepler support fully-accelerated hardware video encoding; GPUs starting with Fermi support fully-accelerated hardware video decoding. The recently released Turing hardware delivered Tensor Cores and better machine learning performance, but the new GPU also incorporated new multimedia features such as an improved NVENC unit to deliver better compression and image quality in video codecs. One can see that with the 16GB of onboard memory, the NVIDIA Tesla T4 can train using a batch size of 128 here, and gets a performance boost from that. At the same time, it is only giving a 5-6% benefit and performance is unable to match our GeForce RTX 2060 results. Deep Learning Training Using OpenSeq2Seq (GNMT)