PNY NVIDIA Tesla T4 Datacenter Card 16GB GDDR6 PCI Express 3.0 x16, Single Slot, Passive Cooling

Product Information

The NVIDIA Tesla T4 is a very successful product. It sells very well even despite the above. There are a few reasons for that. c:v h264_nvenc -preset medium -b:v BITRATE -bufsize BITRATE*2 -profile:v high -bf 3 -b_ref_mode 2 -temporal-aq 1 -rc-lookahead 20 -vsync 0

High Quality mode which represents most common encoding scenarios with VBR control and B frames enables. Walton, Mark (6 April 2016). "Nvidia unveils first Pascal graphics card, the monstrous Tesla P100". Ars Technica . Retrieved 19 June 2019. We will run batch sizes of 16, 32, 64, 128 and change from FP16 to FP32. Our graphs show combined totals. H.264 emerged 15 years ago and has become an ubiquitous video coding standard. It has become most important and widespread codec in the industry. These tests show how the Tesla T4 performs versus the well-known open source encoder libx264 in two scenarios:

Theoretical Performance

In our benchmarks for Inferencing, a ResNet50 Model trained in Caffe will be run using the command line as follows. If one has, for example, a 2U server, then things get considerably hazier. In a server that has physical slots open, and needs say three GPUs, the math looks like: Nvidia Tesla was the name of Nvidia's line of products targeted at stream processing or general-purpose graphics processing units (GPGPU), named after pioneering electrical engineer Nikola Tesla. Its products began using GPUs from the G80 series, and have continued to accompany the release of new chips. They are programmable using the CUDA or OpenCL APIs. https://investor.nvidia.com/news/press-release-details/2023/NVIDIA-and-Google-Cloud-Deliver-Powerful-New-Generative-AI-Platform-Built-on-the-New-L4-GPU-and-Vertex-AI/default.aspx

We also found that this benchmark does not use two GPU’s; it only runs on a single GPU. You can, however, run different instances on each GPU using commands like. a b "Tesla C2050 and Tesla C2070 Computing Processor" (PDF). Nvidia.com . Retrieved 11 December 2015. Roughly the size of a cell phone, the T4 has a low-profile, single-slot form factor. It draws a maximum of 70W power, so it requires no supplemental power connector. Specifications for NVIDIA Tesla GPUs for virtualization workloads. Nvidia retired the Tesla brand in May 2020, reportedly because of potential confusion with the brand of cars. [1] Its new GPUs are branded Nvidia Data Center GPUs, [2] as in the Ampere A100 GPU. [3] Overview [ edit ] Nvidia Tesla C2075 precision: Specify FP32 or FP16 precision, which also enables TensorCore math for Volta and Turing GPUs.Again, at a single and perhaps up to three cards in a 1U server, the T4 will not have GeForce competition. Once one moves to a 2U form factor, it is extraordinarily more expensive. That TCO delta is plenty to offer swapping to passive GeForce coolers and add power cables for example. Those become rounding errors in the sea of TCO delta. That shows the less exciting side of this industry. How the NVIDIA Tesla T4 Sells Well Tesla products are primarily used in simulations and in large-scale calculations (especially floating-point calculations), and for high-end image generation for professional and scientific fields. [8] We also wanted to train the venerable ResNet-50 using Tensorflow. During training the neural network is learning features of images, (e.g. objects, animals, etc.) and determining what features are important. Periodically (every 1000 iterations), the neural network will test itself against the test set to determine training loss, which affects the accuracy of training the network. Accuracy can be increased through repetition (or running a higher number of epochs.)

Using the two NVIDIA Tesla T4’s in the same space as one full-sized GPU’s we find the NVIDIA Tesla T4 achieves near the NVIDIA RTX 2080 Ti results at lower power. This is a good result. The T4 is built on NVIDIA’s Turing architecture — the biggest architectural leap forward for GPUs in over a decade — enabling major advances in efficiency and performance. 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) Casas, Alex (19 May 2020). "NVIDIA Drops Tesla Brand To Avoid Confusion With Tesla". Wccftech . Retrieved 8 July 2020. c:v h264_nvenc -preset llhp -rc cbr_ld_hq -b:v BITRATE -bufsize BITRATE/FRATE -profile:v high -g 999999 -vsync 0The NVIDIA Titan RTX is a dual-slot, longer, and higher power card. On the other hand, it would take more than three NVIDIA Tesla T4’s to equal the same performance as a similarly priced GPU cousin.

Turing 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. As part of Project Denver, Nvidia intends to embed ARMv8 processor cores in its GPUs. [6] This will be a 64-bit follow-up to the 32-bit Tegra chips. 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.In 2013, the defense industry accounted for less than one-sixth of Tesla sales, but Sumit Gupta predicted increasing sales to the geospatial intelligence market. [9] Specifications [ edit ] Model 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 FP16 Here we did not get down to INT4, but INT8 is becoming very popular. Using INT8 precision is generally faster for inferencing than using floating-point. There is significant research that shows in many situations INT8 is accurate enough for inferencing making it an accurate enough and lower computational power choice for the workload. Figures 1 through 4 show that the Tesla T4 delivers same or slightly better visual quality to libx264 in high quality mode for all-round balanced sequences like Kimono, BQ terrrace and Park Scene. Figure 1. PSNR RD curve for Kimono sequence in 720p resolution. Figure 2. PSNR RD curve for BQ terrace sequence in 1080p resolution. Figure 3. PSNR RD curve for Park Scene sequence in 720p resolution. Figure 4. PSNR RD curve for Park Scene sequence in 1080p resolution. The Tesla P100 uses TSMC's 16 nanometer FinFET semiconductor manufacturing process, which is more advanced than the 28-nanometer process previously used by AMD and Nvidia GPUs between 2012 and 2016. The P100 also uses Samsung's HBM2 memory. [7] Applications [ edit ]