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First of all, from the article subheading, you will already be aware that this GPU packs in 21.1 billion transistors and is fabricated using TSMCs 12nm FFN process. The Volta GV100 GPU powering Nvidia's latest accelerator product has some mighty specs. I think that means the GV100 leapfrogs Google's TPU ASIC which is capable of 90 TOPS. "Tesla V100’s Tensor Cores deliver up to 120 Tensor TFLOPS for training and inference applications," notes Nvidia. They provide a significant performance uplift in training neural networks. There are 8 Tensor Core per SM unit in the Volta GV100, that's 640 in total. It's interesting to see that Nvidia's Volta GV100 architecture offers dedicated Tensor Cores to compete with accelerators from the likes of Google.
#Tesla p100 fp64 professional#
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. The P100 also uses Samsung's HBM2 memory. 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 20. This will be a 64-bit follow-up to the 32-bit Tegra chips. Īs part of Project Denver, Nvidia intends to embed ARMv8 processor cores in its GPUs.
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However, the last Tesla C-class products included one Dual-Link DVI port. Unlike Nvidia's consumer GeForce cards and professional Nvidia Quadro cards, Tesla cards were originally unable to output images to a display. Tesla cards have four times the double precision performance of a Fermi-based Nvidia GeForce card of similar single precision performance. As of 2012, Nvidia Teslas power some of the world's fastest supercomputers, including Summit at Oak Ridge National Laboratory and Tianhe-1A, in Tianjin, China. Offering computational power much greater than traditional microprocessors, the Tesla products targeted the high-performance computing market.