Nvidia unveils Tesla V100 GPU with new DGX server and workstation

Chip giant announces new accelerator that has 12 times the throughput of Pascal for neural network training.
Written by Chris Duckett, Contributor

Nvidia has taken the wraps off its newest accelerator aimed at deep learning, the Tesla V100.

Developed at a cost of $3 billion, the V100 packs 21 billion transistors laid down with TSMC's 12 nanometre FinFET manufacturing process. The GPU has 5,120 CUDA cores and is claimed to have 7.5 TeraFLOPs for 64-bit precision and 15 TeraFLOPs for 32-bit. On the memory front, the GPU has 16GB of HBM2 RAM that has bandwidth of 900GB per second.

At the heart of the V100 is the new Tensor core that has a 4x4 main processing array that completes matrix multiplications in parallel, giving it 12 times the throughput of its Pascal architecture at certain precisions, Nvidia founder and CEO Jen-Hsun Huang said at GTC on Wednesday.

Huang said the V100 has 1.5 times the general purpose FLOPS compared to Pascal, a 12 times improvement for deep learning training, and 6 times the performance for deep learning inference.

"What was possible on Titax X in a few minutes is possible in a few seconds," Huang said.

Alongside the new GPU, the company is updating its DGX-1 box to pack 8 Telsa V100s at a cost of $149,000 to be delivered in the third quarter. The company also announced a new DGX workstation priced at $69,000.

General availability of the V100 is set for the fourth quarter of the year.

In September last year, Nvidia released its first pair of Pascal GPUs for neural networks following the architecture's May launch.

(Image: Nvidia)

Disclosure: Chris Duckett travelled to GTC as a guest of Nvidia

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