Nvidia launched hardware and software improvements to its deep learning computing platform that deliver a 10 times performance boost on deep learning workloads compared with the previous generation six months ago.

In the past five years, programmers have made huge advances in AI, first by training deep learning neural networks based on existing data. This allows a neural network to recognize an image of a cat, for instance. The second step is inferencing, or applying the learning capability to new data that has never been seen before, like spotting a cat in a picture that the neural network has never been shown.

At the GPU Technology Conference (GTC) event in San Jose, California, Nvidia CEO Jensen Huang didn’t announce a new graphics processing unit (GPU). Rather, he described improvements to the overall system that deliver the better results.

They include a twofold memory boost for the Nvidia Tesla V100, Nvidia’s datacenter GPU. Nvidia also created a kind of freeway cloverleaf data transfer system — a GPU interconnect fabric dubbed the Nvidia NVSwitch — that enables 16 Tesla V100 GPUs to communicate with each other simultaneously at a speed of 2.4 terabytes per second. And, finally, Nvidia launched an optimized software stack.

Huang also announced a major breakthrough in deep learning computing with Nvidia DGX-2, the first single server capable of delivering two petaflops of computational power. DGX-2 has the deep learning processing power of 300 central processing unit (CPU) servers occupying 15 racks of datacenter space, while being 60 times smaller and 18 times more power…

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