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NVIDIA Sets Six Records in AI Performance

NVIDIA CEO Jensen Huang unveils the DGX-2 supercomputer.

This week NVIDIA announced that the company has broken a total of six performance records on a broad set of AI benchmarks. As a full suite, the benchmarks cover a variety of workloads and infrastructure scale – ranging from 16 GPUs on one node to up to 640 GPUs across 80 nodes.

Backed by Google, Intel, Baidu, NVIDIA and dozens more technology leaders, the new MLPerf benchmark suite measures a wide range of deep learning workloads. Aiming to serve as the industry’s first objective AI benchmark suite, it covers such areas as computer vision, language translation, personalized recommendations and reinforcement learning tasks.

The six categories include image classification, object instance segmentation, object detection, non-recurrent translation, recurrent translation and recommendation systems. NVIDIA did not submit results for the seventh category for reinforcement learning, which does not yet take advantage of GPU acceleration.

The new records were powered by NVIDIA DGX systems, including NVIDIA DGX-2, the world’s most powerful AI system, featuring 16 fully connected V100 Tensor Core GPUs.

The new MLPerf benchmarks demonstrate the unmatched performance and versatility of NVIDIA’s Tensor Core GPUs,” said Ian Buck, vice president and general manager of Accelerated Computing at NVIDIA. “Exceptionally affordable and available in every geography from every cloud service provider and every computer maker, our Tensor Core GPUs are helping developers around the world advance AI at every stage of development.”

The software innovations and optimizations used to achieve NVIDIA’s industry-leading MLPerf performance are available free of charge in the company’s latest NGC deep learning containers.

Download the code today from the NGC container registry

 

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