NVIDIA Sets Six Records in AI Performance

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Tensor Core GPUs achieve best performance across every MLPerf benchmark submitted; acceleration stack available to all on NGC.

nvidia blogs, Paresh Kharya, Dec. 12, 2018 – 

NVIDIA has set six AI performance records with today's release of the industry's first broad set of AI benchmarks.

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.

NVIDIA achieved the best performance in the six MLPerf benchmark results it submitted for. These cover a variety of workloads and infrastructure scale – ranging from 16 GPUs on one node to up to 640 GPUs across 80 nodes.

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.

A key benchmark on which NVIDIA technology performed particularly well was language translation, training the Transformer neural network in just 6.2 minutes. More details on all six submissions are available on the NVIDIA Developer news center.

NVIDIA engineers achieved their results on NVIDIA DGX systems, including NVIDIA DGX-2, the world's most powerful AI system, featuring 16 fully connected V100 Tensor Core GPUs.

NVIDIA is the only company to have entered as many as six benchmarks, demonstrating the versatility of V100 Tensor Core GPUs for the wide variety of AI workloads deployed today.

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