MLPerf consists of five inference benchmarks which cover the main three AI applications today: image classification, object detection, and translation.

“Industry-standard MLPerf benchmarks provide relevant performance data on widely used AI networks and help make informed AI platform buying decisions,” said Rangan Majumder, VP of Search and AI at Microsoft.

Last year, NVIDIA led all five benchmarks for both server and offline data centre scenarios with its Turing GPUs. A dozen companies participated.

23 companies participated in this year’s MLPerf but NVIDIA maintained its lead with the A100 outperforming CPUs by up to 237x in data centre inference.

For perspective, NVIDIA notes that a single NVIDIA DGX A100 system – with eight A100 GPUs – provides the same performance as nearly 1,000 dual-socket CPU servers on some AI applications.

“We’re at a tipping point as every industry seeks better ways to apply AI to offer new services and grow their business,” said Ian Buck, Vice President of Accelerated Computing at NVIDIA.

“The work we’ve done to achieve these results on MLPerf gives companies a new level of AI performance to improve our everyday lives.”

The widespread availability of NVIDIA’s AI platform through every major cloud and data centre infrastructure provider is unlocking huge potential for companies across various industries to improve their operations.

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The specific GPU added to Google Cloud is the NVIDIA A100 Tensor Core which was announced just last month. NVIDIA says the A100 “has come to the cloud faster than any NVIDIA GPU in history.”

NVIDIA claims the A100 boosts training and inference performance by up to 20x over its predecessors. Large AI models like BERT can be trained in just 37 minutes on a cluster of 1,024 A100s.

For those who enjoy their measurements in teraflops (TFLOPS), the A100 delivers around 19.5 TFLOPS in single-precision performance and 156 TFLOPS for Tensor Float 32 workloads.

Manish Sainani, Director of Product Management at Google Cloud, said:

“Google Cloud customers often look to us to provide the latest hardware and software services to help them drive innovation on AI and scientific computing workloads.

With our new A2 VM family, we are proud to be the first major cloud provider to market NVIDIA A100 GPUs, just as we were with NVIDIA T4 GPUs. We are excited to see what our customers will do with these new capabilities.”

The announcement couldn’t have arrived at a better time – with many looking to harness AI for solutions to the COVID-19 pandemic, in addition to other global challenges such as climate change.

Aside from AI training and inference, other things customers will be able to achieve with the new capabilities include data analytics, scientific computing, genomics, edge video analytics, and 5G services.

The new Ampere-based data center GPUs are now available in Alpha on Google Cloud. Users can access instances of up to 16 A100 GPUs, which provides a total of 640GB of GPU memory and 1.3TB of system memory.

You can register your interest for access here.

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