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Google breaks AI performance records in MLPerf with world’s fastest training supercomputer

Google said it has built the world’s fastest machine learning (ML) training supercomputer that broke AI performance records in six out of eight industry-leading MLPerf benchmarks.

Using this supercomputer, as well as the latest Tensor Processing Unit (TPU) chip, Google has set new performance records. Here at Google, recent ML-enabled advances have included more helpful search results and a single ML model that can translate 100 different languages.

The latest results from the industry-standard MLPerf benchmark competition demonstrate that Google has built the world’s fastest ML training supercomputer. Using this supercomputer, as well as our latest Tensor Processing Unit (TPU) chip, Google set performance records in six out of eight MLPerf benchmarks.

Google’s latest TPU supercomputer can train the same model almost five orders of magnitude faster just five years later. MLPerf models are chosen to be representative of cutting-edge machine learning workloads that are common throughout industry and academia. The supercomputer Google used for the MLPerf training round is four times larger than the “Cloud TPU v3 Pod” that set three records in the previous competition.

Graphics giant Nvidia said it also delivered the world’s fastest Artificial Intelligence (AI) training performance among commercially available chips, a feat that will help big enterprises tackle the most complex challenges in AI, data science and scientific computing. Nvidia A100 GPUs and DGX SuperPOD systems were declared the world’s fastest commercially available products for AI training, according to MLPerf benchmarks.

The world’s leading cloud providers are helping meet the strong demand for Nvidia A100, such as Amazon Web Services (AWS), Baidu Cloud, Microsoft Azure and Tencent Cloud, as well as dozens of major server makers, including Dell Technologies, Hewlett Packard Enterprise, Inspur and Supermicro. “Users across the globe are applying the A100 to tackle the most complex challenges in AI, data science and scientific computing,” said the company.

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