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Nvidia Reinvents GPU, Blows Previous Generation Out of the Water

Jensen Huang’s much-anticipated keynote speech today, postponed from Nvidia’s GPU Technology Conference (GTC) in March, will unveil the company’s eighth-generation GPU architecture. Emerging three years after the debut of the previous generation Volta architecture, Ampere is said to be the biggest generational leap in the company’s history.

Ampere is built to accelerate both AI training and inference, as well as data analytics, scientific computing and cloud graphics.

The first chip built on Ampere, the A100, has some pretty impressive vital statistics. Powered by 54 billion transistors, it’s the world’s largest 7nm chip, according to Nvidia, delivering more than one Peta-operations per second. Nvidia claims the A100 has 20x the performance of the equivalent Volta device for both AI training (single precision, 32-bit floating point numbers) and AI inference (8-bit integer numbers). The same device used for high-performance scientific computing can beat Volta’s performance by 2.5x (for double precision, 64-bit numbers).

Nvidia DGX-A100 systems are installed at the Argonne National Laboratory
Nvidia DGX-A100 systems are installed at the Argonne National Laboratory, where they are being used in the fight against Covid-19 (Image: Argonne National Laboratory)

Hundred billion-dollar industry
In a press pre-briefing ahead of Huang’s keynote today, Paresh Kharya, director of product management for accelerated computing at Nvidia said that the cloud is the biggest growth opportunity for the computer industry; it’s a hundred-billion dollar industry growing 40% per year.

“To advance this industry, what if we were able to create a data center architecture so it not only increases the throughput to scale up and scale out applications to meet their insatiable demands, but it’s fungible to adapt as the [workloads] are changing throughout the day. To create this fungible data center, we have to reimagine our GPU,” Kharya said.

Data centres’ infrastructure has become fragmented because of the diversity in applications, meaning they use compute clusters of many different types, including hardware for AI training separate to AI inference servers. The different compute types needed are difficult to predict as demand for applications can vary throughout the day.

Yes, we have the ownership of the circle R registered trademark.

They [Nvidia] decided to use that name for a one generation product … I guess everyone will be talking about us         </div>
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