The A100 Tensor Core GPU set new MLPerf benchmark records last month—outperforming CPUs by up to 237x in data centre inference. In November, Amazon Web Services made eight A100 GPUs available in each of its P4d instances.

For those who prefer their hardware local, the DGX Station A100 is available in either four 80GB A100 GPUs or four 40GB configurations. The monstrous 80GB version of the A100 has twice the memory of when the GPU was originally unveiled just six months ago.

“We doubled everything in this system to make it more effective for customers,” said Paresh Kharya, senior director of product management for accelerated computing at NVIDIA.

NVIDIA says the two configurations provide options for data science and AI research teams to select a system according to their unique workloads and budgets.

Charlie Boyle, VP and GM of DGX systems at NVIDIA, commented:

“DGX Station A100 brings AI out of the data centre with a server-class system that can plug in anywhere.

Teams of data science and AI researchers can accelerate their work using the same software stack as NVIDIA DGX A100 systems, enabling them to easily scale from development to deployment.”

The memory capacity of the DGX Station A100 powered by the 80GB GPUs is now 640GB, enabling much larger datasets and models.

“To power complex conversational AI models like BERT Large inference, DGX Station A100 is more than 4x faster than the previous generation DGX Station. It delivers nearly a 3x performance boost for BERT Large AI training,” NVIDIA wrote in a release.

DGX A100 640GB configurations can be integrated into the DGX SuperPOD Solution for Enterprise for unparalleled performance. Such “turnkey AI supercomputers” are available in units consisting of 20 DGX A100 systems.

Since acquiring ARM, NVIDIA continues to double-down on its investment in the UK and its local talent.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named,” Huang said in September. “We want to propel ARM – and the UK – to global AI leadership.”

NVIDIA’s latest supercomputer, the Cambridge-1, is being installed in the UK and will be one of the first SuperPODs with DGX A100 640GB systems. Cambridge-1 will initially be used by local pioneering companies to supercharge healthcare research.

Dr Kim Branson, SVP and Global Head of AI and ML at GSK, commented:

“Because of the massive size of the datasets we use for drug discovery, we need to push the boundaries of hardware and develop new machine learning software.

We’re building new algorithms and approaches in addition to bringing together the best minds at the intersection of medicine, genetics, and artificial intelligence in the UK’s rich ecosystem.

This new partnership with NVIDIA will also contribute additional computational power and state-of-the-art AI technology.”

The use of AI for healthcare research has received extra attention due to the coronavirus pandemic. A recent simulation of the coronavirus, the largest molecular simulation ever, simulated 305 million atoms and was powered by 27,000 NVIDIA GPUs.

Several promising COVID-19 vaccines in late-stage trials have emerged in recent days which have raised hopes that life could be mostly back to normal by summer, but we never know when the next pandemic may strike and there are still many challenges we all face both in and out of healthcare.

Systems like the DGX Station A100 help to ensure that – whatever challenges we face now and in the future – researchers have the power they need for their vital work.

Both configurations of the DGX Station A100 are expected to begin shipping this quarter.

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AI is helping with COVID-19 research around the world and much of it is being powered by NVIDIA GPUs. It’s a daunting task, new drugs often cost over $2.5 billion in research and development — doubling every nine years — and 90 percent of efforts fail.

Nvidia wants to help speed up discoveries of vital medicines while reducing costs

“COVID-19 hits home this urgency [for new tools],” Huang says.

Huang announced NVIDIA Clara Discovery—a suite of tools for assisting scientists in discovering lifesaving new drugs.

NVIDIA Clara combines imaging, radiology, and genomics to help develop healthcare AI applications. Pre-trained AI models and application-specific frameworks help researchers to find targets, build compounds, and develop responses.

Dr Hal Barron, Chief Scientific Officer and President of R&D at GSK, commented:

“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise.

NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients.

Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the UK’s outstanding scientists.”

Researchers can now use biomedical-specific language models for their work, thanks to a breakthrough in natural language processing. This means researchers can organise and activate large datasets, research literature, and sort through papers or patents on existing treatments and other vital real-world data.

“Where there are popular industry tools, our computer scientists accelerate them,” Huang said. “Where no tools exist, we develop them—like NVIDIA Parabricks, Clara Imaging, BioMegatron, BioBERT, NVIDIA RAPIDS.”

We’re all hoping COVID-19 research – using such powerful new tools available to scientists – can lead to a vaccine within a year or two, when they have often taken a decade or longer to create.

“The use of big data, supercomputing, and artificial intelligence has the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” commented Editor Weatherall, Ph.D., Head of Data Science and AI at AstraZeneca.

During his keynote, Huang provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer – which will be used to further healthcare research – the Cambridge-1.

NVIDIA has established partnerships with companies leading the fight against COVID-19 and other viruses including AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. These partners can harness Cambridge-1 for their vital research.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

And, for organisations wanting to set up their own AI supercomputers, NVIDIA has announced DGX SuperPODs as the world’s first turnkey AI infrastructure. The solution was developed from years of research for NVIDIA’s own work in healthcare, automotive, healthcare, conversational AI, recommender systems, data science and computer graphics.

While Huang has a nice kitchen, I’m sure he’d like to be back on the big stage for his GTC 2021 keynote. We’d certainly all love COVID-19 to be well and truly in the rear-view mirror.

(Photo by Elwin de Witte on Unsplash)

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NVIDIA is investing heavily in the UK’s accelerating AI sector. The company announced its acquisition of legendary semiconductor giant Arm for $40 billion back in September along with the promise to open a new AI centre in Cambridge.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named,” Huang said at the time. “We want to propel Arm – and the UK – to global AI leadership.”

NVIDIA promises to advance Arm’s platform in three major ways:

• NVIDIA will complement Arm partners with GPU, networking, storage and security technologies to create complete accelerated platforms.
• NVIDIA will work with Arm partners to create platforms for HPC, cloud, edge and PC — this requires chips, systems, and system software.
• NVIDIA will port the NVIDIA AI and NVIDIA RTX engines to Arm.

“Today, these capabilities are available only on x86,” Huang said, “With this initiative, Arm platforms will also be leading-edge at accelerated and AI computing.”

Huang also provided more details about NVIDIA’s effort to build the UK’s fastest supercomputer, the Cambridge-1.

Cambridge-1 will boast 400 petaflops of AI performance and will be used by NVIDIA for its vast AI and healthcare collaborations in the UK across academia, industry, and startups.

“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Huang. “The Cambridge-1 supercomputer will serve as a hub of innovation for the UK and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”

The company’s first partners are AstraZeneca, GSK, King’s College London, the Guy’s and St Thomas’ NHS Foundation Trust, and startup Oxford Nanopore. A partnership with GSK will also see the world’s first AI drug discovery lab built in London.

“Because of the massive size of the datasets we use for drug discovery, we need to push the boundaries of hardware and develop new machine learning software,” commented Dr Kim Branson, senior vice president and global head of AI and ML at GSK.

“We’re building new algorithms and approaches in addition to bringing together the best minds at the intersection of medicine, genetics and artificial intelligence in the UK’s rich ecosystem. This new partnership with NVIDIA will also contribute additional computational power and state-of-the-art AI technology.”

While there were some natural concerns that Arm’s acquisition would see operations move from the UK to the US, NVIDIA clearly wants to build up its operations in what’s quickly becoming Europe’s AI epicentre.

(Photo by A Perry on Unsplash)

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The supercomputer will deliver eye-watering amounts of power from its 285,000 CPU cores and 10,000 GPUs (yes, it can probably even run Crysis.)

OpenAI is a non-profit that was founded by one Elon Musk to promote the ethical development of artificial intelligence technologies. Musk, however, departed OpenAI following disagreements over the company’s direction.

Back in February, Musk responded to an MIT Technology Review profile of OpenAI saying that it “should be more open,” and that all organisations “developing advanced AI should be regulated, including Tesla.”

Microsoft invested $1 billion in OpenAI last year and it seems we’re just beginning to see the fruits of that relationship. While most AIs today focus on doing single tasks well, the next wave of research is focusing on performing multiple at once.

“The exciting thing about these models is the breadth of things they’re going to enable,” said Microsoft Chief Technical Officer Kevin Scott.

“This is about being able to do a hundred exciting things in natural language processing at once and a hundred exciting things in computer vision, and when you start to see combinations of these perceptual domains, you’re going to have new applications that are hard to even imagine right now.”

So-called Artificial General Intelligence (AGI) is the ultimate goal for AI research; the point when a machine can understand or learn any task just like the human brain.

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity,” said Sam Altman, CEO, OpenAI. “Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.”

“We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

AGI will, of course, require tremendous amounts of processing power.

Microsoft and OpenAI claim their new supercomputer would rank in the top five but do not give any specific power measurements. To rank in the top five, a supercomputer would currently require more than 23,000 teraflops of performance. The current leader, the IBM Summit, reaches over 148,000 teraflops.

“As we’ve learned more and more about what we need and the different limits of all the components that make up a supercomputer, we were really able to say, ‘If we could design our dream system, what would it look like?’” said Altman. “And then Microsoft was able to build it.”

Unfortunately, for now at least, the supercomputer is built exclusively for OpenAI.

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