|From: Frank Sully
|8/1/2021 9:29:11 PM
|Will Nvidia’s huge bet on artificial-intelligence chips pay off?
The unassuming chipmaking giant was early to the AI revolution—and remains ahead of rivals
Aug 1st 2021
“WE’RE ALWAYS 30 days away from going out of business,” is a mantra of Jen-Hsun Huang, co-founder of Nvidia, a semiconductor company. That may be a little hyperbolic coming from the boss of a company whose market value has increased from $31bn to $486bn in five years and which has eclipsed Intel, once the world’s mightiest chipmaker, by selling high-performance chips for gaming and artificial intelligence (AI). But only a little. As Mr Huang observes, Nvidia is surrounded by “giant companies pursuing the same giant opportunity”. To borrow a phrase from Intel’s co-founder, Andy Grove, in this fast-moving market “only the paranoid survive”.
Constant vigilance has served Nvidia well. Between 2016 and 2021 its revenues grew by 233%. In the three months to May sales expanded by a dizzying 84%, year on year, and gross margin reached 64%. Although Intel’s revenues are four times as large and the older firm fabricates chips as well as designing them, investors value Nvidia’s design-only business more highly (twice as much in terms of market capitalisation). Its hardware and accompanying software are used in all data centres that make up the computing clouds operated by Amazon, Google, Microsoft and China’s Alibaba. Nvidia’s systems have been adopted by every big information-technology (IT) firm, as well as by countless scientific research teams in fields from drug discovery to climate modelling. It has created a broad, deep “moat” that protects its competitive advantage.
Now Mr Huang wants to make it broader and deeper still. In September Nvidia confirmed rumours that it was buying Arm, a Britain-based firm that designs zippy and energy-efficient chips for most of the world’s smartphones, for $40bn. The idea is to use Arm’s design prowess to engineer central processing units (CPUs) for data centres and AI uses that would complement Nvidia’s existing strength in specialised chips known as graphics-processing units (GPUs). Given the global reach of Arm and Nvidia, regulators in America, Britain, China and the European Union must all approve the deal. If they do—a considerable “if”, given both firms’ market power in their respective domains—Nvidia’s position in one of computing’s hottest fields would look near-unassailable.
Mr Huang, whose family immigrated to America from Taiwan when he was a child, founded Nvidia in 1993. For its first 20 years or so the company made GPUs that made video games look lifelike. In the past decade, however, it turned out that GPUs also excel in another futuristic, but less frivolous, area of computing: they dramatically speed up how fast machine-learning algorithms can be trained to perform tasks by feeding them oodles of data. Four years ago Mr Huang, who goes by Jensen, startled Wall Street with a blunt assessment of his company’s prospects in what has become known as accelerated computing. It could “work out great”, he said, “or terribly”. Regardless, the company was “all in”.
Around half of Nvidia’s annual revenues of $17bn still comes from gaming chips. They have also proved excellent at solving the mathematical puzzles that underpin ethereum, a popular cryptocurrency. This has at times injected crypto-like volatility to GPU sales, which contributed to a near-50% fall in Nvidia’s share price in late 2018. Another slug of sales comes from selling chips that accelerate features other than graphics or AI to computer-makers and car companies.
But the AI business is growing fast. It includes specialised chips as well as advanced software that lets programmers fine-tune them—itself enabled by an earlier bet by Mr Huang, which some investors criticised at the time as an expensive distraction. In 2004 Mr Huang started investing in “Cuda”, a base software layer that enables just such fine-tuning, and implanting it in all of Nvidia’s chips.
A lot of these systems end up in servers, the powerful computers that undergird data centres’ processing oomph. Sales to data centres have increased from 25% of total revenues in early 2019 to 36%, contributing nearly as much as to the total as gaming GPUs. As companies across various industries adopt AI, the share of Nvidia’s data-centre sales going to big cloud providers such as Amazon and Google has declined from 100% to half that.
Today its AI hardware-software combo is designed to work seamlessly with the machine-learning algorithms collected in libraries such as TensorFlow (which is maintained by Google) and PyTorch (run by Facebook), boosting the algorithms’ number-crunching power. Nvidia has created programs to hook its hardware and software up to the IT systems of big business customers with AI projects of their own. All this makes AI developers’ job immeasurably easier, says a former Nvidia executive. Nvidia is also expanding into AI “inference”: running AI models, hitherto the preserve of CPUs, rather than merely training them. Real-time, huge AI models like those used for speech recognition or content-recommendation systems increasingly need the specialised GPUs to perform well, says Ian Buck, head of Nvidia’s accelerated-computing business.
This is also where Arm comes in. Owning it would give Nvidia the CPU chops to complement its historic strength in GPUs and more recently acquired abilities in network-interface cards needed to run server farms (in 2019 Nvidia acquired Mellanox, a specialist in such interconnecting technology). In April the company unveiled plans for its first data-centre CPU, Grace, a high-performance chip based on an Arm design. Arm’s energy-efficient chips would help Nvidia supply AI products for “edge computing”—in self-driving cars, factory robots and other places away from data centres, where power-hungry GPUs may not be ideal.
Transistors in microprocessors are already the size of a few atoms, so have little room to shrink and tricks such as outsourcing computing to the cloud, or using software to split a physical computer into several virtual machines, may run their course. So businesses are expected to turn to accelerated computing as a way to gain processing power without spending through the roof on ever more CPUs. Over the next five to ten years, as AI becomes more common, up to half of the $80bn-90bn that is spent annually on servers could shift to Nvidia’s accelerated-computing model, estimates Stacy Rasgon of Bernstein, a broker. Of that, half could go on accelerated chips, a market which Nvidia’s GPUs dominate, he says. Nvidia thinks the global market for accelerated computing, including data centres and the edge, will be more than $100bn a year.
Nvidia is not the only one to have spotted the opportunity. Competitors are proliferating, from startups to other chipmakers and the tech giants. Venture capitalists have backed companies such as Tenstorrent, Untether AI, Cerebras and Groq, all of which are trying to make semiconductors even better suited to AI than Nvidia’s GPUs, which for all their virtues can be power-hungry and fiddly to program. Graphcore, a British firm, is touting its “intelligence-processing unit”.
In 2019 Intel bought an Israeli AI-chip startup called Habana Labs and ceased work on the neural-network processors it had acquired as part of an earlier purchase of Nervana Systems, another startup. Amazon Web Services (AWS), the e-commerce giant’s cloud division, will soon start offering Habana’s Gaudi accelerators to its cloud customers, claiming that the Gaudi chips, which are slower than Nvidia’s GPUs, are nevertheless 40% cheaper relative to performance. Advanced Micro Devices (AMD), a veteran chipmaker that is Nvidia’s main rival in the gaming market and Intel’s in the CPU business, is in the process of finalising a $35bn deal to acquire Xilinx, which makes another kind of accelerator chip called field programmable gate arrays (FPGAs).
A bigger threat comes from Nvidia’s biggest customers. The cloud giants are all designing their own custom silicon. Google was the first to come up with its “tensor-processing unit”. Microsoft’s Azure cloud division opted for FPGAs. Baidu, China’s search giant, has its “Kunlun” chips for AI and Alibaba, its e-commerce titan, has Hanguang 800. AWS already has a chip designed for inference, called Inferentia, and has one coming for training. “The risk is that in ten years’ time AWS will offer a cheap AI box with all AWS-made components,” says the former Nvidia executive. Mark Lipacis at Jefferies, an investment bank, notes that since mid-2020 AWS has put Inferentia into an ever-greater share of its offering to customers, potentially at the expense of Nvidia.
As for the Arm acquisition, it is far from a done deal. Arm’s customers include all of the world’s chipmakers as well as AWS and Apple, which uses Arm chips in its iPhones. Some have complained that Nvidia could restrict access to the chip designer’s blueprints. The Graviton2, AWS’s tailor-made server chip, is based on an Arm design. Nvidia says it has no plans to change Arm’s business model. Western regulators are due to decide on whether to approve the deal with Britain’s competition authority, which had until July 30th to scrutinise the transaction and is expected to be among the first to do so. China, for its part, is unlikely to welcome an American takeover of an important supplier to its own tech firms, which is currently owned by SoftBank, a Japanese technology conglomerate.
Even if one of the antitrust watchdogs puts paid to the acquisition, however, Nvidia’s prospects look bright. Venture capitalists have become markedly less enthusiastic over time about backing startups taking on Nvidia and the tech giants investing in accelerated computing, says Paul Teich of Equinix, an American data-centre operator. Intel has overpromised many things, including accelerated computing, for years, and mostly undelivered. AWS and the rest of big tech have plenty of other things on their plates and lack Nvidia’s clear focus on accelerated computing. Nvidia says that, measured by actual utilisation by businesses, it has not ceded market share to AWS’s Inferentia.
Mr Huang says that it is the expense of training and running AI applications that matters, not the cost of hardware components. On that measure, he says, “we are unrivalled on price-for-performance.” None of Nvidia’s rivals possess its software ecosystem. And it has a proven ability to switch gears and capitalise on good luck. “They’re always looking around at what’s out there,” enthuses another former executive. And with an entrenched position, Mr Lipacis says, it also benefits from inertia.
Investors have not forgotten the near-halving of Nvidia’s share price in 2018. It may still be partly tied to the fortunes of the crypto market. Holding Nvidia stock requires a strong stomach, says Mr Rasgon of Bernstein. Nvidia may present itself as a pillar of the industry, but it remains an aggressive, founder-led firm that behaves like a startup. Sprinkle in some paranoia, and it will be hard to disrupt.
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|From: Frank Sully
|8/2/2021 2:14:24 AM
|NVIDIA And King’s College London Uses Cambridge-1 To build AI Models To Generate Synthetic Brain Images
August 1, 2021
NVIDIA and King’s College London have revealed new information about one of the first projects to be run on Cambridge-1, the UK’s most powerful supercomputer. The UK’s most powerful supercomputer, Cambridge-1, was announced in October last year and cost $100 million to build.
King’s College London uses Cambridge-1 to create AI models that can generate synthetic brain images by learning from tens of thousands of MRI (Magnetic resonance imaging) brain scans of people of all ages and disorders.
The company’s early collaborations with AstraZeneca, GSK, Guy’s and St Thomas’ NHS Foundation Trust, King’s College London, and Oxford Nanopore Technologies include:-
Scientists will be able to distinguish healthy brains from diseased brains due to this new research, providing them a more sophisticated knowledge of how diseases appear and potentially allowing for earlier and more accurate diagnoses. Jorge Cardoso, a senior lecturer of artificial medical intelligence at King’s College London, mentioned that Cambridge-1 allows accelerated generation of synthetic data that gives researchers at King’s College London a better understanding of how different factors affect the brain, anatomy, and pathology. Jorge also added that you could ask their model to generate an almost infinite amount of data with prescribed ages and diseases. With this, they can start tackling problems such as how diseases affect the brain and when abnormalities might start existing.
- Developing a deeper understanding of brain diseases similar to dementia.
- Using AI to design new drugs.
- Improving the accuracy of being able to find disease-causing variations in human genomes.
AI for healthcare is proliferating in the UK, with a range of startups and larger pharmaceutical companies turning to mine the vast quantities of data available to discover potential drugs, further understand certain diseases, and hence, improve and personalize patient care.
The use of synthetic data has the extra benefit of ensuring patient privacy since the images were AI-generated. This also allows King’s to open the research to the broader UK healthcare community.
The AI model was created by data scientists and engineers from King’s and NVIDIA. It’s one of the numerous ongoing initiatives on Cambridge-1. Drug discovery and genome sequencing are among the digital biology projects proposed by other top UK healthcare organizations.
With 80 NVIDIA DGXTM A100 systems integrating NVIDIA A100 GPUs, BlueField®-2 DPUs, and NVIDIA HDR InfiniBand networking, Cambridge-1 is the UK’s most powerful supercomputer.
The synthetic data model developed by King’s College London will be shared with the more extensive research and startup community.
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|From: Frank Sully
|8/2/2021 3:16:53 PM
|Nvidia AI development hub now available to North American customers
Monthly subscription pricing to the Nvidia Base Command Platform starts at $90,000, with a three-month minimum.
By Jonathan Greig | August 2, 2021 -- 13:00 GMT (06:00 PDT)
Nvidia announced on Monday that its new hosted AI development hub -- the Nvidia Base Command Platform -- is now available to North American customers after debuting in May.
Nvidia said in a statement that the platform "provides enterprises with instant access to powerful computing infrastructure wherever their data resides."
The tool is available to be rented for a monthly subscription price of $90,000. There is a three-month minimum to all subscriptions, Nvidia explained.
Manuvir Das, head of Enterprise Computing at Nvidia, said the Base Command Platform makes it easy for enterprises to instantly access the power of an Nvidia DGX SuperPOD to "accelerate the AI and data science development lifecycle."
The platform gives companies access to Nvidia DGX SuperPODTM supercomputers through optimized AI workflow software, and the tool is hosted remotely by Equinix. According to a statement from the company, the Base Command Platform is the first Nvidia-powered hybrid cloud offering available through the Nvidia AI LaunchPad partner program.
The tool is tailored for organizations that have large-scale, multiuser and multi-team AI workflows looking to push AI projects into production.
Nvidia announced that Adobe was already using the tool to help researchers and data scientists work "simultaneously on shared accelerated computing resources to speed up the development of new AI-powered software features and applications."
Abhay Parasnis, CTO and chief product officer at Adobe, said the platform requires little effort to onboard AI developers.
"Our team is exploring the potential of Base Command Platform to simplify the machine learning development workflow," Parasnis said.
The tool is supported by a number of Nvidia partner organizations like NetApp and Equinix, and Weights and Biases, which offers MLOps software for the Base Command Platform.
In addition to a cloud-based user interface, the tool comes with a command-line API, integrated monitoring and reporting dashboards to accelerate the AI development lifecycle, incorporating a "broad range of AI and data science tools" like the Nvidia NGCTM catalog of AI and analytics software.
Equinix vice president Steve Steinhilber added that businesses often struggle to provide the simple yet powerful digital infrastructure that researchers and scientists can share efficiently when it comes to AI.
The Base Command Platform is "the fastest and most cost-effective way to tap into the leading performance of an Nvidia DGX SuperPOD to accelerate AI development, seamlessly access distributed data lakes wherever they may be located via Equinix Fabric, and quickly deploy developed and tested algorithms to inference engines all over the world," Steinhilber explained.
Kim Stevenson, senior vice president and general manager of the foundational data services group at NetApp, noted that the tool was a cloud-hosted solution for end-to-end AI development with fully managed AI infrastructure.
"Enterprises want to simplify AI experimentation and streamline workflow management across teams of users and jobs," Stevenson said.
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