While gaming remains Nvidia’s primary source of growth, the company’s artificial intelligence business is growing rapidly. In the second fiscal-quarter, Nvidia reported increased demand for both its graphics and AI chips.

For some idea of Nvidia’s growth in AI, Huang says there are now over 4,000 AI startups working with his company. That number is up from 2,000 in April 2017 and goes to show the growing interest around the technology.

Nvidia continues to innovate in AI and is quickly establishing itself as a major player; particularly in driverless cars.

The company’s self-driving platform DRIVE is powered by Nvidia Xavier chips and consists of three parts:

1. DRIVE AV — This part of the DRIVE platform was also available in the previous generation and uses neural networks to perform the calculations required for self-driving cars.
2. DRIVE IX — The first of the two new inclusions is a software development kit which enables AI assistants that can harness data from sensors inside and outside the vehicle.
3. DRIVE AR — While there have been many advancements around self-driving technologies and AI assistants for cars, augmented reality is still relatively unexplored. With DRIVE AR, Nvidia intends to enable new graphical experiences which can deliver things such as information about points of interest along the route.

Nvidia says its Xavier chips have been in development for over four years and represents the work of over 2,000 engineers. Xavier features more than nine billion transistors and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created.

In a conference call last August, Huang teased Tesla’s reported difficulties in building its own AI chips: “It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

Autonomous car manufacturers need to train petabytes of data per day, reiterate their models, and deploy them again in order to get those vehicles to market. Nvidia has a server, equivalent to 800 traditional servers, called the DGX-2 which delivers two petaflops of performance.

During AI Expo London earlier this year, Nvidia senior director of enterprise David Hogan said: “The compute demands of AI are huge and beyond what anybody has seen within a standard enterprise environment before. You cannot train a neural network on a standard CPU cluster.”

While Nvidia is well-positioned in AI, and is already reaping the benefits, it foresees gaming continuing to be its primary source of revenue for some time.

Nvidia reported earnings per share of $1.24 on revenues of $2.58 billion in the second fiscal quarter ended July 31. Gaming was about 50.3% of total revenues, and GPUs were 81% of revenues.

Huang believes Nvidia’s GPU sales will increase thanks to the growing popularity of ‘ray tracing’ in games, deliver stunning realism by enabling things such as full-detail reflections which mirror the action happening in and around the player’s field of view:

Few games offered support for ray tracing last year and supporting RTX chips were expensive, so sales were subdued. That’s changed this year, with more supporting games and cheaper RTX chips available; a combination which produced $1.3 billion in gaming revenue for Nvidia this quarter.

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Speaking to investors and media at Volvo’s annual event for the capital-markets community, Volvo Group CEO Martin Lundstedt said:

“Partnership is the new leadership. If we are to succeed in the future with speed, quality, and safety – and to gain benefits of autonomous driving – we need to partner up with the best guys. In this world of unknowns, you need a partnership built on trust.”

Volvo is the world’s second-largest truckmaker after Daimler. The carmaker demonstrated its first cabin-less autonomous truck called Vera last year.

By applying AI to trucks, Volvo hopes to disrupt industries from public and freight transport, to forestry and construction.

Jensen Huang, NVIDIA founder and CEO, commented:

“Trucking is the world’s largest network – a network that through online shopping puts practically anything, anywhere in the world, quickly within our reach.

The latest breakthroughs in AI and robotics bring a new level of intelligence and automation to address the transportation challenges we face. We are thrilled to partner with Volvo Group to reinvent the future of trucking.”

The deal with Volvo is a particular boost for Nvidia and its AI technology for self-driving cars after Tesla ditched the firm last year. Tesla CEO Elon Musk said the company would focus on creating its own chips.

Nvidia debuted its ‘Xavier’ processors for the company’s DRIVE autonomous car platform last year. Xavier was in development for over four years, represents the work of over 2,000 engineers, features more than nine billion transistors, and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created

During a conference call in August last year, Huang said: “It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

Nvidia has made a name for itself as the maker of powerful GPUs, traditionally for gaming purposes. The company has been increasingly shifting gears into other computation-heavy areas like AI and machine learning.

Automotive chips accounted for $641 million of Nvidia’s $11.7 billion in revenue in its most recent fiscal year.

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Tesla currently uses Nvidia’s silicon for its vehicles. The company’s CEO, Elon Musk, said earlier this month that he’s a “big fan” of Nvidia but that an in-house AI chip would be able to outperform those of the leading processor manufacturer.

During a conference call on Thursday, Huang said its customers are “super excited” about  Nvidia’s Xavier technology for autonomous machines. He also notes that it’s currently in production, whereas Tesla’s rival is yet-to-be-seen.

Here’s what Huang had to say during the call:

“With respect to the next generation, it is the case that when we first started working on autonomous vehicles, they needed our help. We used the 3-year-old Pascal GPU for the current generation of Autopilot computers.

It’s very clear now that in order to have a safe Autopilot system, we need a lot more computing horsepower. In order to have safe computing, in order to have safe driving, the algorithms have to be rich. It has to be able to handle corner conditions in a lot of diverse situations.

Every time there are more and more corner conditions or more subtle things that you have to do, or you have to drive more smoothly or be able to take turns more quickly, all of those requirements require greater computing capability. And that’s exactly the reason why we built Xavier. Xavier is in production now. We’re seeing great success and customers are super excited about Xavier.

That’s exactly the reason why we built it. It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

The conference call was carried out following the release of Nvidia’s fiscal earnings report where the company reported better-than-expected earnings.

“Growth across every platform – AI, Gaming, Professional Visualization, self-driving cars – drove another great quarter,” said Huang. “Fueling our growth is the widening gap between demand for computing across every industry and the limits reached by traditional computing.”

However, due to lower-than-expected revenue guidance, Nvidia stock fell by six percent on Thursday following the earnings report.

What are your thoughts on Huang’s comments? Let us know below.

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Xavier is designed to power the NVIDIA DRIVE software stack which has been expanded to a trio of AI platforms covering every aspect of the experience inside next-generation automobiles.

DRIVE AV — This part of the DRIVE platform was also available in the previous generation and uses neural networks to perform the calculations required for self-driving cars.

DRIVE IX — The first of the two new inclusions is a software development kit which enables AI assistants that can harness data from sensors inside and outside the vehicle.

DRIVE AR — While there have been many advancements around self-driving technologies and AI assistants for cars, augmented reality is still relatively unexplored. With DRIVE AR, Nvidia intends to enable new graphical experiences which can deliver things such as information about points of interest along the route.

The processor has been in development for over four years and represents the work of over 2,000 engineers. Xavier features more than nine billion transistors and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created.

Autonomous vehicles require data to be captured and processed at very fast rates. To help achieve this, Xavier is built around a custom 8-core CPU. The processor also includes a new 512-core Volta GPU, deep learning accelerator, new computer vision accelerators, and 8K HDR video processors.

These features come together to calculate 30 trillion operations per second while consuming just 30 watts. Nvidia claims this generation of its DRIVE platform is 15 times more energy efficient than its previous.

Nvidia Pegasus is the company’s supercomputer for self-driving cars. It uses two Xavier SoCs in tandem with two next-generation GPUs to deliver 320 trillion operations per second of performance.

Customers are due to receive their first samples of Pegasus in mid-2018.

What are your thoughts on Nvidia’s new Xavier SoC?

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