I asked Huang to compare the GTC of eight years ago to the GTC of today, given how much of Nvidia's focus has changed. "We invented a computing model called GPU accelerated computing and we introduced it almost slightly over 10 years ago," Huang said, noting that while AI is only recently dominating tech news headlines, the company was working on the foundation long before that. "And so we started evangelizing all over the world. GTC is our developers conference for that. The first year, with just a few hundred people, we were mostly focused in two areas: They were focused in computer graphics, of course, and physics simulation, whether it's finite element analysis or fluid simulations or molecular dynamics.
In 1993, at the age of 30, he co-founded Nvidia and has occupied the top executive spot ever since. What began as a provider of relatively niche graphics processing units (GPUs) with a narrow field of general computing uses has evolved to become, arguably, the bedrock underlying the current AI market explosion. As Nvidia gears up for its eighth annual GPU Technology Conference (GTC), which happens May 8-11 in San Jose, the company has a lot to celebrate. Its stock price hit record highs this year, its tech was everywhere at CES, and in addition to general AI applications, it's found a new collection of deep-pocketed partners among automakers looking to usher in autonomous driving using neural networks powered by GPUs. I spoke to Huang about how the company got to where it is today, as well as what GTC has become in the general landscape and what it means to Nvidia.
San Francisco: Jensen Huang, co-founder, president and chief executive officer of Santa Clara-based Nvidia Corp., says that the rapid adoption of artificial intelligence (AI) technologies such as machine learning, deep learning, natural language processing and computer vision augur well for the growth prospects of his company. His confidence stems from the fact that Nvidia designs the chips that can deliver the extra computing power that clients need in an algorithm-driven world, which is increasingly using these AI technologies to make business sense of the voluminous data that users generate and thus gain a competitive edge. These chips, called graphics processing units (GPUs), helped Nvidia fuel the growth of the personal computer gaming market almost two decades back. Huang hopes the increasing use of GPUs for AI will help his company repeat the success. Huang argues that even when you increase the number of central processing unit transistors in a computer, they result in a small increase in application performance, whereas GPUs, which are specifically designed to handle multiple tasks simultaneously, make them more suitable for high-performance computing tasks.
Nvidia cofounder Chris Malachowsky is eating a sausage omelet and sipping burnt coffee in a Denny's off the Berryessa overpass in San Jose. It was in this same dingy diner in April 1993 that three young electrical engineers–Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang–started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. East San Jose was a rough part of town back then–the front of the restaurant was pocked with bullet holes from people shooting at parked cop cars–and no one could have guessed that the three men drinking endless cups of coffee were laying the foundation for a company that would define computing in the early 21st century in the same way that Intel did in the 1990s. "There was no market in 1993, but we saw a wave coming," Malachowsky says. "There's a California surfing competition that happens in a five-month window every year. When they see some type of wave phenomenon or storm in Japan, they tell all the surfers to show up in California, because there's going to be a wave in two days.
It was in this same dingy diner in April 1993 that three young electrical engineers--Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang--started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. "We've been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia's platform," says Marc Andreessen of venture capital firm Andreessen Horowitz. Starting in 2006, Nvidia released a programming tool kit called CUDA that allowed coders to easily program each individual pixel on a screen. From his bedroom, Krizhevsky had plugged 1.2 million images into a deep learning neural network powered by two Nvidia GeForce gaming cards.