ARCHITECHT Daily: How long will the good times last for Nvidia?

It must feel pretty good to be Nvidia co-founder and CEO Jensen Huang right now. Ever since deep lear
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ARCHITECHT Daily: How long will the good times last for Nvidia?
By ARCHITECHT • Issue #73 • View online
It must feel pretty good to be Nvidia co-founder and CEO Jensen Huang right now. Ever since deep learning caught fire a few years ago, really kicking off the current machine learning and artificial intelligence revolution, Nvidia has been a critical part of the conversation. After all, GPUs were a big part of the reason deep learning actually worked this time around, and they’re still the engine that powers the (vast?) majority of AI models.
So it’s no surprise that when Nvidia announced its first-quarter earnings on Tuesday, it brought news of a 48 percent year-over-year uptick in revenue—including a nearly 3x uptick in revenue for its Datacenter GPU business. And among the early announcements coming out of its GPU Technology Conference this week, we’ve seen a promise to train 100,000 developers on deep learning this year; a GPU-powered video platform for cities, spanning from camera to the cloud; and partnerships, like this one with H2O, to help bring GPU-powered deep learning to large enterprises not named Google or Facebook.
(On a related note, there are a lot of high-profile conferences happening this week: Nvidia GTC, Microsoft Build, Dell EMC World, OSCON and OpenStack Summit. Did I miss any?)
The company has been on a two- or three-year mission to make machine learning as popular as possible (not that it needed much help), and to position its GPUs as the default hardware platform for doing it. It’s doing pretty well, too: literally (I’m pretty certain) every popular deep learning library and framework is built to run on Nvidia GPUs with its CUDA programming model.
You can’t blame Nvidia for doubling down on machine learning and trying stake its claim as the only processor game in town. Because the good times are not guaranteed to last, especially if Nvidia gets lazy on the innovation front or lays off on the marketing. Lurking in the shadows (if that’s possible), is Intel, which would love to see machine learning workloads run on its line of CPUs, FPGAs and other next-generation gear.
In fact, there’s evidence to suggest FPGAs might actually be viable alternative to GPUs for AI workloads in the data center. And just this week, Intel led an $8 million investment in FPGA software startup Falcon Computing. Two other FPGA-based startups, Flex Logix Technologies and Edico Genome, also raised venture capital (the latter, as noted yesterday, from Dell).
That’s not to mention Google’s decision to build its own AI chips, called Tensor Processing Units, which it claims are largely superior to GPUs for its purposes. This is important: cloud providers like Google, AWS and Microsoft are major purchasers of data center hardware, and are all investing heavily in AI. If those companies, or even two of them, aren’t buying GPUs in bulk, then Nvidia is leaving a lot of money on the table.
Outside the data center, there is a lot of investment in specialized, low-power, chips optimized to run AI workloads right on devices. In other cases, including with the new Caffe 2 deep learning framework recently announced by Facebook, models can run (even if they can’t be trained) on existing hardware such as smartphone processors and the Raspberry Pi.
Would I love to be Nvidia right now? Absolutely. But I’d also spend a lot of time thinking about my next moves to get out in front of the competition, or at least to make sure it remains the stuff of research labs and niche deployments.

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Sponsor: Cloudera
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