Moore’s Law, a tenet of electrical engineering from the mid-1960s which states that the number of transistors in a processor doubles every two years, has come to a halt. As Moore’s law has stalled, the amount of digital data has exploded, which requires more computing power. This need for computational improvements has spurred interest in using the full power of the parallel computing power available in graphics processing units (GPUs).
Computing has become increasingly visually complex, moving from two color graphics in the 1980s to the high definition displays in an iPhone today, powered by specialized graphics chips. Nvidia’s roots are in high performance desktop video gaming, which requires high computational power to display rapidly shifting, complex, visually detailed environments at high frame rates. Machine learning programs run especially well on hardware that is designed for massively parallel calculations, which is precisely what Nvidia’s family of GPUs do. A central processing unit (CPU) could be analogized as a long and complex assembly line compared to the GPU’s batch process.
Machine learning needs large amounts of parallel computational capacity to crunch ‘wide’ data sets. The machine learning revolution has pulled Nvidia away from its core market of video gaming and thrust their GPUs into data centers. CEO Jensen Huang has the opportunity to be the machine learning leader, in a market that is estimated to be worth $50B in data center sales alone.
Nvidia’s core challenge is how to serve innovative technology companies with valuable services and hardware while keeping competition from AMD, Intel, and startup chipmakers at bay.
In the short term, Nvidia is driving hardware innovation with graphics cards that surpass what AMD can offer, such as the 10th generation GeForce series and the Titan V professional card which offer dramatic increases in generalized compute performance over the competition and superior performance per dollar. In addition, Nvidia has responded to highly specialized needs in promising markets, such as Drive CX and Drive PX, in the autonomous automotive market. These two products combine hardware and software to provide real time high performance machine learning for cars to offer assisted or full autonomous driving capability.
Longer term, Nvidia needs to keep its GPU products at the forefront of computationally intensive tasks. Currently, Nvidia partners with businesses across industries such as automotive, genomics, medical devices, and manufacturing as well as top universities to provide advanced software developed advanced and optimized hardware and software. In the five years since 2013, Nvidia has flipped from employing 60% hardware engineers to 60% software engineers to support these partnerships. The company has also launched a program called Nvidia Inception, designed to provide access to technology, software support, expertise, market exposure and capital to 1,300 startups that are applying machine learning.
Nvidia’s core challenge is to maintain its lead in GPU hardware and to ensure that existing and potential customers develop machine learning software that runs best on Nvidia.
Given the existence of powerful and inexpensive cloud computing platforms that use Nvidia like Amazon Web Services and Google Cloud, it does not make sense for Nvidia to build its own cloud offering. Instead, Nvidia should target companies that need a high performance on-premise solution, such as national research laboratories, the defense industry and universities.
Second, Nvidia should design an enterprise customer hardware leasing and upgrade program on top of the existing software partnerships for use in specialized applications. In the driverless car space, customers would receive faster DrivePX hardware every year that would simply be plugged into the car, bringing faster processing and advanced autonomous features. In both the driverless car space and medical imaging, faster hardware means safer cars and more accurate diagnoses. This program would ensure that customers across all industries will have the latest and greatest hardware running increasingly sophisticated software.
Lastly, Nvidia cannot forget about the consumer gaming business. This is the one segment where AMD offers real competition to Nvidia and continued dominance is predicated upon wise research and development spending. The datacenter division may be the fastest growing segment, but gaming is still by far the largest business at $5.5B in FY2018 and is still growing fast enough that gaming added $1.4B year over year, when the entire datacenter business in the last year was $1.9B.
Given the tension in computing between proprietary, closed and high performance systems vs open source, standardized systems, how do you perceive Nvidia’s market strategy? How would you persuade customers to use your machine learning hardware and software, while also hosting their precious business data, in an era where companies want complete control over data?
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