- The proliferation of the artificial intelligence and deep learning phenomena that rely on Nvidia’s graphics chips presents the company with a potentially massive growth opportunity.
- The company has a first-mover advantage in the autonomous driving market, which could lead to widespread adoption of its Drive PX self-driving platform.
- The increasing complexity of graphics processing units provides a barrier to entry for most potential rivals, as it would be difficult to match Nvidia’s large R&D budget.
- The artificial intelligence opportunity remains nascent, and it is not a foregone conclusion that Nvidia’s GPUs will dominate.
- Nvidia’s automotive endeavors face plenty of competition, as numerous chipmakers are targeting the market.
- The maturing PC industry—via PC gaming—provides a large portion of the company’s sales.
Morningstar Analyst Abhinav Davuluri Says
Nvidia NVDA is the top designer of discrete graphics processing units that enhance the visual experience on computing platforms. Its chips are used in a variety of end markets, including high-end PCs for gaming and data centers. Traditional GPU uses include professional visualization applications that require realistic rendering, including computer-aided design, video editing, and special effects. Nvidia has experienced success in focusing its GPUs in nascent markets such as artificial intelligence (deep learning) and self-driving vehicles. Hyperscale cloud vendors have leveraged GPUs in training neural networks for uses such as image and speech recognition.
The linchpin of Nvidia’s current business is gaming. PC gaming enthusiasts generally purchase high-end discrete GPUs offered by the likes of Nvidia and AMD. But going forward, we expect the data center segment to drive most of the company’s growth, led by the explosive artificial intelligence phenomenon. This involves collecting large swaths of data followed by techniques that develop algorithms to produce conclusions in the same way as humans. As Moore’s law-led CPU performance improvements have slowed, GPUs have become widespread in accelerating the training of AI models to perform a task. However, we think other solutions are more suitable for inferencing (the deployment of a trained model on new data). Today’s basic variants of AI are consumer-oriented and include digital assistants, image recognition, natural language processing, and recommendation engines.
Nvidia views the car as a “supercomputer on wheels.” Although this segment currently contributes relatively little to the top line, we acknowledge the opportunity Nvidia has to expand its presence in cars beyond infotainment as drivers seek autonomous features in newer vehicles. Nvidia’s Drive PX platform is a deep-learning tool for self-driving that is being used in research and development at more than 370 partners. In 2020, Nvidia acquired Mellanox to bolster its data center offerings in the networking realm, raise switching costs, and improve performance of its existing portfolio.
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