There has been concern that Nvidia GPU’s were just being used for AI training, and in the future, when mass markets using the inferences have been developed, other companies whose chips were cheaper and simpler would take their place. Here is news about huge new contracts for inference use!
Saul
Nvidia Pursues Inference Business
Nvidia Corp. recently announced agreements with two large corporations for drone applications that represent vast revenue potential in the high growth AI and deep learning segment. These moves show Nvidia seeking to secure a substantial share of the AI inference business to build on revenue derived from the training of neural networks. They also illustrate the company targeting the huge Chinese market for AI applications.
The first of the two agreements is with JD.com, (NASDAQ:JD), the second largest online retailing company in China, for the manufacture of drones, with aerial drones representing an area of autonomous robotics beyond autos. Under this agreement, drones will be applied to uses in delivery to retail customers, in agriculture, and for rescue operations.
Nvidia’s Jetson platform will provide navigation intelligence to an aerial drone and to a ground-based drone, with the latter needing to negotiate uneven terrain. JD declared that Jetson was selected due to its low power draw and low cost.
One Million Drones In Next Five Years
As to the performance parameters of the aerial drones, JD has said that they will fly at velocities of up to 100 km an hour to deliver packages of up to 30 kg, with future models perhaps able to deliver packages of approximately 200 kg.
Most interestingly for Nvidia, and demonstrating the centrality of such end markets in the company’s forward planning for inference applications, JD claims it will produce one million drones in the next five years. This projection is largely based on JD’s calculation that drones will reduce logistics costs by 70%, especially when it is considered that parts of China suffer from inadequate infrastructure which impedes overland delivery.
The potential impact on Nvidia’s stock price of the JD agreement is appreciable. With overall revenue from GPUs standing at approximately $1.6 billion in Q2 FY18, this and similar contracts will establish a new income stream for the company which may be described as a sub-group of auto, namely autonomous vehicles excluding automobiles.
Lower Marginal Cost
Much of the groundwork has been laid by the work the company has already done within the autonomous automobile sector, providing Nvidia the opportunity for market expansion of the autonomous segment with lower marginal cost, reaping the benefits of sunk costs and economies of scale.
With the prospect of one million drones being produced by JD alone over the next five years, clearly this inference application for the retail market around the world will be highly revenue-generative for Nvidia when the company’s current 58.39% revenue margin is borne in mind. Expanding the paradigm and building on the knowledge base derived from the JD project to serve other large retail adopters around the globe promises significantly enhanced revenue.
Nvidia currently depends for 58.3% of its revenue on one market, gaming, and consequently is in dire need of broadening its revenue streams to insulate it against a possible downturn in that segment. This provides strategic logic to its pursuit of end-user inference applications: a quest for greater defensive flexibility.
The second of the noteworthy agreements Nvidia has struck to develop aerial drones is with Avitas Systems, a General Electric Company venture. The purpose of the agreement is to combine AI, analytics, and drones to more efficiently undertake inspections of heavy industrial facilities, like oil rigs, where the installation is located in an inhospitable environment. Drones will shoot video of the installation, which will then be analyzed by AI computers to locate issues requiring attention, such as leaks and corrosion.
Hundreds Of Millions Spent On Inspections
Avitas estimates that industrial companies outlay hundreds of millions of dollars each year for inspection and maintenance, much of it mandated by insurance companies. Market expansion for Nvidia within the market for industrial testing, inspection, and certification will lay in the direction of autonomous underwater vehicles and robot crawlers.
As with the JD aerial drone project, when Nvidia has built an inference ecosystem for the Avitas project that will be transferable to other customers with similar operating needs, the company will have achieved a paradigm that will attract new customers within the sector at a lower incremental production cost.
Avitas will utilize Nvidia’s DGX-1 and DGX training systems to achieve defect identification, while developing convolutional neural networks for image categorization, with ancillary generative adversarial neural networks to reduce the compute necessary to identify images.
Large Potential Revenue Stream
Nvidia and Avitas believe that the cost of plant inspections may be reduced through the use of drones by 25%. End-user markets will include oil and gas, petrochemicals, aerospace and defense, construction, food and beverage, brewers and distillers. Transparency Market Research holds that the global market for testing, inspection, and certification will grow at an annual rate of 5.7% to 2024 to reach $285.34 billion, clearly representing a large potential revenue stream for Nvidia.
Previously Nvidia’s growth in AI has been driven by the neural network training market rooted in the data centers of U.S.-based cloud services providers. Now, building on the recent introduction of TensorRT3 software in all of China’s major data centers, the company is developing business in the machine learning inference segment of the fast-evolving Chinese AI market.
China’s State Council anticipates annual IT spending in the country will top $901 billion by 2020, while research firm i-Research projects that China’s AI market will grow at a 50% CAGR. China has been estimated to offer half of the most interesting AI opportunities in the world, according to venture capitalist Jim Breyer.
Inference To Contribute More Than Training
The inference market should in the longer run provide more revenue for Nvidia than will the training market, its erstwhile prime revenue contributor within the AI sector. However as Nvidia moves further towards end-user applications by increasing participation in the inference market, the company may anticipate that the level of competition will escalate.
As a consequence, Nvidia’s unit profit might decline as price competition sets in, yet increased volume, sunk costs and economies of scale should more than offset this consideration. Importantly, full immersion in the inference market is an essential strategic move for the company to maximize its continued growth, as it seeks to remain central to the evolutionary changes of the next generation of AI applications.
With the costs of penetration of the inference market reduced by the amount of research and production experience the company has already acquired through its activities in the autonomous autos market, Nvidia will enjoy a reduced risk position when pursuing end-user inference application opportunities…
https://seekingalpha.com/article/4112117-nvidia-essential-pu…