However, it’s also facing very important, strong threats from Intel and AMD. Without a doubt, Nvidia still has impressive products and powerful, positive drivers. That, in turn, helps enable the creation of huge “neural networks” which are used to facilitate AI.įinally, as InvestorPlace contributor Louis Navellier pointed out recently, the company’s products are facilitating the deployment of 5G technology. That’s because Mellanox’s technology allows servers to essentially use the memory of other servers without any of them utilizing their operating systems. Meanwhile, Nvidia’s pending acquisition of Mellanox (NASDAQ: MLNX) could enable it to continue benefiting from the explosive growth of AI, even if its GPUs lose market share to Intel’s CPUs. I also predicted that “by the end of this year, many companies - including multiple Nvidia partners - will be offering services with truly autonomous vehicles.” But in 2019, the company’s auto business accounted for less than 10% of its total sales. NVDA Stock Still Has Strong, Positive Catalystsīarron’s reported that ” ADAS (advance driver-assistance systems) is coming on strong,” while Nvidia ” launched a new ADAS offering in January 2019, and NVDA is already partnering with 370 companies in the auto sector.” AMD’s share of the GPU market increased by 3% in Q4 versus Q3, while Nvidia’s share fell by almost 1%. Nvidia’s shipments fell in Q4 even though it’s usually a stronger period for GPUs than Q3. But Nvidia’s sales could start to be meaningfully negatively impacted by the discovery six months to a year from now.Īccording to Jon Peddie Research, AMD’s shipments of GPUs jumped 22.6% in Q4 versus Q3, while Nvidia’s fell 1.9% quarter-over-quarter.

It will likely take time for all the kinks of the new technique to be worked out and to convince data centers to try it. The 10 Best Stocks to Buy After The Market's Historic Sell-OffĪccording to Anshumali Shrivastava, the assistant professor in Rice’s Brown School of Engineering who led the team which invented the new technique, “a top-of-the-line GPU platform like the ones Amazon (NASDAQ: AMZN), Google and others offer for cloud-based deep learning services has eight Tesla V100s and costs about $100,000.”Īlthough I could not find the price of the 44-core Intel chip which the Rice/Intel team used, other types of Xeon chips can be obtained for only a few hundred dollars each. 2 press release announcing the news points out that the researchers were able to achieve the speed increase on computer processing units made by Intel, instead of the much more expensive graphics processing units in which Nvidia specializes.

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