Nvidia has a market cap of roughly $550 billion compared to Apple’s nearly $2.5 trillion. We believe Nvidia can surpass Apple by capitalizing on the artificial intelligence economy, which will add an estimated $15 trillion to GDP. This is compared to the mobile economy that brought us the majority of the gains in Apple, Google and Facebook, and contributes $4.4 trillion to GDP. For comparison purposes, AI contributes $2 trillion to GDP as of 2018.
While mobile was primarily consumer, and some enterprise with bring-your-own-device, artificial intelligence will touch every aspect of both industry and commerce, including consumer, enterprise, and small-to-medium sized businesses, and will do so by disrupting every vertical similar to cloud. To be more specific, AI will be similar to cloud by blazing a path that is defined by lowering costs and increasing productivity.
I have an impeccable record on Nvidia including when I stated the sell-off in 2018 was overblown and missing the bigger picture as Nvidia has two impenetrable moats: developer adoption and the GPU-powered cloud. This was when headlines were focused exclusively on Nvidia’s gaming segment and GPU sales for crypto mining.
Although Nvidia’s stock is doing very well this year, this has been a fairly contrarian stance in the past. Not only was Nvidia wearing the dunce hat in 2018, but in August of 2019, the GPU data center revenue was flat to declining sequentially for three quarters, and in fiscal Q3 2020, also declined YoY (calendar Q4 2019). We established and defended our thesis on the data center as Nvidia clawed its way back in price through China tensions, supply shortages, threats of custom silicon from Big Tech, cyclical capex spending, and on whether the Arm acquisition will be approved.
Suffice to say, three years later and Nvidia is no longer a contrarian stock as it once was during the crypto bust. Yet, the long-term durability is still being debated —- it’s a semiconductor company after all —- best to stick with software, right? Right? Not to mention, some institutions are still holding out for Intel. Imagine being the tech analyst at those funds (if they’re still employed!).
Before we review what will drive Nvidia’s revenue in the near-term, it bears repeating the thesis we published in November of 2018:
Nvidia is already the universal platform for development, but this won’t become obvious until innovation in artificial intelligence matures. Developers are programming the future of artificial intelligence applications on Nvidia because GPUs are easier and more flexible than customized TPU chips from Google or FGPA chips used by Microsoft [from Xilinx]. Meanwhile, Intel’s CPU chips will struggle to compete as artificial intelligence applications and machine learning inferencing move to the cloud. Intel is trying to catch-up but Nvidia continues to release more powerful GPUs – and cloud providers such as Amazon, Microsoft and Google cannot risk losing the competitive advantage that comes with Nvidia’s technology.
The Turing T4 GPU from Nvidia should start to show up in earnings soon, and the real-time ray-tracing RTX chips will keep gaming revenue strong when there is more adoption in 6-12 months. Nvidia is a company that has reported big earnings beats, with average upside potential of 33.35 percent to estimates in the last four quarters. Data center revenue stands at 24% and is rapidly growing. When artificial intelligence matures, you can expect data center revenue to be Nvidia’s top revenue segment. Despite the corrections we’ve seen in the technology sector, and with Nvidia stock specifically, investors who remain patient will have a sizeable return in the future.”
Notably, the stock is up 335% since my thesis was first published – a notable amount for a mega cap stock and nearly 2-3X more returns than any FAAMG in the same period. This is important because I expect this to trend to continue until Nvidia has surpassed all FAAMG valuations.
Below, we discuss the Ampere architecture and A100 GPUs, the Enterprise AI Suite and an update on the Arm acquisition. These are some of the near-term stepping stones that will help sustain Nvidia’s price in the coming year. We are also bullish on the Metaverse with Nvidia specifically but will leave that for a separate analysis in the coming month.
Nvidia Not Standing Still with Ampere Architecture and A100 GPU
“Nvidia’s acceleration may happen one or two years earlier as they are the core piece in the stack that is required for the computing power for the front-runners referenced in the graph above. There is a chance Nvidia reflects data center growth as soon as 2020-2021.” -published August 2019, Premium I/O Fund
Last year, Nvidia released the Ampere architecture and A100 GPU as an upgrade from the Volta architecture. The A100 GPUs are able to unify training and inference on a single chip, whereas in the past Nvidia’s GPUs were mainly used for training. This allows Nvidia a competitive advantage by offering both training and inferencing. The result is a 20x performance boost from a multi-instance GPU that allows many GPUs to look like one GPU. The A100 offers the largest leap in performance to date over the past 8 generations.
At the onset, the A100 was deployed by the world’s leading cloud service providers and system builders, including Alibaba cloud, Amazon Web Services, Baidu Cloud, Dell Technologies, Google Cloud platform, HPE and Microsoft Azure, among others. It is also getting adopted by several supercomputing centers, including the National Energy Research Scientific Computing Center and the Jülich Supercomputing Centre in Germany and Argonne National Laboratory.
One year later and the Ampere architecture is becoming one of the best-selling GPU architectures in the company’s history. This quarter, Microsoft Azure recently announced the availability of Azure ND A100 v4 Cloud GPU which is powered by NVIDIA A100 Tensor Core GPUs. The company claims it to be the fastest public cloud supercomputer. The news follows the launch by Amazon Web Services and Google Cloud general availability in prior quarters. The company has been extending its leadership in supercomputing. The latest top 500 list shows that Nvidia power 342 of the world’s top 500 supercomputers, including 70 percent of all new systems and eight of the top 10. This is a remarkable update from the company.
Ampere architecture-powered laptop demand has also been solid as OEM’s adopted Ampere Architecture GPUs in a record number of designs. It also features the third-generation Max-Q power optimization technology enabling ultrathin designs. The Ampere architecture product cycle for gaming has also been robust, driven by RTX’s real-time ray tracing.
In the area of GPU acceleration, Nvidia is working with Apache Spark to release Spark 3.0 run on Databricks. Apache Spark is the industry’s largest open source data analytics platform. The results are a 7x performance improvement and 90 percent cost savings in an initial test. Databricks and Google Cloud Dataproc are the first to offer Spark with GPU acceleration, which also opens up Nvidia for data analytics.
The demand has been strong for the company’s products which have exceeded supply. In the earnings call, Jensen Huang mentioned “And so I would expect that we will see a supply-constrained environment for the vast majority of next year is my guess at the moment.” However, he assured that they have secured enough supplies to meet the growth plans for the second half of this year when he said, “We expect to be able to achieve our Company’s growth plans for next year.”
Virtual Machines for AI Workloads
Virtualization allows companies to use software to expand the capabilities of physical servers onto a virtual system. VMWare is popular with IT departments as the platform allows companies to run many virtual machines on one server and networks can be virtualized…
Read More:Here’s Why Nvidia Will Surpass Apple’s Valuation In 5 Years