Nvidia has added another leg to its investment case, planted far away from the data center. It’s on your desk at the office and at home. At the influential Computex conference in Taiwan, CEO Jensen Huang focused the first half of his keynote address on the data center and the wonders of Nvidia’s Vera computing platform for agentic AI workloads. It was familiar territory — dominating the market for data center AI chips is what’s vaulted the company into its place as the world’s most valuable company. Then, Huang pivoted and introduced an all-new product line for Nvidia, putting it head-to-head with Intel and AMD in the Windows personal computer market. Huang unveiled a suite of new laptops, desktops, and heavy-duty workstations running on a new integrated chip called the RTX Spark . Shares of Nvidia jumped over 4% Monday, while Intel and AMD slipped. Co-designed with Taiwan’s MediaTek, the RTX Spark is considered a system-on-chip (SoC) — essentially, this means a bunch of computing functions are integrated onto a single piece of silicon, instead of having separate chips strung together. In the case of the RTX Spark, Nvidia has designed the central processing unit (CPU), graphics processing unit (GPU), and neural processing unit (NPU) needed for intense on-device AI computing — all integrated into a single SoC package. As a result, Nvidia for the first time will fully dictate the performance, power requirements, and AI capabilities of these PCs. The GPU is based on its Blackwell family architecture. The CPU is based on the power-efficient Arm instruction set, in another win for Club name Arm . To be sure, it’s not a fully integrated computer like what you get from Apple, which is designing every aspect of the device — from the physical appearance to the operating system software and even the silicon. However, it is far more vertically integrated than anything we’ve seen from Nvidia in the consumer PC space to date. Nvidia has historically only made graphics cards for PCs, and they’ve been incredibly popular with PC gamers. It was Nvidia’s original dominant market, before moving into the data center. Official release dates haven’t been announced, but expect to start seeing these Nvidia-based PCs on store shelves in the fall, right in time for the holiday shopping season. The initial laptops and desktop PC models will be made by ASUS, Dell, HP, Lenovo, fellow Club name Microsoft and MSI. NVDA 1Y mountain Nvidia’s stock performance over the past 12 months. When buying a PC, you tend to have three options for the graphics card, or GPU: dedicated, integrated, or SoC. The SoC configuration is the most modern configuration and has become increasingly popular in recent years. Clearly, Nvidia is signaling it expects that popularity will only increase. Here’s why: Integrated : The graphics card is integrated onto the same silicon foundation, known as a substrate, as the CPU, sharing power and memory (RAM). Slow memory, due either to a physical lack of RAM or because the CPU is using it, results in slower performance (latency). The RAM is also optimized for the CPU, not the GPU. Dedicated : The graphics card is separate from the CPU and has its own dedicated memory (video RAM or VRAM) and power source. The memory is designed specifically for and dedicated entirely to graphics data, resulting in lower latency. However, the issue with dedicated memory is that the CPU and GPU can’t communicate efficiently. The GPU has all the VRAM it could ask for, but until the information from the CPU’s RAM is copied over to the GPU’s VRAM, it won’t matter much. That copy/paste process represents a bottleneck. System-on-Chip : All components sit on the same “package,” sharing a unified memory that the CPU and GPU can access at the same time. It’s sort of like the integrated option, except that it’s extremely high-bandwidth so that the CPU can treat the unified memory component like RAM, while the GPU treats it like VRAM, and both draw on it at the same time without one slowing down the other. Moreover, because the memory is shared, we get rid of the copy/paste bottleneck that came with the dedicated option. The takeaway is that, for an AI-oriented PC, the SoC design is optimal. However, that offering for Windows PCs has only been available from Intel, AMD and, to a lesser extent, Qualcomm — until now. If you wanted a Windows-based PC with an Nvidia GPU, you had to get it as a dedicated option with either an AMD or Intel CPU. Moreover, AMD and Intel are based on the x86 architecture, which is basically the language that software and CPUs use to communicate. If you wanted an Arm-based PC, you were either looking at a Mac (which means you’re giving up Windows and committing to a completely different ecosystem), or a Qualcomm Snapdragon-based PC. The issue with the Snapdragon option is that it has some compatibility issues due to being built on an Arm-based architecture in a world in which Windows was designed for x86. Nvidia, however, is claiming to have completely addressed the compatibility issue, making this the first Arm-based Windows PC that users can buy without worrying about compatibility issues with the programs they rely on. According to Huang, Nvidia’s Arm-based PCs can run not only 100% of Windows applications, it can also run everything Nvidia has ever done. Running Windows is key for adoption, but the ability to run the entire Nvidia stack locally? That is something to get excited about. Think about it: If you’re an AI engineer that needs to develop and test large language models or new agents, but wants to work on the Windows operating system, you can now do so locally. That saves tons of money in cloud compute rental costs while you build the application, as you can run and test it locally before deploying into the cloud. This gets to another big theme. We are now firmly in the era of “edge computing.” Cloud computing is about sending data over the internet to massive data centers for processing. Edge computing is about doing that compute “at the edge,” meaning locally, where the user is. By bringing computers capable of running everything Nvidia has ever created to the PC form factor, Nvidia is essentially putting a supercomputer in the home that can act as the brains for all other smart devices. While the most intense tasks may still need to be sent to the cloud for data center-level computations, most will no doubt be able to run locally, reducing latency and cost while increasing reliability, runtime, and security. Nvidia’s decision to aggressively compete for market share in the consumer PC market, where Intel, AMD, and Apple, have reigned supreme, reinforces our belief that this a stock to own for the long term. Nvidia’s data center business will likely remain its largest opportunity, considering the projections for trillions of dollars in annual AI infrastructure spending. But the PC market isn’t niche, with industry firm IDC projecting sales of $274 billion in 2026. As Nvidia comes onto Intel and AMD’s home turf, while maintaining its momentum in the AI data center market, we struggle to see how the price-to-earnings multiples of these three stocks don’t converge over time. Intel is trading at 91 times earnings estimates for the next 12 months, AMD is at 52 times, and Nvidia is only at 21 times. Of course, these three chipmakers aren’t carbon copies of each other — for example, Intel has a fledgling third-party manufacturing business that’s excited investors. But with them now competing more head-to-head than ever before, we simply do not think the current valuations make sense. That’s even more true considering that neither of the other two brings to the table the software moat, nor the level of vertically integrated hardware stack, that Nvidia does. We’re not saying explicitly that Intel and AMD are overvalued; we’ll let others make that call. What we’re saying is that Nvidia has been and remains extraordinarily undervalued when compared with those two peers and the overall S & P 500, which is often viewed as a proxy for your “average stock.” Nvidia and the S & P 500 currently sport roughly the same multiple. With its sights set on a new market, Nvidia is clearly not an average company, nor is it an average stock. (Jim Cramer’s Charitable Trust is long NVDA, ARM, AAPL. and MSFT See here for a full list of the stocks.) As a subscriber to the CNBC Investing Club with Jim Cramer, you will receive a trade alert before Jim makes a trade. Jim waits 45 minutes after sending a trade alert before buying or selling a stock in his charitable trust’s portfolio. If Jim has talked about a stock on CNBC TV, he waits 72 hours after issuing the trade alert before executing the trade. THE ABOVE INVESTING CLUB INFORMATION IS SUBJECT TO OUR TERMS AND CONDITIONS AND PRIVACY POLICY , TOGETHER WITH OUR DISCLAIMER . NO FIDUCIARY OBLIGATION OR DUTY EXISTS, OR IS CREATED, BY VIRTUE OF YOUR RECEIPT OF ANY INFORMATION PROVIDED IN CONNECTION WITH THE INVESTING CLUB. NO SPECIFIC OUTCOME OR PROFIT IS GUARANTEED.
