NVIDIA’s (NVDA) stellar quarter and strong guidance raises could drive the AI chip powerhouse’s share price up another 14% from its all-time high over the next six to nine months. This is generally the period of the club stock price target, and NVIDIA has increased it from $300 to $450 per share. We have affirmed our rating on the stock at ‘2’, indicating we want to wait for a rebound before buying more. You’re not kidding, are you? Nvidia closed Wednesday at $305 a share. Shares then surged nearly 29% to an intraday high of $394.80 on Thursday following a surprise earnings release. The market capitalization is nearing $1 trillion. Jim Cramer, who has backed Nvidia since at least 2017, recently named it the club’s second no-trading stock. (Apple was the first). Jim renamed his pet dog Nvidia. Nvidia’s new $450 per share target price is about 45 times its full-year fiscal 2025 (or calendar 2024) earnings forecast. Nvidia’s financial calendar is bizarre, with earnings reported for the first quarter of 2024 on Wednesday night. 45x is just over double the S&P 500’s current valuation, which isn’t cheap on a valuation basis, but just above the 40x average valuation investors have placed on the stock over the past five years. is. In our view, this is more than justified considering the runway to growth Nvidia has in front of them. This latest revision to forecasts suggests that NVIDIA is proving to be cheaper (or more valuable) than originally thought, as analysts have been consistently overly conservative about NVIDIA’s potential. It’s also a reminder that there are many, and that’s what we’re seeing on Thursday. The destructive nature is now in full play as the undisputed leader of the cards for implementing artificial technology. NVDA 5Y Mountain Nvidia’s 5-Year Achievement Jim has continued to admire Jensen Huang, his CEO of NVIDIA, over the years. Not to mention, it also covers much of the graphics processing unit (GPU) technology already in place that has allowed the company to capitalize on his AI explosion. When ChatGPT went viral this year, it penetrated consumer consciousness. On Wednesday night’s post-earnings conference call, management said it expects things to get even better in the second half of the year. While no formal guidance has been released for beyond the current quarter, the team said that demand for generative AI and large language models has “extended our data center visibility for several quarters, and We were able to source a significantly increased supply.” . Simply put, management seems to be suggesting that earnings in the second half of the year could be even greater than in the first. The demand they are talking about is wide-ranging and spans consumer internet companies, cloud service providers, enterprise customers and even AI-based start-ups. Keep in mind that Nvidia’s first-ever central processing unit (CPU) for data centers will be available later this year. “At the International Supercomputing Conference in Germany this week, the University of Bristol unveiled his new Nvidia-based supercomputer,” said executives. A Grace CPU superchip that is six times more energy efficient than his previous supercomputer. ” Energy efficiency is a big selling point. As we saw in 2022, energy is a significant input cost in running a data center, so anything you can do to reduce it will be very attractive to customers looking to improve profitability. prize. Omniverse Cloud will also be available in the second half of this year. At a higher level, management spoke on a conference call about the need for the world’s data centers to undergo significant upgrade cycles to handle the computing demands of generative AI applications such as OpenAI’s ChatGPT. (Microsoft, the club’s name, is a major backer of his Open-AI, using the startup’s technology to power its new AI-enhanced Bing search engine.) We are moving towards rated computing,” Huang said. he said Wednesday evening. This is he’s a trillion dollar data center he infrastructure is almost entirely based on his CPU and as Huang pointed out this is “fundamentally unaccelerated” It means it needs to be refurbished. But with generative AI clearly becoming the new normal and accelerated GPU-based computing being far more energy efficient than non-accelerated CPU-based computing, data center Budgets “need to shift very dramatically towards accelerated computing,” he said. looking at it now. As we said in our guide on how the semiconductor industry works, the CPU is basically the brain of the computer, taking instructions/inputs, decoding those instructions, and processing them to perform operations that yield the desired result. is responsible for sending . GPUs, on the other hand, are more specialized and are good at handling many tasks at once. Whereas CPUs process data sequentially, GPUs divide complex problems into many small tasks and execute them all at once. Huang further said that basically as we move forward, capex budgets from data center customers will focus on generative AI and accelerated computing infrastructure. So over the next five to 10 years, the increasingly valuable data center budget, which is now around $1 trillion, will be in the cloud Nvidia as his provider looks to Nvidia as an accelerated computing solution. will shift in favor of It’s really that simple after all. All roads lead him to Nvidia. Notable companies such as Amazon Web Services (AWS), Microsoft’s Azure, and Google Cloud have all moved their workloads to the cloud, and all of his cloud providers rely on his Nvidia to support their products. doing. Why NVIDIA? Huang pointed out on the conference call that core to Nvidia’s value proposition is the lowest total cost of ownership solution. Nvidia excels in several areas that allow it. These are full-stack data center solutions. It’s not just about having the best chips, but also about engineering and optimizing software solutions that allow users to get the most out of their hardware. In fact, on the conference call, Huang mentioned his networking stack called DOCA and his acceleration library called Magnum IO, commenting that “these two pieces of software are some of our crown jewels.” . He added, “Nobody talks about it because it’s hard to understand, but it allows you to connect tens of thousands of GPUs.” Nvidia excels at making the most of their entire data center architecture, not just a single chip. That is, the way all parts are built from the ground up to work together. In Huang’s words, “A computer is a data center, or a data center is a computer is another way of thinking. It’s not a chip. It’s a data center, and it’s never happened before.” It never happened, and in this particular environment, the networking operating system, the distributed computing engine, the networking gear, the switches, the architecture of the computing system, the computing fabric, that whole system is the computer that it’s trying to operate. You need to understand the full stack and data center scale to get the best performance, and that’s accelerated computing.” Leveraging is another key component of Nvidia’s competitive edge. . As Huang pointed out, a data center that can only do one thing, even if it can do it incredibly fast, will be underutilized. But Nvidia’s “Universal GPU” can do a lot (again, back to the large software library) and thus achieve much higher utilization. Finally, there is the company’s data center expertise. During the conference call, Huang discussed issues that can arise when building a data center, noting that construction can take up to a year for some people. Nvidia, on the other hand, has perfected this process. He said that Nvidia can measure delivery times in weeks instead of months or he years. This is a huge selling point for customers who want to stay on the cutting edge of technology, especially as we enter this new era of AI, which is gaining significant market share. Conclusion As we look to the future, ChatGPT was an eye-opening moment, or, as Huang put it, the “iPhone moment,” but it’s important to remember that we’re just the beginning. is. The excitement about ChatGPT is less about what it can already do than more of a proof of concept of what it can do. Launched 16 years ago next month, the original iPhone was nowhere near what it is today. But it showed people what smartphones really are. Extending this metaphor, what we have today is the original original iPhone. If you’re going to own Nvidia instead of trading Nvidia as we’re planning, just like generative AI applications are already good, than about what we have now We have to think more about what this technology will be able to do when we reach that age. “iPhone 14 version” of generation AI. That’s why it’s really interesting (and a little scary) to keep holding shares in this AI-enabling giant. (The Jim Cramer Charitable Trust is long NVDA, MSFT, AMZN, AAPL, GOOGL. See here for a full list of stocks.) As a subscriber to Jim Cramer’s CNBC Investment Club, Jim trades Receive trade alerts before you do. trade. Jim waits 45 minutes after sending a trade alert before buying or selling shares in the charitable trust’s portfolio. If Jim talks about a stock on his CNBC television, he will wait 72 hours after issuing a trade alert before executing the trade. The Investment Club information above is subject to our Terms of Use and Privacy Policy, along with our Disclaimer. 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Nvidia CEO Jensen Huang in his usual leather jacket.
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Continue Nvidia(NVDA)’s astonishing quarter and strong guidance hike could see the AI chip powerhouse’s share price rise another 14% from its all-time high over the next six to nine months.
