Nvidia Is Scheduled to Report Fourth-Quarter Financial Results on Wednsday after the Bell.
It’s expected to put the finishing touches on one of the most remarkable years from a large company ever. Analysts Pollled by Factsset Expect $ 38 billion in sales for the Quarter Ended in January, which would be a 72% increase on an annual basis.
The January Quarter will cap off the second fiscal year where Nvidia’s sales more than doubled. It’s a Breathtaking Streak Driven by the Fact that Nvidia’s Data Center Graphics Processing Units, OR GPUS, Are Essential Hardware for Building and Deploying Artificial Intelligence Services Like Oopain Chatgpt. In the past two years, Nvidia stock has risen 478%, Making it the most valuable us company at times with a market Cap Over $ 3 Trillion.
But nvidia’s stock has slowed in recent months as investors question where the chip company can go from here.
It’s trading at the same price as it did last October, and investors are wary of any signs that Nvidia’s most important customers might be tightening Expenditures. This is particularly concerning in the wake of recent breakthroughs in ai out of China.
Much of Nvidia’s Sales Go to a Handful of Companies Building Massive Server Farms, Usually to Rent Out to Other Companies. The Cloud Companies are typical called “Hyperscalers.” Last February, Nvidia said a single customer account for 19% of its total revenue in fiscal 2024.
Morgan Stanley Analysts Estimated This Month That Microsoft will account for near 35% of spending in 2025 on blackwell, Nvidia’s latest ai chip. Google is at 32.2%, Oracle at 7.4% and Amazon at 6.2%.
This is why any sign that microsoft or its rivals might Pull Back Spending Plans Can Shake Nvidia Stock.
Last Week, TD Cowen Analysts Said That They’D Learned that Microsoft Had Canceled Leases with Private Data Center Operators, Slowed Its Process of Negotiating to enter into new leases and adjusted plans Spend on International Data Centers in Favor of Us Facility.
The report raised fears about the sustainability of ai infrastructure growth. That count means demand for nvidia’s chips. TD Cowen’s Michael Elias said his team’s finding points to “a potential oversupply position” for microsoft. Shares of Nvidia Fell 4% on Friday.
Microsoft Pushed Back Monday, Saying It Still Planned to Spend $ 80 Billion on Infrastructure in 2025.
“While we may be strategically pace or adjust our infrastructure in some area, we will continue to grow strongly in all regions. spokesperson Told CNBC.
Over the last monthMost of Nvidia’s Key Customers Touted Large Investments. Alphabet is targeting $ 75 billion In Capital Expenditures This year, Meta will spend as much as $ 65 billion And amazon is aiming to spec $ 100 billion,
Analysts say about half of ai infrastructure capital expecteds ends up with nvidia. Many Hyperscalers Dabble in AMD’s GPUS and Are Developing his own ai chips to lessen their dependence on Nvidia, but the company holds the majority of the market for the market for cutting-edge ai chips.
So far, these chips have been used primarily to train new age ai models, a process that can cost Hindreds of Millions Dollars. After the AI is developed by companies like Openai, Google and Anthropic, Warehouses full of Nvidia gpus are required to serve that there models to customers. That’s why nvidia projects its revionue to continue growing.
Another Challenge for Nvidia is last month’s emergence of chinese startup deepsek, which releases an efficient and “distilled“Ai model. It has had high enough performance that suggested billions of dollars of nvidia gpus arenys to train and use cutting-edge ai. That tamporarily Sunk Nvidia ‘Stock, Causing The Company to Causing The Company to Lose almost $ 600 billion in Market Cap.
Nvidia Ceo Jensen Huang will have an opportunity on Wednsday to explain why ai will continue to need even more gpu capacity even Eveen after last year’s massive buy-out.
Recently, Huang has spoken about the “scaling law,” an observation From Openai in 2020 that AI Models Get Better The More Data and Compute Are Used When Creating Them.
Huang said that Deepsek’s R1 Model Points to a New Wrinkle in the Scaling Law that Nvidia Calls “Test Time Scaling. “Huang has contended that the next major path to ai improvement is by applying more gpus to the process of deplying ai, or infererance. Through a problem.
AI models are trained only a few times to create and fin-tune them. But ai models can be called millions of time per month, so using more Compute at Infererance will require more nvidia chips deployed to customers.
“The Market Responded to R1 as in, ‘Oh My Gosh, AI is Finished,’ That AI Doesn’T Need to Do Any More Computing Anymore,” Huang said in a prettered Interview Last Week“It’s exactly the opposite.”
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