Wall Street’s AI conversation has become fixated on Nvidia Corp‘s (NASDAQ:NVDA) newest chips. CoreWeave, Inc. (NASDAQ:CRWV), however, used its second quarter earnings call to make a different point: older GPUs may have far more earning power than investors think.
The cloud infrastructure provider revealed it recently signed a customer contract for Nvidia’s A100 GPUs that extends through 2029—nearly a decade after the chip debuted.
More importantly, management suggested this isn’t an exception but evidence that AI infrastructure can continue generating attractive returns long after its first deployment.
CoreWeave Sees Long-Term Value in Older Nvidia GPUsThe clearest indication came from CFO Nitin Navin, who highlighted the longevity of one of the company’s latest deals. “We recently signed an A100 contract that extends into 2029 at an attractive price. As a reminder, this SKU was introduced in 2020,” Navin said.
CEO Michael Intrator expanded on that point, arguing the contract offers a glimpse into how AI infrastructure could be monetized over a much longer period than many investors currently assume.
“The fact that we have been able to go ahead and sell a GPU whose architecture was from 2020 in a contract that was fully priced out to 2029 really provides some insight into what the future is going to look like,” Intrator said.
Those comments challenge a common assumption in the AI infrastructure market—that each new generation of Nvidia chips quickly renders older hardware economically obsolete.
Instead, CoreWeave is signaling that mature GPUs can continue attracting customers if the workload and pricing remain attractive.
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CoreWeave’s GPU Strategy Goes Beyond the First ContractThe company also hinted that older hardware could generate revenue more than once.
Navin said every time an existing GPU is renewed or redeployed after its initial contract, the revenue comes on top of returns already earned during the original lease. “Every resale or renewal is incremental on top of the returns already earned within the initial term,” he said.
Intrator added that managed inference—a business the company expects to surpass $250 million in annual recurring revenue by the end of 2026—offers another avenue for putting GPUs coming off contract back to work rather than leaving them idle.
For investors, the takeaway extends beyond a single A100 contract. If CoreWeave can consistently renew, redeploy or repurpose older GPUs into new customer agreements and inference workloads, the economic life of its infrastructure could prove much longer than many expect.
That would allow the company to generate additional returns from assets already on its balance sheet. A dynamic that could become increasingly important as the AI market moves beyond the race for the latest chips and toward maximizing the value of existing GPU fleets.
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Nvidia Corp. (NASDAQ:NVDA) shares moved higher Wednesday as investors focused on continued AI infrastructure spending, broader semiconductor momentum and the company’s upcoming earnings report.
The company’s stock rose almost 3% as investors looked ahead to the company’s quarterly earnings report on Aug. 26, 2026. The move came as S&P 500 futures gained 0.3%, supporting risk appetite for mega-cap technology stocks.
AI Infrastructure Demand Supports Chip StocksIntel Corp. (NASDAQ:INTC) kept investor attention on AI infrastructure after increasing its equity offering to $20 billion and expecting about $19.7 billion in net proceeds.
Intel said it plans to use the funds for general corporate purposes, including working capital and capital expenditures, and expects 2026 capital spending to exceed $20 billion.
CoreWeave Inc. (NASDAQ:CRWV) and Super Micro Computer Inc. (NASDAQ:SMCI) also reported strong backlogs and continued data-center spending, reinforcing demand for AI infrastructure.
Heavy AI hardware investment by major cloud and technology companies continues to support demand for Nvidia’s chips.
Earnings & Analyst OutlookThe countdown is on: Nvidia Corp is set to report earnings on August 26, 2026 (confirmed).
EPS Estimate: $2.07 (Up from $1.04 YoY) Revenue Estimate: $91.82 Billion (Up from $46.74 Billion YoY) Valuation: P/E of 33.3x (Indicates premium valuation relative to peers) Analyst Consensus & Recent Actions: The stock carries a Buy rating with an average price forecast of $325.74. Recent analyst moves include:
Wells Fargo: Overweight (Maintains Forecast to $315.00) (Aug. 11) Keybanc: Overweight (Raises Forecast to $330.00) (July 14) China Renaissance: Initiated with Buy (Forecast $319.00) (June 5) Top ETF Exposure Franklin Focused Dynamic Growth ETF (NASDAQ:FFOG): 9.99% Weight Xtrackers Net Zero Pathway Paris Aligned US Equity ETF (NYSE:USNZ): 9.73% Weight iShares Nasdaq Top 30 Stocks ETF (NASDAQ:QTOP): 9.96% Weight Significance: Because NVDA carries such a heavy weight in these funds, any significant inflows or outflows for these ETFs will likely force automatic buying or selling of the stock.
Price ActionNVDA Stock Price Activity: Nvidia shares were trading higher by 2.66% at $223.21 at the time of publication on Wednesday, according to Benzinga Pro data.
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CoreWeave (CRWV), an AI cloud provider that rents out Nvidia (NVDA) chips, rose 15.64% premarket after reporting second-quarter revenue of $2.58 billion, up 112
Last month, American tech giants came together to urge policymakers not to place "premature restrictions" on open-weight AI models, even if they're from China. Now, two of those companies are making a concerted effort to compete by introducing their own open offerings.
Meta and Nvidia both released artificial intelligence models this week that are available for developers to download for free via the open-source ecosystem, a contrast to the popular proprietary models from OpenAI and Anthropic.
Open-source AI has become a contentious topic from Silicon Valley to Washington, D.C., with critics raising concerns about the potential national security risks of Chinese models, and of the AI training practice called distillation, which can be viewed as a form of intellectual property theft. Meanwhile, most of the industry's leading players contend that restricting use of the models would be to our own detriment and would place too much power in the hands of too few companies.
"The age of AI can be one of prosperity," the consortium of tech companies wrote in an open letter on July 24. "With the right choices, open weight AI can expand opportunity, strengthen competition, extend American technological leadership, mitigate risk, and ensure that the benefits of this extraordinary technology are shared broadly across our economy."
As for distillation, they call it "a widely used technique for model improvement, evaluation, and validation."
watch now
Meta on Monday released Muse Glimmer as part of a strategy to release its most powerful AI models to the open-source community. CEO Mark Zuckerberg said the company would open the weights for its latest AI model, Muse Spark 1.2. Weights refer to the calculations and rules that determine how the AI works and behaves.
A day later, Nvidia debuted Nemotron 3.5 Lightning. The model stems from the company's Nemotron 3 family of models released in December. The chipmaker said its models are "truly open source," because the company publishes the related "training datasets, techniques, and model weights" for developers to inspect.
Both companies still have to prove there's an audience for their offerings in a market featuring popular models from Chinese AI labs like Moonshot AI and DeepSeek, as well as Alibaba's Qwen.
Box CEO Aaron Levie, one of the signatories of last month's letter, is optimistic. He said Zuckerberg's plan for Muse Spark 1.2 is a "very big deal" because it's a powerful model that rivals top foundation models from Anthropic and OpenAI. The models this week from Meta and Nvidia are smaller and intended to run on laptops for tasks like powering on-device digital agents.
"There's a very firm flag in the ground that America will have near-frontier open-source models," Levie said.
Meta has tried this route in the past with Llama. That was Zuckerberg's initial entry into the foundation AI market, but the release of Llama 4 in April 2025 left developers unimpressed. Meta followed by spending billions of dollars to overhaul its AI unit, installing Scale AI CEO Alexandr Wang as the division's leader.
Recently, Wang's group has been rolling out proprietary models under the Muse branding to try and develop new revenue streams.
'Tremendous amount of potential'Levie said that companies put off by using Chinese open-weight AI models would be more inclined to experiment with Meta's upcoming variant.
"You probably wouldn't be able to put a non-domestic open-source model in a major government agency, as an example, and you wouldn't be able to use it at very large banks most likely," Levie said. "If you think about the kind of use cases that now Muse can be used in, it actually opens up a tremendous amount of potential."
Still, Meta in particular faces some headwinds as it pursues yet another open-source strategy. Umesh Sachdev, CEO of business AI startup Uniphore, said Meta burned bridges with third-party developers when it shifted from open weight to proprietary AI models.
"I think it's going to take more than a 3,500 worded article from Zuck to convince developers," Sachdev said regarding Zuckerberg's accompanying manifesto this week. "The emotion of my developers at Uniphore, they almost feel betrayed."
But Sachdev said he's rooting for domestic companies to succeed, "because more competition will drive down token cost, and will drive up innovation, and it's always good for consumers."
It's a sentiment shared by Forrester analyst Charlie Dai. He called Meta's latest move "strategically important because it restores a major U.S. frontier AI vendor to the open ecosystem."
"Developers and enterprises will likely welcome Meta's shift back toward open weights because it improves transparency, customization, deployment flexibility, and data sovereignty," Dai said. Now, the company must "prove it can cultivate a durable ecosystem beyond releasing competitive models," he said.
WATCH: Meta's new AI model is a "positive development for the ecosystem."
Blackwell has been Nvidia's most advanced graphics processing unit (GPU), but Vera Rubin has begun deployment. The GPU market is already massive and only expected to grow.
Analyst’s Disclosure: I/we have a beneficial long position in the shares of NVDA either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
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AI is the most important tech advance in human history, and perhaps the greatest investment of all time. One only has to talk to the hundreds of millions of people who use it (or perhaps billions) and the army of companies around the world that say it will transform the way their employees work and pave a golden road to profits. It has also made untold numbers of people richer via ownership of its stock and the constellation of AI-related companies that surround it. Many of these have been helped by investments from Nvidia (NASDAQ: NVDA | NVDA Price Prediction), or loans that will make them grow faster. If AI is the world’s most important technology development, Nvidia sits in the middle of its future.
After dozens of deals, it has financial relationships and ownership in Anthropic, OpenAI, Intel (NASDAQ: INTC), Coreweave, Nokia, Synopsys (NASDAQ: SNPS), Marvell (NASDAQ: MRVL) and at least half a dozen others.
AI data centers are the heartbeat of AI’s future. Nvidia has just pulled off a coup as it set up a partnership with some of the world’s largest financial companies to set up a $500 billion facility to build out more of this infrastructure. The group includes Apollo, BlackRock, Blackstone, Goldman Sachs (NYSE: GS) and several smaller companies. Goldman Sachs is the world’s premier investment bank. BlackRock is the world’s largest money manager. And Nvidia will backstop some of the deal, just in case other investors fall short. As Bloomberg reports, “Executives indicated that it will focus on debt financing to provide access to computers for Nvidia’s largest customers and that there are already many deals in the works that would qualify toward this commitment.” This means that much of this money will return to Nvidia as revenue, which is central to many of its other investments. “Round-tripping” of revenue or not, it helps to guarantee Nvidia’s 60%-plus top-line quarterly growth.
Nvidia has a path toward being the world’s largest company by revenue. In its most recent quarter, it had revenue of $81 billion, which grew 85% year over year. That means Nvidia’s revenue run rate is close to $330 billion. And, it said in the current quarter, revenue will be $91 billion. In the upcoming fiscal year, at its current growth rate, its revenue could be $630 billion. Amazon topped the Fortune 500 based on revenue at $714 billion, but its growth rate is 19%. Give Nvidia two or three years, and it could sit atop the Fortune list.
The business world has never seen a company like Nvidia. One could argue that Microsoft was more important in the late 20th century and the beginning of this one. The operating system sector has faded. Google’s search business was a huge step forward, but Nvidia has largely replaced it. Apple is built on one product.
Nvidia’s market cap is $5.4 trillion. That is based on its role as the arms merchant to the AI industry. As it provides money for core assets like data centers, that role grows. It grows more and it becomes the AI sector’s banker.
Despite a bit of sideways movement, recently Nvidia’s stock is up 977% in the last five years. To reach a market cap of $10 trillion, it does not have to come close to that pace.
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Nvidia CEO Jensen Huang. Bloomberg/Getty Images Majestic Labs cofounder Sha Rabii knows what it's like to build AI infrastructure in an industry dominated by Nvidia.
Rabii spent years leading custom silicon inside Google and Meta before launching his own AI hardware startup.
When Rabii considers Nvidia's biggest competitive advantage, he doesn't point to its brilliant engineers, leading technology, or well-established sales channels.
"Where they're kind of unmatched is in the giant bag of cash they're sitting on top of," Rabii said.
Nvidia had more than $80 billion in cash and marketable investments as of April, when it last reported earnings, and generated another $50 billion in operating cash flow that quarter. While other Big Tech giants have comparable stashes, Nvidia doesn't shoulder the same massive data-center construction costs.
Even after returning billions to shareholders, it leaves it with an enviable problem: how to put its riches to work.
"What better use of the rest of the cash to invest and grow and strengthen the ecosystem around their products?" said Bernstein analyst Stacy Rasgon.
On Monday, Nvidia unveiled a new way to turbo-charge the power of its cash hoard by bringing in at least $500 billion of outside capital to finance far more Nvidia-powered AI infrastructure than what it's already funding. This new initiative includes some of the biggest firms on Wall Street, including Apollo, Blackstone, and Goldman Sachs.
The company is also in talks to guarantee an OpenAI data center project worth hundreds of billions of dollars. It's providing financial backstops to neoclouds, or specialized AI cloud providers, to buy its graphics processing units (GPUs) — the chips used to run AI.
It's also making long-term commitments to secure scarce components, and investing in and supporting AI startups through its NVentures and Inception programs. If one bet takes off, Nvidia hits the lottery on both the investment and future chip sales.
The result is a self-reinforcing cycle: Nvidia's technological dominance generates cash, and that cash is increasingly becoming a moat of its own.
Nvidia's scale has boosted its financing playbookVendors using their balance sheets to help customers buy their products isn't a novel strategy, said Bernie Margulies, who works on GPU financing at American Compute. The practice is spreading more broadly.
AMD has previously backstopped chips for customers, while Broadcom recently partnered with Apollo and Blackstone on an AI-infrastructure financing platform. Neither rival can match Nvidia's scale, said D.A. Davidson analyst Gil Luria, calling the chipmaker's financial might a different "order of magnitude."
In addition, Meta agreed to purchase up to $15 billion of computing capacity from the neocloud Nebius, while Google has backed Anthropic's data-center projects.
Historically, Nvidia hasn't needed to do this, Margulies said.
As the AI buildout explodes, Nvidia helps finance projects that might not otherwise get built. Margulies said this could also be read as an effort to keep customers from experimenting with alternatives, like AI chips built by AMD or Google.
Alex Yeh, CEO of the neocloud GMI Cloud — which has a backstop agreement with Nvidia — said the deals help the chip giant build an "alliance" with neoclouds and AI startups as its biggest cloud customers develop their own chips.
They can also unlock massive orders for AI chips.
Yeh said that without Nvidia stepping in, he wouldn't have been able to take on the AI startup Fireworks as a customer, with which GMI has a nine-figure deal.
Likewise, Rabii said financing is a complication for one of Majestic's target markets: neoclouds. If a customer wanted to buy billions of dollars of Majestic hardware, his startup couldn't backstop the financing the way Nvidia can.
"Nvidia's real moat today is how freaking expensive it is to build data centers," Rabii said. Instead, he said he's betting Majestic's power-efficient chips will convince customers to choose its systems.
Nvidia is looking beyond its own balance sheetNvidia's growing financial ties have fueled concerns about circular financing.
Margulies said he isn't particularly concerned, though the strategy "does cause a lot of investors to be worried." If Nvidia loses market share and has to pay out on its backstops, "it'll be brutal," he said.
Michael Reid, CEO of Australian neocloud Megaport, which does not have a backstop agreement with Nvidia, said overall demand for GPUs provides another safeguard. If one customer falters, the neocloud could rent the capacity to another.
Nvidia CEO Jensen Huang said on X that the company's latest Wall Street partnerships are designed to address concerns about circular financing. Analysts said that tapping outside capital means Nvidia doesn't have to shoulder as much of the financial risk.
"They're sharing the reward, but they're also sharing the risk," said D.A. Davidson's Luria.
In this vein, the chipmaker's next financial moat may be access to money that isn't even its own.
Have a tip? Contact this reporter via email at [email protected] or Signal at @geoffweiss.25. Use a personal email address, a nonwork WiFi network, and a nonwork device; here's our guide to sharing information securely.
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Geoff Weiss You're currently following this author! Want to unfollow? Unsubscribe via the link in your email.
Geoff Weiss is a senior reporter on Business Insider’s tech team, where he writes about AI startups and Y Combinator, the intersection of AI and the media industry, and workplace dynamics within top AI labs and chip companies.Previously, Geoff was on the media desk, covering YouTube and Netflix, and themes like the intersection of Hollywood and the creator economy. His work on Netflix’s video podcasting ambitions and Mr Beast’s lessons for Hollywood won second and first prize, respectively, at the 2025 LA Press Club Awards.Prior to joining Business Insider, Geoff was the senior editor of Tubefilter and a staff writer at Entrepreneur. He graduated from New York University with a degree in English Literature.He can be reached at [email protected], on Signal @geoffweiss.25, and on LinkedIn. Have a tip? Use a personal email address and a nonwork device; here's our guide to sharing information securely.Selected stories:Nvidia crushed its quarter — and CEO Jensen Huang said in a leaked all-hands that 'the market did not appreciate it'Nvidia will foot the bill for Trump's new visa fees. Here's what CEO Jensen Huang told staff.Massive AI salaries and RTO are fueling a real estate boom in San Francisco: 'It's going to rain money'The AI talent wars are ricocheting across startups. Here's how they're competing with Big Tech.
Nvidia is positioned as a durable, differentiated AI compute leader, with new $500B funding platforms set to deepen its CUDA moat. Current valuation rests on delivering ~40% FY28 earnings growth, with a 12-month base case price target of $275 and a Buy rating. Q2 earnings strength is expected, but broadening demand beyond hyperscalers and prudent credit management are critical near-term signals.
In the rapidly expanding semiconductor ecosystem, Advanced Micro Devices (AMD +1.01%) and Nvidia (NVDA -0.02%) stand as pivotal players powering the compute backbone of artificial intelligence (AI). Nvidia dominates with its Blackwell and Rubin GPU architectures, which deliver industry-leading performance for large-scale training and inference workloads.
Meanwhile, AMD contributes complementary technology via its Instinct MI-series accelerators and Epyc processors, offering high-performance CPUs and GPUs that hyperscalers integrate for cost-effective AI clusters.
Hyperscalers such as Microsoft, Amazon, Alphabet, and Meta Platforms rely on both companies extensively -- deploying Nvidia GPUs for AI acceleration while leaning on AMD's CPU and GPU offerings for broader data center efficiency and diversification away from single-supplier risk. Yet when the valuations are examined side by side, only one of these AI chip stocks emerges as the clear buy right now.
Image source: The Motley Fool.
Assessing the valuation discrepancy between Nvidia and AMD The chart below illustrates the forward price-to-earnings (P/E) ratios for Nvidia and AMD. The trends reveal a striking divergence: AMD currently trades at a forward P/E of 63, while Nvidia sits at 24. Over the displayed time period spanning mid-2024 through today, Nvidia's forward P/E has largely existed in a band between roughly 20x and 40x, whereas AMD's ratio has climbed sharply in more recent months.
Data by YCharts.
AMD's premium appears counterintuitive. Competitive pressures from custom ASICs developed by hyperscalers -- Google's TPUs, Amazon's Trainium and Inferentia chips, and Microsoft's Maia -- appear to be intensifying primarily against Nvidia, whose GPUs face substitution risk as cloud providers seek to lower infrastructure costs and assert greater control over their chip stack.
Therefore, some investors might actually expect Nvidia's valuation multiple to compress aggressively. However, these same dynamics do not apply to AMD, which commands an elevated valuation, while Nvidia's profile has stabilized to a more modest level.
This discrepancy suggests investors are pricing in catch-up potential for AMD's Instinct accelerators and accompanying software stack, even as the ASIC threat remains acute for Nvidia as the incumbent AI chip designer.
Comparing AMD's growth to Nvidia AMD's second-quarter financial results showed solid but comparatively measured expansion. The company reported total revenue of $11.5 billion, a 50% increase year over year. AMD's data center segment -- the primary AI growth driver -- generated $6.7 billion in sales, surging 107% year over year. Meanwhile, free cash flow grew modestly to $1.6 billion.
Nvidia has yet to report second-quarter results. But during the first quarter, Nvidia showed far greater scale and velocity relative to AMD. Total revenue reached $81.6 billion, up 85% year over year, with record data center revenue of $75.2 billion, climbing 92%. Lastly, Nvidia's free cash flow of $48.5 billion nearly doubled from the prior year.
AMD's absolute numbers remain a fraction of Nvidia's, and its overall growth rate lags its larger rival. Despite this slower trajectory and substantially smaller revenue base, AMD's valuation sits at 2.5 times Nvidia's, a valuation gap that seems difficult to justify based on growth and profitability metrics alone.
AMD vs. Nvidia: Which is the better stock to buy? The trends explored above illustrate a recurring pattern in which both Nvidia and AMD tend to rerate once their forward P/E ratios approach an elevated threshold. In prior cycles, AMD's forward P/E spiked above 50 or 60 only to experience subsequent normalization through multiples compression.
Nvidia has shown similar behavior, with its multiple rising and then settling into more sustainable ranges (which is happening right now). AMD's current levels position the stock for potential normalization in the near term, while Nvidia's relatively modest multiple leaves room for valuation expansion if its growth momentum persists.
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History suggests AMD shares could face selling pressure as its premium unwinds, even as Nvidia could accelerate into a new leg higher. Given Nvidia's superior scale, faster overall growth, significantly higher profitability, and more entrenched position in hyperscale AI ecosystems, I think the risk-reward balance favors investing in Nvidia at the moment. Investors seeking exposure to the ongoing AI semiconductor boom might be better served by buying Nvidia stock over AMD right now while the valuation gap remains this wide.
Inference has become the fastest-growing part of the artificial intelligence (AI) infrastructure market, and Bloomberg Intelligence projects it will double the size of the AI training market by 2032, reaching $1.3 trillion. With so much at stake, both leading chipmakers and upstarts are jockeying to grab a slice of this huge, fast-growing market.
Nvidia (NVDA -0.02%), Cerebras (CBRS +2.06%), and Advanced Micro Devices (AMD +1.01%) are all tackling this market in different ways. Let's see how these AI stocks stack up and why they could all be winners, given the size and growth of the inference market.
Image source: Getty Images.
1. Nvidia Already the winner in AI model training, Nvidia now has its sights on the inference market. The company's big move to capture share was its "acquisition" of Groq and its language processing units (LPUs). Inference is more about fast memory access and low latency than raw compute power, and LPUs help address this by having SRAM (static random-access memory) embedded directly on their chips.
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LPUs are particularly useful during the decode phase of inference, which is when large language models (LLMs) answer queries. As such, Nvidia now offers complete systems designed specifically for inference, where its graphics processing units (GPUs) handle the pre-fill phase (reading the prompt) while its LPUs handle the decode phase, thereby speeding up response times.
This is a nice solution and positions Nvidia to remain an AI infrastructure leader, even if it doesn't capture the same market share it does in training.
2. Cerebras Like Nvidia, Cerebras is tackling inference with SRAM-based chips. However, because SRAM is so bulky, instead of just embedding a small amount onto its chips and stringing them together, Cerebras has created huge wafer-sized chips that are five to six times faster than LPUs.
The physical size of Cerebras' chips comes with some trade-offs. They require specialized cooling and energy management solutions and, as such, are only sold or rented as part of the Cerebras CS-3 systems. They also come at a very premium price tag.
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However, the company has inked major deals with OpenAI and Amazon's AWS, and it recently announced a partnership with AMD that should help reduce the cost of ownership. The two companies will offer an inference solution in which AMD's Helios rack-scale solution will handle the pre-fill phase of inference, which it can do more cheaply, while Cerebras' Wafer-Scale Engine will perform the decode phase, which it can do more quickly. It's a nice way for companies to better compete with Nvidia's offerings.
Given the high cost of its systems, Cerebras has been more of a premium, niche solution, but it now looks set to become a major player in the humongous inference market.
3. AMD After losing out on the LLM training market to Nvidia, AMD has been aggressively pursuing the inference market to make sure it doesn't get left behind again. Its chiplet design is better suited for inference, as it allows its GPUs to be packaged with more high-bandwidth memory (HBM) and to act as part of an entire unit to reduce latency. Meanwhile, its partnership with Cerebras looks like a smart move to help it better compete with Nvidia's complete inference system.
However, the company has not stopped there. It recently acquired memory optimization company MEXT and chip start-up Taalas to boost its inference offering. Memory is one of the biggest AI bottlenecks right now, and MEXT's solution can offload seldom-accessed data from DRAM to unused flash and then, using predictive AI, can transfer it back into DRAM before an application even requests it. This can reduce the need for more expensive DRAM and help save costs.
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Meanwhile, Taalas has developed chips in which AI models are hardwired directly to bolster inference performance. Since the chips are model-specific, they aren't as flexible, but they are cheaper and much faster. The company plans to use them as part of a complete system where its GPUs would handle the pre-fill phase and Taalas' chips would handle the decode phase.
AMD is tackling inference from a couple of different angles, which should position it to capture a nice share of this huge market.
Even as NVIDIA Corp.’s (NASDAQ:NVDA) shares surge in August, cryptocurrency punters remain skeptical that the stock will finish the month at or above $250.
Polymarket Wagers on NVDAThe betting crowd on Polygon (CRYPTO: POL)-based Polymarket currently assigns only a 9% chance to the possibility, down from 50% a month ago.
The odds of NVDA ending above $260 also remained at 9%. Punters put the odds at 62% for a close above $210 on Aug. 31, and 87% for a close above $190. The prediction market was pricing a 99% probability that the stock would close above $140.
The stock closed at $200.75 on the last trading day in July, and has gained 8.34% month-to-date. It closed at $223.96 last week, but has since retraced.
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NVIDIA, often viewed as the definitive gauge of AI sector, is set to release its second-quarter results later on Aug. 26, making the month pivotal for the stock.
Analysts expect the company to report earnings per share of $2.07, up nearly 100% year-over-year. The firm is expected to report revenue of $91.82 billion, a 96% increase from the same quarter last year.
Investors are waiting to see if earnings beat expectations, and whether that catalyst is enough to push shares higher.
The stock carries a consensus “Buy” rating from 30 analysts, with an average price target of $313 and a third-quarter target of $323.
Price Action: NVIDIA shares rose 0.25% in after-hours trading after closing 0.02% lower at $217.50 during Tuesday’s regular trading session, according to data from Benzinga Pro. Year-to-date, the stock has rised 16.64%.
Benzinga Edge Stock Rankings indicate that NVDA maintains a stronger price trend across short-, medium-, and long-term timeframes, supported by elevated Value and Growth scores.
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Nvidia (NVDA -0.02%) signed memorandums of understanding Monday with six of Wall Street's largest investment firms to establish independent financing platforms -- vehicles meant to mobilize more than $500 billion of third-party capital for artificial intelligence (AI) infrastructure. The six are Apollo Global Management, BlackRock, Blackstone, Brookfield Asset Management, Goldman Sachs, and KKR.
The idea is to let lenders treat computing hardware the way they treat buildings or toll roads (an asset that produces revenue and can therefore secure debt).
Markets have run a version of this experiment before. In the late 1990s, telecom equipment makers lent their customers the money to buy their gear, and by the end of 2000, nine suppliers (including Lucent, Nortel, and Cisco) had extended about $25.6 billion, according to a McKinsey estimate.
Nvidia's new target is about 20 times that figure. The structure is different this time, and the difference matters. But the announcement leaves something open that may matter more.
Image source: Nvidia.
Compute as collateral The platforms are designed to create dedicated pools of outside capital for Nvidia's customers (the companies building AI data centers) so the next wave of hardware isn't paid for out of the buyers' own cash.
"This is really the first time that technology chips have become an investable asset class," CEO Jensen Huang told CNBC on Monday.
The market's response was cool. Shares slipped about 3% Monday and trade near $218 as of this writing.
Note what the plan is not: a response to weak demand. Nvidia's trailing-12-month revenue rose about 71% to $253 billion, and its net income roughly doubled from the year before, to about $160 billion. In the fiscal first quarter of 2027 (the period ended April 26, 2026), revenue grew 85% year over year, accelerating from the quarter before, and management guided for about $91 billion in the quarter it reports next. The sequence is still climbing.
The financing exists because the bill is starting to outgrow the buyers. Rating agencies have warned that record capital spending has begun squeezing the big AI spenders' free cash flow and pushing them toward heavier debt loads. In other words, the next $500 billion of hardware needs more money than the customers' own operations throw off.
The telecom precedent The last time an industry's suppliers arranged their customers' financing at anything like this scale, it ended badly. Network operators in the late 1990s demanded financing from equipment vendors as a condition of awarding contracts, and the vendors obliged with their own money.
When spending collapsed, 24 of the 30 largest publicly traded telecom carriers went bankrupt, and an estimated one-third to 80% of the suppliers' loan portfolios were lost. Lucent and Nortel were pushed to the brink of insolvency.
That history, to me, is the right measuring stick. Against McKinsey's count of what the era's nine biggest lenders had extended, more than $500 billion is roughly 20 times as much. Broader measures of the period's vendor lending run higher, which would shrink the multiple. On any measure, though, the new pool is far larger.
The difference is who carries the loans. Lucent's sat on Lucent's balance sheet, which is why its customers' failures nearly became its own. Nvidia isn't the lender here. The platforms are independent, and the money is Wall Street's.
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Who takes the loss? That is the part Monday's announcement doesn't finish explaining. The release describes the platforms as independent and the capital as third-party. It says nothing about who absorbs the damage when a borrower defaults.
The collateral case comes from Huang himself. Chips can be moved from one customer to another, he argues, and they keep improving through Nvidia's CUDA software. A lender could, in principle, take the hardware back and lease it to someone new.
Maybe so. But a repossessed chip is only worth what someone will pay for computing at that moment, and each new chip generation can cut the price of the one before it. If a borrower failed because AI demand cooled, the collateral would be cooling with it.
And financed demand is still demand. After all, hardware bought with borrowed money shows up in Nvidia's revenue exactly the way hardware bought with cash does. In the telecom collapse, that was arguably the demand that vanished first.
Ultimately, I think the platforms fix the failure that defined 2000 -- the vendor betting its own solvency on its customers' success. Nvidia's balance sheet stays out of the lending, as the release describes it, and the business it is financing demand for keeps posting extraordinary numbers.
What the structure can't change is what the money is for -- hardware that gets paid back out of AI revenue that mostly doesn't exist yet. The announcement doesn't say who takes the loss if that revenue falls short.
Jensen Huang built the world's most valuable company by pioneering the specialized computer chips behind the artificial intelligence boom.
To keep his vision for the future within reach, the Nvidia founder is now attempting a different kind of engineering: convincing Wall Street investors that those chips are long-term financial assets akin to commercial real estate or toll roads.
His bet hinges on outpacing AI developments in China.
This week, Nvidia unveiled agreements with six of the world's largest asset managers, BlackRock, Blackstone, Apollo, KKR, Brookfield and Goldman Sachs. The goal was to assemble a $500 billion pipeline to finance the construction of data centers and GPU clusters for companies that lack the credit rating or cash to buy millions of dollars of silicon outright.
Key to his plan, which Huang announced during a CNBC segment flanked by the leaders of all six Wall Street firms, is one crucial assumption: that Nvidia's graphics processing units will hold their value over time, behaving more like traditional hard assets than fast-depreciating consumer electronics.
"Nvidia's AI factory platform is really an investable asset, an infrastructure asset," Huang said. "The reason for that is because it's productive, it's revenue generating, it is fungible, it's used by just about every cloud service provider, it runs every AI model."
In standard asset-backed finance, a bank lends money because if a borrower defaults, the bank can repossess the asset — like a building, a warehouse or a cargo ship — and sell it to get their money back. Those physical assets have established secondary markets and can last decades.
But the productive lifespan of cutting-edge GPUs is far from settled.
While new chips power frontier model training, after a few years they are relegated to lower-margin inference work — a shift that directly impacts their resale and collateral value.
"Depreciation is the one key risk here," said Ben Emons, founder of FedWatch Advisors, who structured similar asset-backed loans for IndyMac before joining Pimco as a portfolio manager. Nvidia chips "could depreciate faster than expected," he said.
High-yield rates?In particular, Emons said he believes the single biggest threat to Nvidia's financing model comes from China, which is rapidly ramping up domestic compute capacity and could choose to flood the market with low-cost silicon in a price war.
If Chinese production pushes hardware prices into a freefall, the collateral backing hundreds of billions in private loans could erode far faster than the terms of the debt itself, leaving investors exposed to losses, according to Emons.
To compensate at least partly for that risk, Emons estimates investors will treat GPUs as high-depreciation equipment rather than real estate, demanding high-yield returns in the 11% to 17% range depending on where they sit in the capital structure.
On top of that, the borrowers are likely to be non-investment grade firms locked out of traditional debt markets, including AI startups and neoclouds, according to a Bank of America Securities note.
If those higher-risk borrowers go under, Wall Street fund managers will be forced to repossess and resell used chips into a potentially falling market.
Whatever risks China poses wouldn't be realized anytime soon. Huawei, the dominant provider of Chinese AI chips, has been on the U.S. Commerce Department's Entity List since 2019. And in May, the U.S. government said Huawei's Ascend AI chips violate U.S. export controls, preventing any American company from using the chips.
In the meantime, Nvidia remains by far the leading supplier of AI chips in the U.S., with upwards of 75% market share by most estimates.
And for now, the economics are still moving in Huang's favor. Driven by scarcity as hyperscalers race to build out capacity, rental rates for Nvidia's H100 chips rose from roughly $1.70 per GPU-hour in late 2025 to about $2.35 per GPU-hour this year, Huang noted.
Crucially, Nvidia argues its CUDA software layer — which enables developers to run AI workloads on its GPUs — continuously improves hardware performance after deployment, allowing older chips to stay productive and generate yield longer than traditional accounting models predict.
The future of the AI buildout, and hundreds of billions of dollars in investor money, may depend on who is right.
US investment giants including Apollo Global Management Inc., Blackstone Inc., BlackRock Inc. and Brookfield Asset Management are partnering with Nvidia Corp. to source $500 billion in financing for artificial intelligence infrastructure. Bloomberg's Ed Ludlow joins to discuss this as well as Intel raised $20 billion in an upsized share sale, a third more than it was targeting when it announced the deal Monday morning.
Nvidia (NVDA -0.02%) said yesterday that it has partnered with six of the biggest names on Wall Street to raise more than $500 billion in outside capital to fund the build-out of artificial intelligence (AI) data centers.
The agreements are memorandums of understanding (MOUs) -- preliminary handshake deals, not binding contracts -- meaning the $500 billion is a target, but not guaranteed at this point.
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Who Nvidia is partnering with and how the deal worksAccording to the company's Aug. 10 press release, Nvidia is teaming up with some of the largest asset managers and investment banks on the planet -- Apollo Global Management, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR.
The group will create what Nvidia calls compute financing platforms that will pool capital to lend to Nvidia customers building AI data centers with its advanced graphics processing units (GPUs). The funding is meant to cover the chips themselves along with servers, networking gear, buildings, and power.
Nvidia said it has the option to guarantee up to 25% of any of the deals, helping the customers receive a better rate.
CEO Jensen Huang spoke to CNBC on Monday, saying that GPUs have become "revenue-generating assets" and could be financed the way investors finance toll roads or power plants. He said that the chips are productive, long-lived, fungible, and flexible, and claims that a single rack of chips could serve one customer after another over its working life.
How $500 billion fits into the bigger AI spending pictureA figure that large is impressive, but it’s in line with what the industry is already spending. Analysts at Bank of America estimate that the big hyperscalers -- think Amazon or Alphabet -- alone will spend a whopping $860 billion on AI capital expenditures in 2026 and an incredible $1.2 trillion in 2027. Nvidia controls something like 85% or more of the market for AI GPUs, which in turn are the most expensive part of an AI data center, so it collects the lion’s share of every dollar spent on the build-out.
Image Source: Getty Images
Why this deal may not be as bullish as it soundsWhile the deal taps additional capital to keep the AI build-out humming along, it comes at a time when many investors are wary of its sustainability. Given that the hyperscalers, who run core businesses that steadily bring in enormous sums of cash, have begun to turn to the bond market and major stock sales, Nvidia’s announcement of yet another funding source could be more concerning than it is reassuring for many.
And there’s a real question of how much demand for this financial product there will be. Unlike power plants, Nvidia’s GPUs have a much shorter useful life. That makes using them as collateral an iffy proposition for investors.
The bottom lineI understand why Jensen Huang is pitching this. The more funding that pours into the system -- whatever the source -- the more chips Nvidia sells. But I’m not convinced it’s good for the health of the market as a whole, and I would guess this news actually hits Nvidia stock rather than helps it.
For now, wait and see if this becomes a reality and if these MOUs become signed agreements.
Johnny Rice has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends BlackRock, Blackstone, Brookfield Corporation, Goldman Sachs Group, KKR, and Nvidia. The Motley Fool has a disclosure policy.
NVIDIA Corp. (NASDAQ:NVDA) may still be the face of the artificial intelligence chip boom, but JPMorgan says the next phase of the semiconductor rally could spread much further across the industry.
• NVIDIA stock is trading at elevated levels. Where is NVDA stock headed?
The latest World Semiconductor Trade Statistics data show semiconductor fundamentals remain strong, with June industry sales reaching $152 billion and year-over-year growth accelerating to 134%, according to JPMorgan. More importantly, the strength is no longer confined to memory chips.
JPMorgan now sees the global semiconductor industry reaching $1.68 trillion in sales in 2026, up 111% year over year, followed by $2.25 trillion in 2027, a 34% increase.
That creates a much bigger opportunity for investors than the familiar Nvidia-led AI trade.
Semiconductor Growth Is BroadeningMemory remains the biggest driver of the industry’s growth. JPMorgan said memory accounted for roughly 77% of the year-to-date increase in semiconductor sales, with DRAM and NAND revenue continuing to benefit from higher pricing and shipment volumes.
But the bank is increasingly focused on what is happening outside memory.
Ex-memory semiconductor sales reached $63.5 billion in June, up 38% year over year and marking a third consecutive month of accelerating growth. Microcomponents, including microcontrollers, microprocessors and digital signal processors, were among the biggest sources of improvement.
The monthly data also show gains spreading across categories including analog chips, sensors and actuators.
That matters because it suggests AI infrastructure spending is beginning to create a broader semiconductor demand cycle rather than benefiting only the most obvious AI accelerators and memory suppliers.
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JPMorgan’s 6 Chip Stocks to WatchJPMorgan sees the strongest positioning across three parts of the semiconductor market.
For compute, JPMorgan picks Nvidia, Broadcom Inc. (NASDAQ:AVGO), Advanced Micro Devices, Inc. (NASDAQ:AMD) and Intel Corp. (NASDAQ:INTC). Nvidia remains the industry’s dominant AI accelerator supplier, while Broadcom and AMD offer exposure to custom AI infrastructure and competing compute platforms. Intel, meanwhile, gives investors exposure to CPUs and its increasingly important foundry and AI ambitions. For memory, JPMorgan favors Micron Technology, Inc. (NASDAQ:MU), as DRAM and NAND pricing continues to strengthen. For networking, JPMorgan again highlights Broadcom, alongside Marvell Technology, Inc. (NASDAQ:MRVL), as AI data centers require increasingly sophisticated connectivity to move data between processors and systems. That gives investors six ways to play the same underlying theme — but with very different exposures.
The AI Trade Is Getting BiggerThe most important takeaway may be that investors don’t necessarily need to pick the next Nvidia.
JPMorgan’s latest data suggest the AI infrastructure boom is creating demand across compute, memory and networking, while other semiconductor categories are beginning to recover as well.
JPMorgan also sees upside risk to its $1.68 trillion and $2.25 trillion industry forecasts because its model assumes largely seasonal growth across most categories.
For investors, that creates a broader semiconductor opportunity: Nvidia may have started the AI chip boom, but JPMorgan sees plenty of room for the rest of the industry to catch up.
Read Next
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HomeIndustriesComputers/ElectronicsTech StocksTech StocksNvidia’s new partnerships with financial players help mitigate concerns that the company is too tightly linked to its customers, according to BofA and Morgan StanleyAug. 11, 2026, 2:36 p.m. ET
Some investors have worried about the tight financial relationships between Nvidia and its customers, but analysts say that newly announced partnerships should put those worries to rest for now.
The company NVDA announced Monday that it has formed strategic partnerships for “independent compute financing.” These are worth more than $500 billion and are meant to support artificial-intelligence infrastructure development over time. Six firms including Apollo Global Management APO, Blackstone BX and Goldman Sachs GS will provide the third-party capital, the chip maker said.
Nvidia Corporation is spearheading a $500B compute financing platform with major financiers, signaling a next step in AI infrastructure investment. Demand for rapid, energized data center capacity is in demand, with a premium on near-term, high-quality assets and creditworthy tenants like Microsoft. Texas is one focal point, with large-scale projects such as Riot Platforms' $10B, 20-year deal and IREN's 750 MW flagship site offering much-demanded capacity for the AI buildout.
Characteristics and Risks of Standardized Options: https://bit.ly/2v9tH6D. Nvidia (NVDA) doubled down on the AI trade, with CEO Jensen Huang announcing that the company aims to finance $500 billion in AI infrastructure with the help of six key financial firms.
Key Takeaways NVIDIA's $500B AI push could broaden the AI trade beyond technology stocks.Wall Street could play a bigger role in financing the AI infrastructure boom.Financial ETFs offer a diversified way to play AI's growing financing needs. Nvidia (NVDA - Free Report) announced on Monday that it had entered into memorandums of understanding with Apollo Global Management (APO - Free Report) , BlackRock (BLK - Free Report) , Blackstone (BX - Free Report) , Brookfield (BAM - Free Report) , Goldman Sachs (GS - Free Report) and KKR (KKR - Free Report) to establish financing platforms aimed at supporting its customers, as quoted on CNBC.
The chipmaker is advancing its effort to turn AI computing into a new asset class for Wall Street, collaborating with major asset managers on a $500 billion financing push. The initiative seeks to make AI compute infrastructure akin to commercial real estate, toll roads and other assets that can be financed against.
As quoted on the abovementioned article, the NVIDIA initiative could unlock more than $500 billion in third-party capital for AI infrastructure, helping hyperscalers, frontier AI labs and enterprises expand data-center capacity and purchase NVIDIA hardware. By connecting its customers with institutional credit, insurance funds and private capital, NVDA is helping shift some of the funding burden away from their balance sheets.
Speaking with CNBC, Jensen Huang, NVIDIA’s founder and CEO, stated that AI computing is emerging as a new investable asset class, as quoted on the abovementioned article. Huang contended that NVDA’s widely adopted and transferable hardware enables lenders to underwrite AI computing as a durable, revenue-generating asset with a long useful life. The chipmaker’s efforts seek to turn AI computing capacity into a long-term, financeable asset. However, skeptics remain.
How Asset Managers Fit Into the AI Financing PushThe AI narrative is increasingly expanding beyond technology to the financing of the infrastructure behind it. NVIDIA’s latest financing push highlights the growing role of financial institutions in funding the infrastructure needed to support AI’s rapid expansion.
The ETFs mentioned below could offer investors exposure to companies positioned to benefit from the broader AI financing boom. Potential beneficiaries include lenders benefiting from growing credit demand, asset managers collecting fees on rising capital deployment, private-credit firms financing AI infrastructure and investment banks and capital-markets firms supporting the financing and structuring of these projects.
Huang believes the next phase of AI infrastructure financing will be driven by Wall Street rather than corporate balance sheets, with leading financial institutions playing a central role in funding the industry's expansion, as quoted on another CNBC article.
As per the previously mentioned CNBC article, alternative asset managers have been increasingly deploying capital into digital infrastructure, using institutional and insurance capital to finance these projects. Firms like Apollo and Blackstone have already helped finance companies like Anthropic through debt and equity arrangements.
Larry Fink, BlackRock’s CEO, along with executives at Wall Street firms like Blackstone’s President Jon Gray and Goldman Sachs’s CEO David Solomon, on Monday, stated that AI compute is emerging as a critical asset class that could drive the next phase of global economic expansion.
According to Blackstone’s Gray, as quoted on the previously mentioned CNBC article, AI compute could eventually be treated as a “financeable asset class,” with lenders financing computing infrastructure much as mortgage lenders finance homes.
Additionally, BlackRock’s Fink described the initiative as the beginning of a “next future for financial engineering,” drawing a comparison to the development of mortgage-backed securities in the 1970s. While some funds have already been raised, Fink said BlackRock plans to raise substantially more capital going forward.
However, it is important to note that the immediate benefit from NVIDIA’s announcement is likely to be concentrated among the financial firms directly involved in the initiative, rather than representing a broad-based tailwind for the entire financial sector. Broader financial companies could benefit if NVIDIA’s financing model gains traction and AI infrastructure investment continues to accelerate.
ETFs for a Direct Play on Alternative Asset ManagersVanEck Alternative Asset Manager ETF GPZVanEck Alternative Asset Manager ETF seeks to track the performance of MarketVector Alternative Asset Managers Index, which measures the performance of alterative asset managers across private equity, venture capital, private credit, private real estate and private infrastructure.
The fund has significant exposure to leading alternative asset managers, including Blackstone, KKR, Apollo Global Management and Brookfield Asset Management, which account for approximately 12.9%, 9.8%, 7.3% and 4.7% of the fund, respectively.
GPZ charges an annual fee of 0.40% and has a one-month average trading volume of about 164,000 shares. The fund has also amassed an asset base of $257.3 million. GPZ has gained about 8.3% over the past month and was up around 0.7% on Monday.
Tema Listed Private Managers ETF PRVTTema Listed Private Managers ETF employs an active strategy, seeking to provide a diversified exposure to publicly traded alternative asset managers across private equity, private credit, venture capital and real assets globally. The fund charges an annual fee of 0.75% and has gathered an asset base of $0.5 million.
The fund has a basket of 28 securities, with no single holding accounting for more than 5.55% of its exposure. PRVT has an exposure of 54.33% to the United States, followed by Canada (14.1%) and the U.K. (13.54%).
ETFs for Diversified Access to the Financial SectorBroad-based financial ETFs offer investors a diversified way to capture potential benefits from the AI financing boom.
Investors can consider State Street Financial Select Sector SPDR ETF (XLF - Free Report) , Vanguard Financials Index Fund ETF Shares (VFH - Free Report) , Invesco KBW Bank ETF (KBWB - Free Report) , iShares U.S. Financials ETF (IYF - Free Report) and iShares U.S. Financial Services ETF (IYG - Free Report) .
Among the mentioned funds, XLF and VFH have a Zacks ETF Rank #1 (Strong Buy). Meanwhile, IYF and IYG have a Zacks ETF Rank #2 (Buy). XLF is the cheapest option among the mentioned funds, charging an annual fee of 0.08%. XLF is also the most liquid fund, having a one-month trading average of about 32.59 million shares.
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At $218.10, NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) trades in a range where risk and reward are closely balanced. The chip giant sits at the intersection of the largest capital cycle in tech history and a growing debate over whether that movement is starting to look like a bubble. The same buildout powering NVIDIA’s growth is also feeding the case against it.
NVIDIA designs the accelerated computing platform that powers essentially every frontier AI model in production. The Data Center segment now represents 92% of total revenue, and the customer list reads like a who’s who of hyperscale AI: Meta, OpenAI, Google Cloud, Microsoft, Oracle, Anthropic, and xAI. The stock has climbed to a $5.42 trillion market cap on four consecutive earnings beats and margins near record highs.
Why the Musk Moonshot Could Push NVDA Toward $500 On SpaceX’s first earnings call, Elon Musk told investors he wants to scale data center capacity from roughly 1.4 gigawatts today to 10 gigawatts by year-end 2027. Research firm SemiAnalysis estimates the buildout at $50 billion per gigawatt annually, or $300 billion to $500 billion in 2027 capital spending alone, and considers the target achievable. Here’s the rub: Musk said SpaceX would work exclusively with NVIDIA, a decision SemiAnalysis ties to expected vendor-financing support.
That thesis is reinforced by the recent $500 billion financing partnership NVIDIA announced with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to underwrite AI factory buildouts. Layer even $150 billion to $200 billion in incremental SpaceX orders onto NVIDIA’s accelerating trajectory, including Q2 guidance of $91 billion, up roughly 34% year over year, and the path to a $500 share price becomes a math problem.
Why the Same Moonshot Could Crack the Stock The problem is scale. A single customer proposing capex on par with AWS and Google combined, at a company far less profitable than either, is exactly the kind of concentration risk bears have salivated over. NVIDIA’s $119 billion pipeline in supply commitments already assumes the AI capex party continues. If SpaceX funding tightens or hyperscalers slow orders to digest capacity, the demand cliff is real.
Valuation leaves no room for error. Shares trade at roughly 34x trailing earnings and 26x forward, with net insider selling across 27 recent transactions. Bears also point to zero China Data Center compute in the Q2 guide and the reality that AMD (NASDAQ:AMD) is courting the same hyperscalers with its MI450 roadmap.
Why Patience Is Winning the Argument Today The fundamentals are extraordinary. Nvidia’s revenue grew 85.2% YoY last quarter to $81.61 billion, gross margin sits at 75.0%, and free cash flow hit $48.55 billion in a single quarter. Yet the stock has been range-bound, up just 3.12% over the past month even as guidance accelerated.
Investors want confirmation, not projection. They want to see the OpenAI 10GW, Meta multi-generation, and SpaceX gigawatt commitments convert to booked orders on schedule. Until then, buyers and sellers are canceling each other out.
What the Numbers Actually Say NVIDIA currently trades at $217.43. The Wall Street consensus target is $302.83, implying roughly 37.6% upside. Coverage is heavily skewed bullish, with 10 Strong Buy, 48 Buy, 2 Hold, and 1 Sell ratings.
Year to date, NVDA is up 16.6%, edging the S&P 500’s 13.36% gain but trailing AMD’s 121.4% surge as the market prices in a genuine second source. Polymarket traders assign a 51.5% probability that shares close August above $220 and just 9.5% above $250.
The Verdict on NVIDIA at $217.43 At $217.43, the risk/reward on NVIDIA looks balanced.
The setup cuts both ways. If SpaceX directs even a fraction of its projected $300 billion to $500 billion in 2027 capital spending toward NVIDIA, with support from the Apollo and BlackRock financing platform, revenue estimates get rewritten higher and a $500 share price becomes a serious 12- to 18-month conversation. If Musk’s gigawatt math slips, funding stalls, or hyperscalers pause to digest capacity, the same $119 billion in supply-related commitments that looks visionary today starts looking like demand risk.
The right posture is patience with clear triggers. Bullish confirmation would require firm procurement commitments for the SpaceX buildout and continued NVIDIA Data Center growth above 60% into fiscal 2028. Bearish confirmation would call for a hyperscaler capital-spending cut, a material Blackwell delay, or evidence that the $500 billion financing platform is struggling to place capital. Watch NVIDIA’s quarterly Data Center growth, gross margin, and the pace at which announced megadeals convert into bookings.
When a single customer’s buildout can move a $5 trillion stock by $80 in either direction, waiting for the picture to develop is the prudent call.
Contact [email protected] for any questions or corrections.
CNBC's Leslie Picker, Kristina Partsinevelos and Ed Yardeni, Yardeni Research president, joins 'Power Lunch' to discuss Nvidia's recent partnership with Wall Street.
Index Dow Jones -0,34 % na 53791,91 b. S&P 500 -0,32 % na 7728,11 b. Nasdaq Composite -0,6 % na 26445,45 b.
Wall Street úterní seanci uzavřela poklesem, protože investoři začali pohlížet pesimističtěji na možnou dohodu, která by měla přinést stabilitu na Blízký východ. Výsledkem byly poklesy hodnot indexů. Index S&P klesl o 0,32% a uzavřel na hodnotě 7 728 bodů, index Nasdaq o 0,60 %, na hodnotu 26 445. Index Dow Jones Industrial Average klesl o 0,34 %, na 53 791 bodů.
Opět rostoucí ceny ropy a nejistý vývoj ohledně ukončení války vyvolaly očekávání ohledně středeční zprávy o indexu spotřebitelských cen. Po páteční zprávě o zaměstnanosti, která byla slabší, než se očekávalo, se očekávání přesunulo směrem k zářijovému zvýšení úrokových sazeb FEDem. Zároveň nové snahy společností Intel , Nvidia a jim podobných o získání finančních prostředků opět přitahují pozornost k kapitálově náročnému rozvoji umělé inteligence. Velké technologické společnosti vykázaly ztráty; akcie společností Alphabet, Apple a Amazon oslabily o 1 – 3,6 %. Ještě se čeká na výsledky společnosti CoreWeave působící v oblasti cloud computingu a výrobce serverů pro umělou inteligenci Super Micro Computer, které budou dnes zveřejněny po uzavření burzy, poskytnou přehled o stavu odvětví hardwaru pro umělou inteligenci.
Ropa posílila o 1,68 %, Zlato zůstalo poblíž své včerejší hodnoty a Bitcoin oslabil o 0,81 %
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Stock Market Limps On Dimming Optimism For U.S.-Iran Deal; Nvidia Sinks
Nvidia Confirms Huge AI Funding Deal. These Financial Stocks Jump.
Dow Jones Futures: Surging Oil Prices Spark Stock Market Losses; SpaceX Rival Rocket Lab Dives On Earnings Nvidia (NVDA) stock rose Tuesday in the wake of news that the company has rounded up more than $500 billion in third-party capital to help fund the buildout of artificial intelligence infrastructure. Nvidia announced on Monday that it has partnered with financial institutions Apollo (APO), BlackRock (BLK), Blackstone (BX), Brookfield (BN), Goldman Sachs (GS) and KKR (KKR) to establish AI…
NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) finances the entire AI industrial revolution. Shares are up 16.79% year to date, yet the stock sits 28% below its 52-week high despite posting $81.6 billion in Q1 FY27 revenue, up 85.2% year over year.
Can NVDA reach $325 per share in 2027?
Why NVDA Shares Are Stuck Despite Blowout Fundamentals The issue is complexity. When Nvidia announced a $500 billion AI infrastructure financing partnership with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR, shares slipped. Barron’s ran the headline “Nvidia Stock Slips as Its AI Investments Draw Fresh Scrutiny”, and NVDA dropped 2.86% in the most recent session. Investors struggle with interconnected deal structures they don’t fully understand.
The broader semiconductor sector hasn’t helped. Qualcomm fell 3.31% and Applied Materials dropped 3.12% on the same day, dragging sentiment across the group. With a beta of 2.22, NVDA amplifies every macro wobble.
Wall Street Sees 39% Upside. Our Model Says 25.5% Consensus is loud: 10 strong buys, 48 buys, 2 holds, and 1 sell, with an average analyst target of $302.83 and 95% bullish positioning. Our model is more measured. Base case predicted price of $272.96, or 25.47% upside, with 90% confidence. The optimistic scenario tops out at $315.40, while the bear case delivers $235.31.
With quarterly earnings growth of 214.5% year over year, analysts may be too conservative on FY28 EPS. If Vera Rubin ships on schedule, $302.83 becomes a floor.
The Path to $325 Per Share Reaching $325 from today’s price of $217.55 requires a 49.4% gain. With forward EPS of $8.25, a price of $325 implies a forward P/E of 39x. Our base case of $272.96 already implies 37x, meaning the bold target requires only 2x additional multiple expansion.
That’s achievable if EPS beats continue. Nvidia has beaten estimates for four consecutive quarters, and Q2 FY27 guidance calls for $91 billion in revenue. The 247Factor adjustment of 1.139 reflects strong sector momentum and 95% bullish analyst consensus.
Three catalysts matter: the $500 billion Wall Street financing platform, the Bank of America “multi-quarter upgrade cycle” thesis with a $350 price target, and the Vera Rubin launch.
CEO Jensen Huang said: “The buildout of AI factories, the largest infrastructure expansion in human history, is accelerating at extraordinary speed.” The primary risk is ongoing exclusion of China Data Center compute revenue from Q2 FY27 guidance.
Where NVDA Trades Today vs Its Earnings Power At $217.55 against forward EPS of $8.25, NVDA trades at roughly 26x forward earnings. For a company compounding earnings at triple-digit rates with 65.6% operating margins and a PEG ratio of 0.617, that multiple looks cheap.
Shares sit between a 52-week low of $163.85 and a high of $236.26. NVDA is up 13,975.65% over the past decade. Current valuation leaves room for the multiple to expand higher.
Can Nvidia Really Hit $325? My Verdict Reaching $325 requires a 49.4% gain and a forward P/E of 39x. It’s a realistic stretch.
Three things must go right: Vera Rubin ships without hiccups, Data Center revenue keeps compounding at the 92% YoY pace it just posted, and the $500 billion financing platform validates AI compute as an asset class. A hard freeze in hyperscaler CapEx would derail it. We’ve outlined the blueprint for how NVIDIA could reach $325 in 2027.
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Nvidia stock NVDA edged higher on Tuesday as investors assessed the chipmaker's new partnership with major Wall Street firms to mobilize more than $500 billion of third-party capital for artificial intelligence infrastructure.
Nvidia stock rose about 0.3% to around $218 after falling 2.9% on Monday, when the company announced MoUs with several financial institutions to expand access to capital for AI companies purchasing Nvidia hardware.
The broader market was under pressure. The S&P 500 fell 0.1%, while the Nasdaq Composite declined 0.4% and the Dow Jones Industrial Average shed 42 points, or 0.1%.
Oil prices also rose as uncertainty surrounding the conflict in the Middle East weighed on hopes that the Strait of Hormuz would reopen and raised doubts over a broader US-Iran resolution.
Nvidia's latest initiative is designed to address financing constraints facing companies building AI infrastructure.
The chipmaker has increasingly invested across the AI ecosystem, particularly in smaller companies that rely on Nvidia hardware.
The latest arrangement brings outside financial institutions into that funding process, potentially allowing Nvidia's customers to access significantly more capital without requiring Nvidia to fund the entire buildout itself.
Nvidia has signed MoUs with Apollo Global Management, BlackRock, Blackstone, Brookfield Asset Management, Goldman Sachs, and KKR to establish financing platforms designed to mobilize more than $500 billion of third-party capital over time.
Wedbush analyst Matt Bryson said the initiative could support Nvidia's position as the dominant infrastructure supplier for data-center projects outside the largest hyperscalers and frontier AI companies.
"Nvidia remains almost the sole supplier of infrastructure for non-hyperscale/frontier model datacenter builds. We see this fund as another mechanism that likely supports Nvidia's leadership and growth away from hyperscalers," Bryson wrote in a research note.
Balance-sheet exposure remains a questionThe involvement of outside investors could reduce the amount of capital Nvidia needs to commit directly to financing AI infrastructure.
However, investors are also examining Nvidia Chief Executive Jensen Huang's comments about potential guarantees connected to the initiative.
"In some cases, Nvidia may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis," Huang wrote on X.
He said the support would be limited and based on residual value, designed to complement rather than replace independent underwriting.
The financing initiative arrives as investors increasingly scrutinize the interconnected funding structures underpinning the AI infrastructure boom.
Nvidia has been involved in financing arrangements with companies that use its chips, raising concerns among some investors that vendor financing could create circular demand within the AI ecosystem.
Huang sought to address those concerns when Nvidia announced the new initiative, saying the capital would come from the financial consortium rather than represent Nvidia revenue.
Bank of America analyst Vivek Arya said the arrangement appeared to represent a shift away from vendor financing and argued that the capital commitment would sit with the financial consortium rather than Nvidia's balance sheet.
Morgan Stanley analysts also said the arrangement should help alleviate concerns over circular financing.
"For all of the handwringing over circularity, Nvidia’s actual direct credit exposure thus far is mostly confined to credit backstops with a couple of smaller neoclouds," Morgan Stanley analyst Joseph Moore wrote.
The financing push comes as major technology companies have raised substantial amounts of debt and equity to fund data centers, AI models, and AI-related infrastructure.
Alphabet, Amazon, Meta, Microsoft, and Oracle have raised more than $150 billion combined this year through debt and equity offerings.
Intel also recently announced a $15 billion stock offering before increasing the size to $20 billion.
The scale of external financing highlights the growing capital requirements of the AI buildout, particularly as some major technology companies have seen their cash flow come under pressure from heavy infrastructure spending.
The neocloud group is bouncing back midday Tuesday after a brutal stretch. Shares of TeraWulf (NASDAQ:WULF) are up 4% to $17, Hut 8 (NASDAQ:HUT) leads the group with a 7% gain to $91, Galaxy Digital (NASDAQ:GLXY | GLXY Price Prediction) is higher by 4% at $20, and Core Scientific (NASDAQ:CORZ) is up 4% to $20.
NVIDIA-Led $500B Financing Push Reignites the Trade The catalyst is a partnership between NVIDIA and Wall Street heavyweights to stand up a financing vehicle for the AI buildout. According to Yahoo Finance reports published this morning, NVIDIA is teaming with Apollo, BlackRock, Goldman Sachs, Blackstone, and KKR to mobilize more than $500 billion in third-party capital for AI data center infrastructure. The structure would shift financing off NVIDIA’s balance sheet and treat compute capacity as an investable asset class, with NVIDIA reportedly willing to underwrite up to 25% of qualifying loans. Wells Fargo reaffirmed an Overweight rating on NVIDIA with a $315 price target on the news.
For the neoclouds, this is a direct read-through. All four have built power-advantaged colocation platforms tied to hyperscaler and AI-native tenants, and their contracted revenue books are enormous relative to current cash flow. Cheaper, more abundant project-level financing lowers the cost of build-out and validates the take-or-pay model these operators are underwriting.
Rebound After a Rough Stretch The move follows real pain. WULF was down 26% over the past month, HUT off 24% in a week, GLXY lower by 22% in a month, and CORZ down 15% on the week. Part of that pressure came from commentary around SpaceX’s stated ambitions in compute, positioning Elon Musk’s private venture as a potential new hyperscale rival. That risk remains live, yet the financing news gives bulls a fresher, more concrete narrative to trade against.
The contracted backlog behind these names remains the anchor. TeraWulf points to roughly $27 billion or more in total contracted revenue across its platform. Core Scientific cites more than $24 billion in potential contracted revenue, anchored by a 15-year AMD partnership 15-year AMD partnership across roughly 530 MW. Hut 8 has flagged ~$26.6 billion in expected aggregate base-term value across 949 MW of contracted IT capacity. Galaxy’s Helios campus is set to begin recognizing roughly $80 million of quarterly lease revenue from CoreWeave starting Q3 2026.
Contact [email protected] for any questions or corrections.
NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) CEO Jensen Huang unveiled a financing framework last night that could reshape how AI infrastructure is bankrolled. Partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR aim to mobilize more than $500 billion in third-party capital for AI factory buildouts. NVIDIA shares edged higher on the news, while the three largest cloud providers traded lower.
The Quote That Reframes the Industry Huang’s central claim was that GPU compute has graduated into project-finance territory. “We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure,” he wrote, adding: “In AI, compute is revenue.”
The economics hinge on pricing power. One-year H100 rental rates climbed from roughly $1.70 per GPU-hour in October 2025 to $2.35 by March 2026, while B200 Blackwell cloud pricing now ranges from about $5.30 to $7.05. Huang also said NVIDIA may provide residual-value support covering up to 25% of an opportunity, evaluated project by project.
Why the Hyperscalers Sold Off Instead of celebrating, Amazon (NASDAQ:AMZN) is down 2.4%, Microsoft (NASDAQ:MSFT) is off about 1%, and Alphabet (NASDAQ:GOOGL) is losing nearly 2%. Polymarket assigned a 97% probability that Amazon would close lower and an 89.5% probability for Microsoft.
The reason is competitive plumbing. The capital pool would help qualified AI labs, enterprises, and cloud providers access AI-factory infrastructure at scale. In practice, it could give neoclouds and frontier labs more capacity to challenge the hyperscalers, which have spent years developing Trainium, TPU, and Maia chips to offset their NVIDIA dependence. Combined 2026 capital spending across the four largest hyperscalers is tracking near $745 billion, an outlay academic Aswath Damodaran recently characterized as “betting, not investing.” A new $500 billion financing channel for alternative infrastructure puts even more pressure on the returns behind that spending.
Where the Money Likely Lands CoreWeave (NASDAQ:CRWV) is the clearest tell. Its Q1 revenue grew 111.6% year over year, backlog soared to nearly $100 billion, and NVIDIA strategically holds a $2 billion equity stake. CEO Michael Intrator described the last quarter as “the strongest bookings quarter in CoreWeave’s history.” CoreWeave shares are up 1% today.
Huang’s own Q1 FY27 numbers explain the confidence to underwrite residual value: $81.61 billion in revenue, Data Center revenue up 92%, and Q2 guidance of $91 billion, give or take. As the Nvidia CEO framed it on the earnings call, “The buildout of AI factories, the largest infrastructure expansion in human history, is accelerating at extraordinary speed.” The latest announcement is his answer to who pays for it.
Contact [email protected] for any questions or corrections.
Nvidia Corp. (NVDA, Financials), the dominant maker of artificial intelligence chips, is expanding its push into open AI models as it looks for another way to i
Byron Deeter, Bessemer Venture Partners partner, joins 'Squawk on the Street' to discuss Nvidia partnering with six Wall Street asset managers on $500B AI infrastructure push.
Nvidia (NVDA +0.04%) supplies the world's most sought-after graphics processing units (GPUs) for data centers, which are the primary chips used in artificial intelligence (AI) training and inference workloads. Sales have been so strong that the company's market capitalization has ballooned from $360 billion to $5.4 trillion since the AI boom started gathering steam in early 2023.
Nvidia is scheduled to release its operating results for its fiscal 2027 second quarter (ended July 31), on Aug. 26, and all signs point to another blowout report. Its stock looks inexpensive right now, so here's why I predict it will soar in the aftermath.
Image source: Nvidia.
Nvidia will update investors on its most powerful chips ever Nvidia's Blackwell Ultra GB300 data center GPUs are currently the best chips on the market for processing AI workloads. In fact, they can deliver up to 50 times more performance in certain configurations than the company's H100 GPU, which launched in 2022, so a lot of progress has been made in the last four years.
But during the second half of this year, Nvidia will start shipping commercial quantities of its new Vera Rubin systems, which include the Rubin GPU, Vera central processing unit (CPU), and a series of upgraded networking components. They are slated to be so powerful that developers can train AI models with 75% fewer GPUs than the Blackwell platform, resulting in a 90% reduction in inference token costs.
Inference tokens are the text, images, and computer code generated by an AI model in response to a user's prompt. They are expensive to produce because they require substantial computing power and electricity, to the point that some large companies, including Walmart and Uber Technologies, have begun capping usage for their employees to prevent budget blowouts.
But if Rubin GPUs can reduce inference costs by 90%, that would encourage greater adoption while also improving data center operators' profit margins. That's why, according to Nvidia CEO Jensen Huang, every frontier model company in the AI industry plans to adopt Vera Rubin systems at launch, which wasn't the case with Blackwell. Investors can expect a progress update on Aug. 26.
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Wall Street is expecting very strong second-quarter results Nvidia generated $81.6 billion in total revenue during its fiscal 2027 first quarter (ended May 28), an 85% increase from the year-ago period. The data center segment alone accounted for $75.2 billion of that revenue, and it grew at an even faster pace of 92%.
According to the company's own guidance, the upcoming second-quarter report on Aug. 26 could show $91 billion in total revenue (plus or minus 2%), which would be a 95% increase from the year-ago period. The data center business is likely to have accounted for the overwhelming majority of that total once again.
Wall Street is aligned with Nvidia's forecast because the average analyst expects $91.8 billion in second-quarter revenue. The Street also anticipates generally accepted accounting principles (GAAP) earnings of $2.06 per share, which would be almost double the year-ago result. The global shortage of GPUs is allowing Nvidia to dictate prices, which is significantly boosting its profit margins.
Wall Street will also be watching Nvidia's forward guidance very closely, because it will be a clear indication of potential future GPU demand. Analysts believe the company will forecast around $103.1 billion in total revenue for the fiscal 2027 third quarter, so anything higher would be very bullish for Nvidia stock.
Nvidia stock is cheap by historical standards Based on Nvidia's trailing 12-month GAAP earnings of $6.53 per share, its stock is trading at a price-to-earnings (P/E) ratio of 34.3. That is a 44% discount to its 10-year average of 61.6.
Data by YCharts.
But it gets better, because Wall Street thinks Nvidia could grow its annual earnings to $9.45 per share during fiscal 2027, placing its stock at a forward P/E ratio of 23.7. In other words, the stock would have to soar by 160% over the next six months or so just to match its 10-year average P/E ratio of 61.6, assuming the Street's earnings forecast proves to be accurate.
Valuation is a big reason why I think Nvidia stock has room to soar after Aug. 26. If the company's second-quarter earnings report meets or exceeds Wall Street's expectations, it could squash some of the recent jitters in the AI infrastructure space and give investors the confidence to put some money to work.
Part of the $500B credit is likely backed by Nvidia Corporation. CEO Jensen Huang said: "In some cases, Nvidia may provide a residual-value support mechanism for up to 25% of an opportunity." The WSJ reporting a roughly $250 billion NVIDIA guarantee on OpenAI's Ohio lease in July suggests that is the playbook. NVDA stock fell almost 3% on the day of that announcement, underperforming the Nasdaq.
Key Takeaways NVIDIA's Vera CPU is up to 1.8 times faster than x86 processors on workloads and targets agentic AI.Vera integrates NVIDIA CPUs, GPUs, networking and software to support complete AI systems for customers.Anthropic, OpenAI and SpaceX plan to adopt Vera, while major hardware vendors prepare Vera-based systems. NVIDIA Corporation (NVDA - Free Report) is taking a bigger step into the CPU (Central Processing Unit) market with its Vera processor, designed specifically for agentic artificial intelligence (AI) workloads. The move could give NVIDIA another growth engine while increasing pressure on established server CPU leaders Intel Corporation (INTC - Free Report) and Advanced Micro Devices, Inc. (AMD - Free Report) .
NVIDIA’s Vera CPU is up to 1.8 times faster than x86 processors on workloads. The Vera CPU is designed to work closely with NVIDIA GPUs (graphics processing units), networking and software, allowing customers to build complete AI systems rather than relying on separate CPU and accelerator platforms. This integrated approach could be particularly attractive as AI agents require more computing power for reasoning, planning and data processing.
Vera CPU is also gaining support from major technology companies. Anthropic, OpenAI and SpaceX are among the AI organizations planning to adopt the platform, while Dell Technologies, Hewlett Packard Enterprise Company, Lenovo and Super Micro Computers are preparing Vera-based systems.
NVIDIA’s AI ecosystem gives Vera CPU an additional advantage and could help it gain meaningful server CPU share. The traction of Vera CPU will further boost NVIDIA’s data center end-market business. The company’s data center revenues reached a record $75.25 billion in the first quarter of fiscal 2027, rising 92% year over year.
Analysts’ projections suggest that the growth momentum in the data center business will continue. The Zacks Consensus Estimate for NVIDIA’s data center revenues is pegged at $363.78 billion, indicating year-over-year growth of approximately 88%.
NVIDIA’s Rivals Have Deep CPU Expertise to Defend Their LeadNVIDIA’s Vera CPU enters a market where Advanced Micro Devices and Intel have established customer relationships and large server CPU businesses.
AMD is the more direct growth challenger. Its data center revenues surged 107% year over year to $6.72 billion in the second quarter of 2026, driven by strong demand for EPYC processors and Instinct GPUs. Advanced Micro Devices is also seeing rising demand from AI workloads, including agentic AI, which directly overlaps with Vera’s target market. Its broad CPU-and-GPU portfolio gives customers an alternative to NVIDIA’s integrated platform.
Intel remains a major force in server CPUs through its Xeon portfolio. Its data center and AI business generated $6.26 billion in the second quarter of 2026, up 59% year over year. Intel’s latest Xeon processors are also designed to handle AI workloads, helping the company defend its position as AI increases demand for high-performance CPUs.
NVIDIA has an important advantage because Vera is designed to work closely with its GPUs, networking and software. However, AMD’s rapid growth and Intel’s large installed base mean NVIDIA will need to prove that Vera can deliver clear performance and efficiency benefits before it can seriously disrupt the CPU market.
NVIDIA’s Price Performance, Valuation and EstimatesShares of NVIDIA have risen around 16.6% year to date, underperforming the Zacks Computer and Technology sector’s gain of 18.1%.
NVIDIA YTD Price Return Performance
Image Source: Zacks Investment Research
From a valuation standpoint, NVDA trades at a forward price-to-earnings ratio of 19.93, below the sector’s average of 21.59.
NVIDIA Forward 12-Month P/E Ratio
Image Source: Zacks Investment Research
The Zacks Consensus Estimate for NVIDIA’s fiscal 2027 and 2028 earnings implies a year-over-year increase of approximately 90.6% and 38.3%, respectively. Estimates for fiscal 2027 and 2028 have been revised upward over the past 30 days.
Image Source: Zacks Investment Research
NVIDIA currently carries a Zacks Rank #2 (Buy). You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.
Daily NVIDIA Corporation Nvidia assembled six of the largest institutional capital firms to commit more than $500 billion to build the data-center infrastructure its chips require. The company turned its biggest risk into its biggest catalyst by getting someone else to fund the buildout. Nvidia is up more than 1% premarket on the announcement and the stock is recovering Monday’s nearly 3% loss in one session.
Intel expanded its stock offering to $20 billion from $15 billion on the same morning. One company has outside capital lining up. The other is asking shareholders for more. The Nasdaq knows the difference.
Hormuz Is Still Sitting Behind the Opening Bid The early equity bid holds because oil stopped climbing for the moment. Iran and Oman may agree on shipping lanes. Iran has not agreed to direct negotiations with Washington or dropped its conditions for a broader settlement. The strait sits where it has been for weeks, close enough to a deal to move crude on a headline but not open enough to remove the energy premium.
If crude starts moving higher again, the same problem comes back. Higher energy prices rebuild the inflation argument and force traders to reassess how much rate relief is actually available. The Nasdaq can lead on AI spending. It cannot lead through another leg higher in oil.
What to Watch Nvidia’s $500 billion financing framework is the morning’s lead because it changes the funding structure of the AI buildout, not just the demand story. The broader market has a modest bid but it needs oil to stay where it is and the Hormuz headline machine to produce something more than another statement.
The S&P 500 is pressing toward last week’s high with both trends intact. The Nasdaq is within range of its record. The Dow needs to clear its pivot to confirm the buyers are back. All three indexes are trading above their 50-day moving averages, which keeps the structure bullish as long as the energy trade does not reassert itself.
NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) and Apple (NASDAQ:AAPL) just delivered earnings that frame the two dominant bets in tech today.
Nvidia posted $81.61B in Q1 FY27 revenue with Data Center at $75.25B. Apple countered with $109.4 billion in Q3 FY26 revenue and its strongest June quarter ever. One sells the shovels. The other sells the finished product.
AI Factories Carry Nvidia. iPhone and Services Carry Apple. Nvidia’s quarter was almost entirely a Data Center story. Compute rose 77% and networking, powered by InfiniBand, NVLink and Spectrum-X, jumped 199%. Jensen Huang told investors “the buildout of AI factories, the largest infrastructure expansion in human history, is accelerating at extraordinary speed.”
Hyperscalers still represent roughly 50% of that segment, with sovereign AI programs and enterprise deployments filling in behind them. Non-GAAP gross margin hit 75%, and free cash flow reached $48.55B.
Apple’s engine looked different. iPhone revenue climbed to $54.252 billion, Services set a June quarter record at $30.7 billion, and paid subscriptions crossed 1.5 billion.
Tim Cook called it “our strongest June quarter ever, with double-digit revenue growth across iPhone, Mac and Services, and in every geographic segment.” A tariff refund added roughly 2 percentage points to gross margin and $0.11 to EPS, which is a one-time gift worth remembering.
Picks and Shovels vs. the Consumer Ecosystem Lens NVIDIA Apple Core Bet AI factories, agentic compute iPhone cycle, Services, on-device Siri AI Growth Rate +85.2% YoY revenue +16.36% YoY revenue Gross Margin ~75% non-GAAP ~50.1% Key Vulnerability China export controls, zero H20 revenue Memory cost inflation, tariff policy Nvidia is scaling Blackwell 300, Vera Rubin and BlueField-4 into every hyperscaler and sovereign AI cluster. Apple is monetizing an installed base above 2.5 billion active devices while rolling out Siri AI to WWDC26 developers.
Cook framed the differentiator as “the unique combination of massive unified memory bandwidth, industry-leading power-efficient performance, and deep on-device intelligence.”
Kevan Parekh flagged a “100-year flood on the memory pricing” that could pressure September quarter margins to 47%-48%. Nvidia has its own supply worry: $119B in purchase commitments tied to TSMC capacity.
The Next Test Is Guidance Nvidia guided Q2 FY27 to $91B in revenue, excluding any China Data Center compute. Prediction markets on Polymarket now imply a 97.2% probability that Data Center clears $80B, but only 19.5% for $90B. That is a narrow beat lane.
Apple guided September quarter growth of 9%-11%, constrained by advanced-node SoC supply that Cook attributed to “a demand forecast issue” rather than a shortage. I want to see whether Siri AI actually pulls subscribers up the iCloud+ stack, and whether Nvidia’s networking growth holds once Blackwell shipments normalize.
Why I Lean Nvidia for Growth, Apple for Ballast If I had to pick one, I would still tilt toward Nvidia for the growth sleeve. A P/E of 34 against +85% revenue growth and 75% margins looks rich, yet the earnings power is compounding faster than the multiple.
Apple, trading at a P/E of 36, fits the defensive investor better. The $62.094 billion in nine-month buybacks and the Services flywheel offer stability that Nvidia cannot match. If memory costs stay elevated into 2027, or China export rules loosen for H20, my ranking could flip fast.
Contact [email protected] for any questions or corrections.
The NVIDIA logo in this illustration taken June 11, 2026. REUTERS/Dado Ruvic/Illustration Purchase Licensing Rights, opens new tab
Aug 11 (Reuters) - Nvidia (NVDA.O), opens new tab is developing a new AI model family, Nemotron 4, with the goal of rivaling top open-source models globally, The Information reported on Tuesday, citing people who work on the project.
The chip giant is among the few major U.S. firms to release open-source models, which have drawn more attention this year as AI bills balloon and cheap Chinese models near the capabilities of top systems from leading American labs Anthropic and OpenAI.
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A spate of recently disclosed hacks involving autonomous AI agents has added to the attention, especially because open models do not have curbs on cybersecurity use.
The largest Nemotron 4 model is expected to have at least 1 trillion parameters, according to multiple employees working on the project, The Information reported.
Nvidia has not set a release date for Nemotron 4 and has yet to complete final training, though employees said the model could be ready as early as late fall, according to the report.
The company did not immediately respond to a Reuters request for comment on the report.
Nvidia last month formed a coalition with other companies to develop and share tools for AI safety and cybersecurity. It also signed an open letter with tech heavyweights such as Microsoft (MSFT.O), opens new tab backing open-weight models so that innovation does not drift overseas.
Separately on Tuesday, the chip firm unveiled Nemotron 3.5 Lightning, an addition to its offerings aimed at code review, tool use, security alert monitoring, answering billing questions and other tasks.
It also released NeMo Switchyard, an open-source model-routing library designed to automatically direct AI tasks to the most suitable models.
Late last year, the chip giant unveiled the third generation of its family of open-source models as offerings from Chinese AI labs proliferated.
Reporting by Anhata Rooprai in Bengaluru; Editing by Pooja Desai
Our Standards: The Thomson Reuters Trust Principles., opens new tab
The next AI winner may not be the company with the biggest model. It could be the company that figures out how to get more useful AI for every dollar it spends. Nvidia Corp. (NASDAQ:NVDA) is helping shape that shift by powering more efficient, task-specific AI systems that aim to squeeze more performance out of every GPU cycle.
DigitalOcean Holdings Inc. (NYSE:DOCN) CEO Paddy Srinivasan told Benzinga in an exclusive email interview that AI builders are increasingly mixing different models for different jobs rather than relying exclusively on expensive frontier systems from companies such as OpenAI and Anthropic.
"We believe in: right model, right cost, for every task," Srinivasan said.
He pointed to healthcare AI company Hippocratic AI as an example, saying AI builders like Hippocratic "get better intelligence per dollar" as they optimize across models.
Hippocratic’s connection to Nvidia makes that strategy particularly interesting. Nvidia says Hippocratic’s Polaris architecture runs more than 25 task-specific AI models on Nvidia H200 GPUs, while its TensorRT-LLM software makes those models faster, smaller and more efficient, lowering costs and allowing more conversations to run on the same hardware.
The AI Model Doesn’t Have to Be the Most ExpensiveSrinivasan said frontier models are typically needed for only about 25% of the job, mainly the hardest reasoning or specialized use cases. The remaining 75% can often be handled by open-weight models, which can offer lower-cost alternatives for less demanding tasks.
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That creates a different optimization problem for AI companies.
Instead of asking which model is the smartest, they can ask which model is smart enough for a particular task at the right price.
"Most AI Native companies today are already multi-model," Srinivasan said. "They all have a mixture of models and route specific prompts to the right model."
DigitalOcean’s Inference Engine is designed to route workloads based on factors including performance, latency, cost and customer preference.
Hippocratic Shows Why Nvidia’s Hardware MattersHippocratic is a useful example because its healthcare AI requires real-time responses while handling safety-sensitive conversations.
The company’s Polaris system runs on Nvidia H200 GPUs and uses more than a trillion parameters across its model constellation. The goal isn’t simply to use the most powerful hardware or model available. It is to make the entire system more efficient so Hippocratic can handle more interactions without proportionally increasing its computing costs.
That is the strategy behind Srinivasan’s "intelligence per dollar" argument.
‘Intelligence Per Dollar’ Could Become the New AI MetricNvidia itself has increasingly emphasized the economics of AI, often focusing on concepts like performance per dollar and the cost efficiency of AI compute to describe the value businesses can get from their computing investments.
That could change how investors view the AI race.
The industry’s first phase was dominated by model size, training costs and the race to build increasingly powerful systems. As AI moves into everyday business applications, however, the economics of actually running those models become harder to ignore.
If companies can use a mix of models and optimize the infrastructure underneath them, the winners may not necessarily be the companies with the biggest AI models.
They could be the companies that figure out how to get the most useful intelligence for every dollar of compute.
Read Next
Image courtesy of DigitalOcean Holdings Inc
Market News and Data brought to you by Benzinga APIs
Chinese humanoid robot maker Unitree just delivered one of the loudest signals yet that investor appetite for physical AI has reached full boil. Reuters reports Unitree’s $900 million Shanghai IPO was more than 8,000 times oversubscribed by retail investors, with a lot-winning rate of roughly 0.018%. For context, SpaceX’s much larger offering was approximately 4x oversubscribed ahead of its June debut, Snowflake’s 2020 IPO was 120x, and Facebook’s 2012 deal was 20x.
U.S. investors chasing the same theme have options beyond the Unitree lottery. Agility Robotics is heading public via a $2.5 billion SPAC, and a deep bench of listed sensor suppliers, warehouse automators, and AI infrastructure names already trade daily. Below, we count down the top five publicly traded robotics stocks based on execution, revenue growth, margin trajectory, and strategic positioning in physical AI.
5. Aeva Technologies Aeva Technologies (NASDAQ:AEVA) is small but strategically loaded. Q2 2026 revenue of $6.14 million grew 11.3% YoY and beat consensus by 3.64% while gross profit swung to +$2.19 million from a $2.72 million loss a year earlier. A $115 million follow-on boosted liquidity to $302.90 million, funding a new Optical Connectivity business with a hyperscaler joint development agreement. Automotive programs with Daimler Truck, a top-10 European OEM, and NVIDIA DRIVE Hyperion anchor the sensing thesis. Shares are up 84.26% year to date.
4. Ouster Ouster (NASDAQ:OUST) delivered the cohort’s strongest sensor-supplier quarter. Q2 2026 revenue jumped 55.9% year over year to $54.63 million, beating estimates by 7.36%. GAAP gross margin expanded 400 basis points to 49%, while sensor shipments topped 17,000.
Ouster CEO Angus Pacala explained, “Customers around the world have continued to scale their investments in Physical AI, and Ouster is well positioned to benefit as autonomy moves into more complex, real-world applications.” The stock has responded, up 108.1% year to date.
3. Symbotic Symbotic (NASDAQ:SYM | SYM Price Prediction) is the scaled non-humanoid play. Q3 FY2026 revenue rose 21.7% year over year to $720.84 million, while gross margin widened from 18.9% to 22.3% and adjusted EBITDA more than doubled to $95 million. A roughly $22.5 billion backlog provides visibility, and the Exol joint venture with Japan’s SoftBank opens the door to a warehouse-as-a-service market valued above $500 billion. The risk side remains hard to ignore: Walmart accounts for an outsized share of Symbotic’s total revenue, internal-control weaknesses persist, and the stock is down 30.6% year to date at last check.
2. Tesla Tesla (NASDAQ:TSLA) is the highest-profile robotics narrative in the market, even if Q2 profitability disappointed. Revenue rose 25.5% year over year to $28.24 billion on record deliveries of 480,126, but non-GAAP EPS of $0.33 missed the $0.54 estimate by a mile as operating margin narrowed to 1.4%. Optimus production lines are being installed in Fremont, Robotaxi now spans seven U.S. metros, and Full Self-Driving (FSD) subscribers jumped 56% to 1.48 million. Prediction markets assign just 14.5% odds to an Optimus release by year-end.
1. NVIDIA NVIDIA (Nasdaq: NVDA) is the pick-and-shovel supplier behind nearly every robotics and physical-AI thesis on this list. Q1 FY2027 revenue surged 85.2% year over year to $81.61 billion, beating estimates by 3.16%, as Data Center revenue jumped 92% to $75.25 billion. Non-GAAP gross margin reached 75%, with Q2 revenue guided to $91 billion. CEO Jensen Huang famously called it “the largest infrastructure expansion in human history.” The Isaac platform, Cosmos, GR00T N models, and DRIVE Hyperion partnerships with Hyundai, BYD, and Uber put NVIDIA inside virtually every humanoid, autonomous vehicle, and warehouse automation roadmap. Shares carry a P/E near 33x and are up 17.4% year to date. Prediction markets assign a 96.1% probability that Q2 Data Center revenue clears $80 billion.
Where Robotics Fever Actually Cashes In Unitree’s 8,000x oversubscription reflects the same thesis powering NVIDIA’s data center backlog, Symbotic’s warehouse deployments, and Ouster’s sensor shipments: physical AI is moving from demo to deployment. Retail investors can get exposure through U.S.-listed names. NVIDIA supplies the compute, Symbotic runs the aisles, Ouster and Aeva build the eyes, and Tesla is trying to build the body. Watch the next earnings cycle to see which of these five converts robotics hype into recurring revenue.
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$500 billion. That's how much money Nvidia (NVDA) wants to source in financing through six key asset management firms for the AI infrastructure buildout.
Nvidia said on Monday it has partnered with six major financial institutions to launch compute financing platforms aimed at raising over $500 billion in third-party capital for AI infrastructure.
Nvidia CEO Jensen Huang said on X that the company has the option to backstop up to $125 billion, or 25% of the potential deals.
The move highlights how surging demand for AI computing capacity is drawing institutional investors, as governments, companies and startups race to build out data centers to support AI workloads.
Nvidia said on Monday it has partnered with six major financial institutions to launch compute financing platforms aimed at raising over $500 billion in third-party capital for AI infrastructure. REUTERS Big Tech companies have signaled that spending on AI would not slow down, with combined outlays set to surpass $730 billion this year.
Nvidia signed memorandums of understanding with Apollo APO.N, BlackRock BLK.N, Blackstone BX.N, Brookfield BAM.N, Goldman Sachs GS.N and KKR KKR.N for the financing platforms.
The initiative is intended to broaden access to Nvidia-based infrastructure among frontier AI developers, enterprises, governments and cloud providers, while creating longer-duration, usage-linked investment opportunities for large asset managers and private capital firms.
“These financing platforms will help customers access scarce compute at scale and build the AI factories that will power every industry and country in the age of AI,” Huang said.
Nvidia said the arrangements would “create dedicated pools of capital at significant scale at attractive rates” for its customers.
Nvidia CEO Jensen Huang said on X that the company has the option to backstop up to $125 billion, or 25% of the potential deals. AP Photo/Julia Demaree Nikhinson The company did not disclose the financial terms, investment commitments by individual firms or a timetable for deploying the planned $500 billion.
The Financial Times had reported the development first on Monday, later confirmed by Reuters.
I keep buying NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) shares, and the Aug. 26 earnings report is another reason to keep going.
Here is the plain version of what pulls me back to the buy button. Jensen Huang calls this “the largest infrastructure expansion in human history”, and every hyperscaler earnings report this cycle backed him up. Microsoft, Meta, Alphabet, and Amazon collectively guided to over $200 billion in AI infrastructure spend, and Nvidia commands over 80% market share in high-end AI accelerators. When the four biggest customers on the planet all commit to spending more, and one vendor sits in the middle of that check, I keep writing my own smaller checks into the same name.
The Numbers That Keep My Hand on the Buy Button Start with the growth. Q1 FY27 revenue landed at $81.615 billion, up 85.23% year over year, beating estimates by 3.16%. Non-GAAP EPS came in at $1.87 versus a $1.7738 estimate, the fourth consecutive beat. Data Center revenue alone was $75.246 billion, up 92% YoY, with networking up 199%. Management guided Q2 FY27 to $91 billion, sequential acceleration on a base that already dwarfs the industry.
Then the moat. Nvidia carries $119.0 billion of supply commitments and $30.0 billion of multi-year cloud service commitments. That is visibility. OpenAI committed to 10 gigawatts of Nvidia systems, Meta signed a multigenerational deal across millions of Blackwell and Rubin GPUs, and CoreWeave is targeting 5+ gigawatts of AI factories by 2030.
Then the balance sheet. Return on equity sits at 101.5%, net margin at 55.60%, debt to equity at 0.073, and interest coverage at 503x. The board raised the dividend from $0.01 to $0.25 per share and authorized an additional $80.0 billion buyback. That is what management confidence looks like on paper.
Why Not the Obvious Alternatives The two names I hear most from friends are AMD (NASDAQ:AMD) and Broadcom (NASDAQ:AVGO). AMD’s MI-series is a real product, and it competes without the CUDA software lock-in or NVLink networking that let Nvidia’s Data Center Compute grow 77% YoY. Broadcom’s custom silicon business is strong, and it is a components play into a few customers rather than a full-stack platform running a 71.07% gross margin. Amazon (NASDAQ:AMZN) has Trainium as a multibillion-dollar business now, and Nvidia’s Data Center Compute still grew 77% YoY right through it.
The Risk I Am Not Ignoring China is the real one. Nvidia’s Q2 guide excludes any Data Center compute revenue from China, erasing what used to be roughly $4.6 billion per quarter in H20 sales. That is real lost revenue. The thesis holds because non-China demand is more than offsetting the shortfall, and management still guided $91 billion in the same breath.
Where My Conviction Goes From Here Shares closed at $217.55, up 16.79% year to date, on a P/E of 44. For a business generating $48.554 billion of free cash flow in a single quarter, with a 75% gross margin and a product roadmap that already extends through Rubin, I will keep clicking buy into Aug. 26 and after it settles.
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The largest capital pool ever assembled around a single technology theme just got a new front door. On CNBC yesterday, co-anchor Becky Quick reported that “Nvidia is coming together with some of the biggest names on Wall Street to put together half a trillion of independent financing, to kind of push AI forward, to build the AI infrastructure out.” The story, initially broken by the Financial Times, positions NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) at the center of a financing consortium designed to unclog what CEO Jensen Huang has long flagged as the single biggest constraint on AI adoption: capital.
Crucially, “Nvidia is not putting up any of the money. This isn’t a circular deal. This is Nvidia basically playing matchmaker to bring together some of these names that have already been pretty active when it comes to financing for some of these deals.” Nvidia will identify customers that need financing to build out AI capacity and connect them with the pledging partners.
Why Huang Wants a Matchmaker Nvidia’s Q1 FY2027 report already showed a business straining against the outer edge of what customers can finance. Data Center revenue reached $75.25 billion, up 92% year over year, and total supply commitments swelled to $119.0 billion. Huang has framed the moment as “the buildout of AI factories, the largest infrastructure expansion in human history” (see the company’s Q1 FY27 earnings release).
Quick captured the logic neatly: “Jensen Huang has for a long time been looking for bottlenecks that would prevent AI from reaching its full growth potential. I think this is a bottleneck that they’ve identified and said, here’s a way that we’re going to go about addressing it.” With hyperscalers projected to spend approximately $3.5 trillion between 2026 and 2028 on data centers, chips, power, and real estate, financing gaps could throttle Nvidia’s chip demand well before compute demand tops out. Shares closed at $217.55, up 16.79% year to date.
What Wall Street Gets The six partners are, in Quick’s words, “the biggest and best names on Wall Street. You’ve got Goldman Sachs, BlackRock, Blackstone, KKR, Apollo and Brookfield.” Each is sitting on record dry powder and hunting for scaled, long-duration assets.
Goldman Sachs (NYSE:GS) posted Q2 2026 revenue of $20.34 billion and investment banking fees up 55%, giving it the underwriting muscle to structure syndicated AI credit at scale. BlackRock (NYSE:BLK) reported record AUM of $15.34 trillion, with CEO Larry Fink pitching the firm as a scaled private markets platform after the HPS Investment Partners deal. Blackstone (NYSE:BX) manages $1.35 trillion in AUM. Stephen Schwarzman has publicly leaned into the AI megatrend, calling Blackstone “a trusted partner at scale to many of the key innovators in this ecosystem.” KKR (NYSE:KKR) holds $143 billion of dry powder against $796.5 billion in AUM. Apollo Global Management (NYSE:APO) crossed $1.05 trillion in AUM and pulled in $60 billion of Q2 inflows, a natural fit for private credit tickets tied to data centers. Brookfield Asset Management (NYSE:BAM) already announced a $100 billion AI data center campus at the DOE’s Paducah, KY site and expanded a Bloom Energy partnership from $5 billion to $25 billion for AI power. For these managers, Nvidia effectively becomes an origination engine. As Quick summarized, “Nvidia will find its customers that need help with AI build out need financing for this and put them together with these partners that are pledging, again, over half a trillion that they will find to come into this.”
What to Watch Next Investors should track three signals: which Nvidia customers surface first as borrowers, how the partners split roles between senior debt, mezzanine, and equity, and whether power and land become the binding constraint rather than GPUs. Nvidia’s own guidance of $91.0 billion in Q2 FY27 revenue assumes customer capacity keeps expanding. This consortium is Huang’s insurance policy that it will.
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After hovering just below $200 per share for the first three months of the year, Nvidia's (NVDA -2.86%) stock price surged past that level in May. Now, $200 is starting to look more like a floor for the chipmaker's share price as opposed to a ceiling.
But Nvidia's management clearly likes to keep its shares affordable. The company has regularly conducted stock splits, including two in the last five years alone. With the stock price up more than 80% since the last split, could another split be coming this year? Here's what we know.
Image source: Nvidia.
Two by two Nvidia has split its stock six times since 2000. But those splits haven't come at regular intervals. Instead, they've come in pairs.
The first two splits were just 15 months apart, in June 2000 and September 2001. Then, there was a big gap before the next ones, which were just 17 months apart in April 2006 and September 2007. After a 14-year gap, we got a split in July 2021, followed by another in June 2024. So history suggests we should have a much longer wait for the next split.
That's doubly true since the 2024 split was a doozy. Instead of a standard 2-for-1 split -- in which each stockholder receives two new shares for every one old share they hold, with the new shares worth half of the old share price -- the 2024 split was 10-for-1, which increased the number of outstanding shares tenfold but also cut the stock price from about about $1,200 to about $120.
Today's Change
(
-2.86
%) $
-6.41
Current Price
$
217.55
Because most brokerages now offer investors the option of purchasing fractional shares, a high stock price isn't necessarily a barrier to stock ownership for individual investors. Considering that Nvidia's shares were well above $1,000 before management split them two years ago, another split this year seems unlikely unless the company's share price goes stratospheric.
John Bromels has positions in Nvidia. The Motley Fool has positions in and recommends Nvidia. The Motley Fool has a disclosure policy.
Geopolitical movers are taking a backseat to Nvidia's (NVDA) strong statement for the AI trade. Tom White talks about the Mag 7 giant's $500 billion push to treat compute like an asset class, something he sees adding muscle to the backbone of tech.
