Nvidia beats Wall Street expectations with $81.6bn Q1 revenue as AI infrastructure boom accelerates

Nvidia forecasts revenue above estimates, announces $80 billion share buyback

May 20 (Reuters) - Nvidia (NVDA.O), opens new tabforecast second-quarter revenue above Wall Street expectations on Wednesday and announced an $80 billion share repurchase program. Shares of the company were up 1.3% in extended trading. The world's most valuable company expects revenue of $91 billion, plus or minus 2%, compared with estimates of $86.84 billion, according to data compiled by LSEG. Nvidia's results are largely considered a barometer for the AI market's health, as its chips are used in virtually every major data center in the world, powering the largest and most advanced AI models. "Nvidia delivered another beat, but at this point that's essentially priced in as it keeps ⁠beating quarter after quarter," said eMarketer analyst Jacob Bourne. "The lingering question is whether it can convince investors the AI buildout has durability into 2027 and 2028, especially as the narrative shifts toward inference workloads and competing silicon from Google, Amazon, AMD, and Intel." The company also said it would increase its quarterly cash dividend to 25 cents per share from 1 cent. Spending on AI infrastructure continues to grow rapidly, with U.S. tech giants, including Alphabet (GOOGL.O), opens new tab, Amazon (AMZN.O), opens new tab and Microsoft (MSFT.O), opens new tab, expected to spend more than $700 billion on AI this year, a sharp jump from around $400 billion in 2025. RISING COMPETITION FROM CUSTOM CHIPS While heavily relying on Nvidia's expensive processors, the companies are also pouring funds into developing their own custom chips to run models, posing a risk to Nvidia's long-held dominance over the chip industry. Those chips are targeted at inferencing - the process by which AI responds to user queries - which represents a ⁠much larger market than training. Nvidia is facing competition not only from Big Tech but also from other chip rivals, including Intel (INTC.O), opens new tab and Advanced Micro Devices (AMD.O), opens new tab, which have touted a large revenue opportunity from the inference market. COMPANY MOVES TO PROTECT POSITION The Santa Clara, California-based company has made moves to defend its position. It unveiled a new central processor and AI system built on technology from Groq - a chip startup specializing in inference - in March. The company is also spending heavily to ensure it does ⁠not hit supply-chain snags during a global memory chip crunch. Nvidia said on Wednesday that its supply rose to $119 billion in the fiscal first quarter, up from $95.2 billion the previous quarter. Nvidia reported first-quarter revenue of $81.62 billion, beating analysts' average estimate of $78.86 billion, according to data compiled by LSEG. Data center revenue in the quarter came ⁠in at $75.2 billion, compared with the average analyst estimate of $72.8 billion. On an adjusted basis, the firm earned $1.87 per share, compared with market estimates of $1.76. Nvidia also disclosed $30 billion worth of cloud computing agreements, up sequentially from $27 billion, which it said were to help its research and development efforts. Seaport ⁠analyst Jay Goldberg said in a research note last year that such commitment likely represents "backstops" in which Nvidia agrees to pay cloud computing companies that buy its hardware for excess capacity from those companies running Nvidia systems. Reporting by Zaheer Kachwala and Anhata Rooprai in Bengaluru and Stephen Nellis and Max A. Cherney in San Francisco Editing by Shinjini Ganguli and Matthew Lewis Our Standards: The Thomson Reuters Trust Principles., opens new tab * Suggested Topics: * Retail & Consumer Max A. Cherney Thomson Reuters Max A. Cherney is a correspondent for Reuters based in San Francisco, where he reports on the semiconductor industry and artificial intelligence. He joined Reuters in 2023 and has previously worked for Barron's magazine and its sister publication, MarketWatch. Cherney graduated from Trent University with a degree in history.

Nvidia Q1 results surpass Wall Street expectations thanks to massive AI chip demand

Artificial intelligence chipmaker Nvidia's quarterly results surpassed Wall Street's expectations once again, fueled by massive demand for its high-end AI chips. The company said Wednesday it earned $58.32 billion, or $2.39 per share, in the February-April period, up from $18.78 billion, or 76 cents per share, in the same period a year earlier. Excluding one-time items, Nvidia earned $1.76 per share. Revenue jumped 85% to $81.62 billion from $44.01 billion. Analysts, on average, were expecting earnings of $1.75 per share and revenue of $78.91 billion, according to a poll by FactSet. Nvidia's results have exceeded the analyst projections that shape investors' perceptions since Nvidia's high-end chips emerged as AI's best building blocks three years ago. "The buildout of AI factories -- the largest infrastructure expansion in human history -- is accelerating at extraordinary speed," said CEO Jensen Huang in a statement. For the current quarter, Nvidia forecast revenue of about $91 billion. Analysts are forecasting $87.29 billion. Shares of the Santa Clara, California-based company dipped slightly after-hours to $222.12 after closing at $223.47 in the regular trading session. As of Wednesday's close, Nvidia had a market value of $5.4 trillion.

Nvidia's Profit Hits $58.3 Billion as A.I. Boom Gathers More Steam

The chip maker said its profit in its most recent quarter jumped 211 percent from a year ago thanks to extreme demand from other big technology companies. Another huge quarterly profit announced by the chip maker Nvidia on Wednesday provided solid evidence that Silicon Valley's artificial intelligence spending spree is still gathering steam. Nvidia said that profit in its most recent quarter was $58.3 billion, up 211 percent from a year ago and topping expectations by financial analysts. Just three years ago, the Silicon Valley company's quarterly profit was $2 billion. Nvidia's chips are an essential part of big A.I. projects, and other tech companies have been lining up to spend tens of billions of dollars on those chips. Nvidia is now the most valuable publicly traded company in the world, and its financial results have become a bellwether for the rest of the tech industry. Nvidia's biggest problem appears to be meeting demand from its spendthrift tech industry customers, a strong indication that the A.I. boom is going strong. Nvidia's share price was flat in after-market trading on Wednesday. It was the second consecutive quarter Nvidia's profit had doubled, and the second time that the chip company had a bigger profit than other tech giants like Apple. Revenue for the quarter was $81.6 billion, up 85 percent from a year ago, also topping expectations. Nvidia also reassured Wall Street about its future. The company projected sales in the current quarter would nearly double from last year to $91 billion. That exceeded Wall Street's prediction for sales of $86 billion. Jensen Huang said the construction of data centers, which he calls A.I. factories, has become "the largest infrastructure expansion in human history." "Nvidia is uniquely positioned at the center of this transformation as the only platform that runs in every cloud, powers every frontier and open source model, and scales everywhere AI is produced," he said in a statement. More than a decade ago, Mr. Huang pushed his company, which made chips mostly used for video games, to develop software and chips to build A.I. His gamble helped Nvidia gain control over 90 percent of the market for the cutting-edge semiconductors that power A.I. projects. Nvidia's sales have been buoyed by tech giants' conviction that A.I. will start the next industrial revolution, and Google, Amazon, Meta, Microsoft and others have committed at least $1 trillion to A.I. data center construction. Those data centers are packed with Nvidia chips. Not surprisingly, data center sales now drive Nvidia's business. In the most recent quarter, the company said revenue from data centers rose 92 percent to $75 billion -- nearly all of its sales for the period. Mr. Huang said this week that new A.I. assistants known as agents are spurring more A.I. spending. That spending is starting to lift the entire chip industry. AMD and Intel have increased sales of traditional server chips, which can fulfill some A.I. queries. Cerebras, an A.I. chip-making start-up, went public this month. And Google, which makes custom A.I. chips known as tensor processing units, has begun selling them to rivals. "If you run an A.I. business, you'll take any chip you can get your hands on because there's way more demand than you can handle," said Daniel Pilling, a portfolio manager at Sand Capital, an investment firm. Nvidia has responded to increased competition with new products. In March, it unveiled an A.I. system with technology it licensed from a start-up called Groq. The product, which pairs Nvidia and Groq chips, more efficiently fulfills A.I. requests through a process known as inference. The chipmaker also has begun using its swelling cash reserves to buy critical components and invest in start-ups. The company spent $95 billion in the previous quarter to secure the memory, optical fiber and other supplies it needs to make its A.I. supercomputers. In February, it also invested in Anthropic, one of the fastest-growing A.I. companies in the world. Mr. Huang has said Anthropic will now begin using more Nvidia chips. But Mr. Huang hasn't been able to execute on one of his top priorities: Selling chips in China. After the Trump administration banned sales to China last year, Mr. Huang persuaded it to reverse course and allow Nvidia to sell Chinese companies its second-most powerful chip. But China has refused to let its companies buy Nvidia technology and instead pushed them to use domestic chipmakers like Huawei. This month, Mr. Huang traveled to Beijing on Air Force One with President Trump. He said he didn't raise the issue with Chinese officials. He is optimistic the situation will change. "The Chinese government has to decide, how much of their local market do they want to protect," Mr. Huang said during an appearance on Bloomberg TV on Monday. "My sense is that over time, the market will open."

Nvidia Wants to Decouple Its Reputation From Meta, Amazon, Google, and Microsoft

A handful of big tech companies operate a large, global network of massive AI data centers. Those companies, chief among them Meta, Amazon, Google, Microsoft, and Oracle, are often referred to as hyperscalers. As the top AI chipmaker, Nvidia provides the hardware for these hyperscalers, and in turn, the hyperscalers are the chip giant's biggest customers. During the rise of the AI hype era, any investment made into AI infrastructure by these hyperscalers was met with market fervor, and any good news for these hyperscalers meant good news for Nvidia. But recently that dynamic has started to change. The hyperscalers' financial commitments for the year topped $725 billion, a figure that has doubled since a year ago. Meanwhile, this AI commitment is still showing limited returns, causing investors to grow wary of this record AI spending and wonder if it is warranted or signifies a bubble. Earlier this year, Evercore analysts warned that the investment commitments could turn these tech giants' cash flow negative, a fate that, if it came to pass, would certainly hit Nvidia's profits as well. It seems to be a major reason why some experts think Nvidia's revenue growth may be peaking. This pressure may have been the driver of a major change in the way Nvidia reports its most valuable financial metric, data center revenue. In an attempt to prove revenue diversification to investors, Nvidia announced on Wednesday that, going forward, it will break down data center revenue into two: hyperscalers and "ACIE," which stands for AI Clouds, Industrial, and Enterprise, aka a catch-all category that encompasses pretty much everything else. "It's really about the fact that our business has now evolved and grown to such a large scale, it's helpful to segment it, so that you have a better understanding of how our business works," Nvidia CEO Jensen Huang said at the company's earnings call on Wednesday evening. On the call, Huang spent considerable time hammering in the point that Nvidia's revenue is not as dependent on the hyperscalers as once thought, even though in the past quarter hyperscalers accounted for half of all data center revenue. Huang assured investors that while hyperscaler growth came first and both categories will "grow incredibly fast," ultimately the second category would "be larger over time." "Hyperscale developed AI first for a lot of reasons, you know, they have great computer science, they have excellent data center capability, and they also focus largely on consumer applications, which, if not perfect, is not the end of the world," Huang said. "For many of the other applications...until the AI is very capable and does really productive work, and does it safely, and it could do it in a way that can actually generate impact and income, it doesn't really get used." Nvidia could back up those growth promises with its results so far. Hyperscaler data center revenue grew 12% quarter over quarter, while the second category grew 31%. Huang has spent the past few months trying to battle the negative investor outlook on the hyperscaler capex commitments to save Nvidia's reputation, but this new reporting method signals a change in strategy. In the previous earnings report, he spent much of the call assuring investors that the spending commitments were warranted, offering an alternative way to measure revenue by correlating it directly with compute power instead. "Compute equals revenue," Huang proclaimed at that earnings call repeatedly. But since then, his new mantra has done little to dissipate the fears, while the hyperscalers upped AI spending commitments even further in the latest round of big tech earnings in April. Now, it seems he's hopping ship and focusing on proving the revenue growth that everyone else will surely bring to Nvidia instead.

Nvidia posts record $81.6bn Q1 amid AI infrastructure boom

NVIDIA smashed revenue records in its first quarter of fiscal 2027, with sales up 85pc year-on-year to $81.6bn. The Santa Clara-based company saw its Data Centre division lead the charge, with revenue reaching $75.2bn, up 92pc from a year ago. Demand for Nvidia's AI chips from hyperscale cloud providers and enterprise AI factories showed no signs of slowing, with quarterly revenue also up 20pc. Gross margins held firm at around 75pc, and net income more than tripled to $58.3bn compared to the same period last year, reflecting Nvidia's central role in the current AI infrastructure boom. CEO Jensen Huang was typically ebullient, describing the moment as a major inflection point for the industry. "The buildout of AI factories - the largest infrastructure expansion in human history - is accelerating at extraordinary speed," he said. "Agentic AI has arrived, doing productive work, generating real value and scaling rapidly across companies and industries." "This was an extraordinary quarter," said Huang closing out yesterday's earnings call, according to CNBC. "Demand has gone parabolic. The reason is simple: Agentic AI has arrived. AI can now do productive and valuable work. Tokens are now profitable, so model makers are in a race to produce more. In the AI era, compute capacity is revenue, and profits." Looking ahead, the company guided for Q2 revenues of $91bn, pointing to continued explosive growth, although he did note Nvidia is not counting on any Data Centre revenue from China in that forecast. Not everyone is without reservation. Alvin Nguyen, senior analyst at Forrester, struck a note of caution: "At a roughly $5trn valuation, the question is no longer whether growth is strong - it's whether growth can be sustained at this level. NVIDIA's continued success creates an extraordinary level of pressure that's difficult to maintain, though the company has consistently risen to the challenge so far." Don't miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic's digest of need-to-know sci-tech news.

Data center revenue soars 92% as NVIDIA turns in another record-breaking quarter...and the share price falls

We built NVIDIA compute platform over three decades, one architecture, vast ecosystem, extreme co-design across chips, systems, networking and software. We built it ahead of this moment so that when agentic AI arrived NVIDIA would be ready. It has arrived. The AI bubble held intact yesterday as NIVIDA turned in record overall revenue of $81.6 billion for Q1 calendar 2027, with data center revenue making up $75.2 billion of that, up a massive 92% year-on-year. Net income was $58.32 billion, up from $18.78 billion in the comparable year-ago quarter. So, of course, the share price fell on Wall Street as the short termists seemingly fretted that growth going forward wouldn't match their greed... But for Jensen Huang, the glass was resolutely more than half-full: This was an extraordinary quarter, demand has gone parabolic. The reason is simple, agentic AI has arrived. AI can now do productive and valuable work. Tokens are now profitable, so model makers are in a race to produce more. In the AI era, compute capacity is revenue and profits. NVIDIA is the platform of this era. The world is re-building computing for agentic AI, he argued: The world has 1 billion users, human users; my sense is that the world is going to have billions of agents. Not today, I mean, we're going to grow into it, but we'll have billions of agents and those billions of agents will all use tools. Those tools can be like PCs, just like us humans using PCs today. In the future, you'll have an agent using a PC, so if you kind of think along the lines of in the future, you pick your favorite number of agents at the moment. At the moment, call it, a few hundred thousand, but in the future, call it, eventually a few billion. Of all the platforms in the world NVIDIA compute supports the richest diversity of demand, he argued, citing five differentiators: First, NVIDIA is the only platform that runs every Frontier AI model. With the addition of Anthropic to our existing partners, OpenAI, xAI, Meta MSL, Gemini and many others, our share of Frontier AI is growing. Second, we are in every hyperscale cloud, supporting their core data processing and machine learning workloads, internal AI services as well as supporting their demand for NVIDIA users in their public cloud services. Third, our full stack complete AI factory solution and vast global ecosystem let us uniquely address new AI data center segments, new AI cloud native clouds and sovereign AI clouds, and on-premises enterprise and industrial infrastructure. Fourth, NVIDIA CUDA extends all the way to the edge, robotics, autonomous vehicles, embedded medical instruments, AI-RAN telco base stations. The next wave is physical AI with billions of autonomous and robotic systems operating in the physical world. This is the third segment we were talking about earlier. And rounding out the top 5 things, we have a major new growth driver, Vera, the world's first CPU purpose-built for agentic AI. Vera opens a brand-new $200 billion TAM for NVIDIA, a market we have never addressed before. And every major hyperscaler and system maker is partnering with us to deploy it. Going back to those staggering data center numbers, Huang breaks this market opportunity into two camps - hyperscale and ACIE or AI clouds, industrial and enterprise: Hyperscale clouds, that would be one large segment and within that segment, there's three different ways that we operate. First way is that we help the hyperscale clouds, accelerate their data processing and machine learning workloads. We accelerate and support their AI processing inside. We also, of course, bring a lot of business, NVIDIA ecosystem business, to their public clouds. The second segment is AI natives, enterprise on-prems, industrial on-prems and that and sovereign AI. That segment is growing incredibly fast because everybody needs AI, and we're going to see AI being adopted by every industry, every country, every company. Everybody wants to build it in a different way, and the fact that we provide the entire solution makes it much easier, makes it possible at all, for people to be able to build these things. He added of the ACIE grouping: The second category is extremely diverse. Instead of five, six, or seven companies representing the revenues associated with our first category, the second category is hundreds, thousands, of companies and in the future, there will be hundreds of thousands of companies with a large number of companies with smaller installations. And that category is going to continue to grow at incredible pace... This second category is fairly poorly understood because there are just so many small companies or so many companies and each one of the installations are relatively small compared to, of course, one of the hyperscalers. The growth rates of the two categories will flip in the future, he predicted: Hyperscale developed AI first for a lot of reasons. They have great computer science, they have excellent data center capability, and they also focus largely on consumer applications, which, if not perfect, is not the end of the world...for many of the other applications, industrial applications, enterprise applications, until the AI is very capable and does really productive work and does it safely, and it could do it in a way that can actually generate impact and income, it doesn't really get used. And so you expect the second category to develop slower than hyperscale. However, long term, if you look at industrial and enterprise, clearly, that's where future economics is going to be because it represents some $50 trillion, $80 trillion of the world's economy and it's going to be larger than that because of AI. And so I expect the second category to be larger over time. I think, it's a foregone conclusion. Both are going to grow incredibly fast. I expect the second category to still grow faster, but both are going to grow incredibly fast. In Q1, hyperscale revenue of $38 billion was approximately 50% of data center revenue, up 12% quarter-on-quarter, while ACIE revenue was $37 billion, growing 31% quarter-on-quarter, including AI cloud revenue that more than tripled year-on-year. There are two primary drivers behind the accelerating build-out of AI infrastructure, he contended: First, from search and advertising to recommender systems and content understanding. The largest hyperscale workloads continue to transition from CPU to GPU-based accelerating computing. Second, the adoption of products and services native to AI is inflecting. Since the advent of ChatGPT, we have witnessed mainstream AI transition from one-shot inference to reasoning and to now agentic. AI is no longer a nice to have. AI is now a necessity for enhancing productivity across all industries and roles. An extraordinary quarter indeed, albeit one which leaves one big question in its wake - how much will it take for the Red Suspender Brigade to be happy?

Nvidia almost doubles its data center revenue as it powers to another solid earnings beat - SiliconANGLE

Nvidia almost doubles its data center revenue as it powers to another solid earnings beat Chipmaker Nvidia Corp., the world's most valuable company, crushed earnings expectations once again today, benefiting from massive demand for high-end artificial intelligence chips. The company reported first-quarter adjusted earnings of $1.87 per share, well ahead of the analyst target of $1.76 per share. Revenue for the period jumped 85% from a year ago, to $81.62 billion, surpassing the analyst's $78.86 billion consensus estimate. Nvidia's results have exceeded the analyst expectations that shape the market's perceptions ever since its high-end chips emerged as the best building blocks for AI three years ago. The company's button line has grown massively too. It reported total income of $58.32 billion at the end of the quarter, up from just $18.78 billion a year earlier. "The buildout of AI factories, the largest infrastructure expansion in human history, is accelerating at extraordinary speed," said Nvidia Chief Executive Jensen Huang (pictued). Looking to the current quarter, Nvidia said it's forecasting revenue of around $91 billion at the midpoint of its guidance range, ahead of Wall Street's forecast of $87.39 billion. The chipmaker has notably changed the way it reports its finances, splitting its business numbers into two market buckets: Data center and edge computing. The data center unit is by far the bigger of the two, generating total sales of $75.2 billion during the quarter, up 92% from the previous year. As for the new edge business, that includes sales of data processing devices for agentic and physical AI, robotics and automotive chips, plus the graphics cards for personal computers and games consoles. The group delivered sales of $6.4 billion in the quarter, up 29% from a year earlier. Prior to the AI boom, gaming has been the bigger of Nvidia's two main businesses, accounting for more than half of its total revenue during fiscal 2020, when only 27% came from data center sales. But those numbers have now flipped, with the latter segment accounting for more than 90% of the total, while gaming was less than 8%. On a conference call with analysts, Huang explained the decision to overhaul the way revenue is reported, saying that it will help analysts and investors to understand the company better. "It's the simplest way of understanding our business," Huang said. "Each one of them has different stacks in a lot of ways. They have different operating systems. They operate in a different way, and we go to market very differently in each one of them." Chief Financial Officer Colette Kress told analysts on the call that the build-out of AI factories has been accelerating, causing the value of its infrastructure to rise significantly over the last few months. The price of renting an H100 graphics processing unit has increased 20% in the year to date, while A100 cloud pricing has risen almost 15% over the same timeframe. She added that customers are continuing to generate profitable revenue streams beyond the depreciable life of their GPUs. Results from the data center business were broken down into two segments. Hyperscalers accounted for more than half of all data center sales at over $38 billion, Kress said. The other $37 billion is tied to the new AI clouds, industrial and enterprise markets segment, now known as ACIE, and saw revenue triple year over year. Huang also talked about the early demand for its next-generation rack-scale system for AI, called Vera Rubin, and promised that it will be "even more successful than Grace Blackwell," which is the company's existing system for data center installations. He said he's confident about this because the company is "growing share in inference very, very quickly," as the number of companies developing frontier models grows. He said Anthropic PBC has become a key customer this year. The Vera Rubin system is comprised of 1.3 million components, including 72 Rubin GPUs and 36 Vera central processing units. Nvidia says it delivers 10 times more performance per watt than its predecessor. Meanwhile, Kress said that Nvidia isn't satisfied with only being the world's GPU king, and also wants to become the "leading CPU supplier" as well. It's an area that's currently dominated by rivals such as Intel Corp. and Advanced Micro Devices Inc. However, the new Vera CPUs have opened a "brand new $200 billion tab" for the company, she said. "Every major hyperscale and system maker is partnering with us to get it deployed," she said, adding that she anticipates CPUs will generate around $20 billion in sales this year. Until now, Nvidia's domination of the AI market has been exclusively led by sales of its GPUs, which excel at the parallel math required to train large language models. But as agentic AI automation becomes increasingly important, CPUs are enjoying a resurgence. . Nvidia also sells a new, custom Groq language processing unit and an entire data center rack filled with those new chips, called LPX. It's a kind of application-specific integrated circuit or ASIC, which is a low-powered chip that can be programmed to power specific computing tasks. It's similar to the custom AI chips sold by Nvidia's cloud customers and rivals, including Amazon Web Services Inc. and Google Cloud. Huang said he has high hopes for the Groq chips, but told analysts it will likely remain a "niche product" for some time to come. "LPX is designed for low latency and high token rate, but its throughput is low," Huang said. "The use case for LPX is not broad." Another ASIC maker is Cerebras Systems Inc., which made a blockbuster debut on the public markets last week in what was interpreted as a clear signal that the AI market is hungry for alternatives to Nvidia's chips. Despite Nvidia's impressive results, the company's stock was more or less flat in extended trading, with many investors hoping for an even more stellar performance. Privately, a lot of market watchers are nervous about a comedown for the chipmaker following a multiyear boom that has seen its market capitalization surge from $400 billion at the end of 2022 to over $5.4 trillion today. Emarketer analyst Jacob Bourne said the inevitable earnings beat has already been priced in before the results were announced, hence the muted reaction to the report. "The lingering question is whether it can convince investors the AI buildout has durability into 2027 and 2028, especially as the narrative shifts toward inference workloads and competing silicon from Google, Amazon, AMD, and Intel," he said. Nvidia's strategy of investing across the AI supply chain is helping to entrench its position, but skeptics worry about how much of that demand is organic versus propped up by Nvidia's own balance sheet." "Time and time again, Nvidia obliterates expectations and consensus; it delivered exactly on what people wanted, especially regarding data centers," said David Wagner, of Aptus Capital Advisors. "But the market doesn't always act as you would expect after a strong report like this one." The company also announced plans to return some money to shareholders. It authorized a plan to buy back $80 billion worth of stock and increased its quarterly cash dividend to 25 cents per share from 1 cent.

NVIDIA's record Data Center revenue for fiscal Q1 2027 beat estimates as the AI boom continues

NVIDIA has announced its latest financial results for fiscal Q1 2027, beating expectations with record revenue of $81.6 billion over three months, representing an 85% increase from the same period last year. Naturally, the company's record Data Center revenue drove this growth, with this segment accounting for $75.2 billion of the overall revenue. The Data Center revenue alone is up a staggering 92% from a year ago, showcasing the seemingly insatiable appetite for all things AI and NVIDIA hardware. "The buildout of AI factories, the largest infrastructure expansion in human history, is accelerating at extraordinary speed," said Jensen Huang, founder and CEO of NVIDIA. "Agentic AI has arrived, doing productive work, generating real value and scaling rapidly across companies and industries. NVIDIA is uniquely positioned at the center of this transformation as the only platform that runs in every cloud, powers every frontier and open source model, and scales everywhere AI is produced, from hyperscale data centers to the edge." The $75.2 billion in Data Center revenue is up 21% from the previous quarter, another record-setting revenue period for the company. NVIDIA lists the new Vera Rubin platform, which includes the groundbreaking Vera CPU built for Agentic AI, as a key highlight for its Data Center business this year, including an extended partnership with Google Cloud that will power Google Gemini with Vera Rubin, Blackwell, and Blackwell Ultra systems. The latest financial results from NVIDIA are also interesting from a data perspective, as the company has changed how it segments its revenue streams, consolidating everything into two categories: Data Center and Edge Computing. The former is everything data center, from hyperscale to cloud, while the latter consolidates PC, gaming, robotics, and automotive into a single category. And with that, the new Edge Computing segment reported $6.4 billion in revenue, up 10% from the previous quarter and 29% from a year ago, NVIDIA says.

Nvidia posts record $81.6 bn quarterly revenue on AI spending boom - The Economic Times

Chip giant Nvidia on Wednesday posted record quarterly revenue of $81.6 billion, blowing past Wall Street forecasts as insatiable demand for its artificial intelligence hardware powered another blockbuster quarter. Demand for Nvidia products seems insatiable despite recurring talk on Wall Street that the AI spending spree could come to a halt.Chip giant Nvidia on Wednesday posted record quarterly revenue of $81.6 billion, blowing past Wall Street forecasts as insatiable demand for its artificial intelligence hardware powered another blockbuster quarter. The results for the first quarter of fiscal 2027, ending April 26, marked an 85 percent jump from the same period a year ago and a 20 percent rise from the prior quarter, underscoring Nvidia's status as the primary beneficiary of a global AI infrastructure buildout. Net profit surged to $58.3 billion, more than tripling from $18.8 billion in the year-earlier period. Nvidia's data center business, which sells the processors powering AI systems at tech giants and technology companies worldwide, was the engine behind the quarter's performance. Data center revenue, which includes Nvidia's key graphics processing units (GPUs), hit a record $75.2 billion, up 92 percent from a year ago. A GPU is a specialized computer chip originally designed to render video game graphics at high speed, but Nvidia has since made it the engine powering artificial intelligence. That pivot has made Nvidia the world's most valuable company, on the back of huge demand for its AI hardware. Demand for Nvidia products seems insatiable despite recurring talk on Wall Street that the AI spending spree could come to a halt. Since its February earnings report, Nvidia has disclosed a $10 billion investment in Anthropic, a major deal with Meta, and a commitment to AI company CoreWeave targeting five gigawatts of AI facilities by 2030. For the current quarter, Nvidia projected revenue of $91 billion, representing a further acceleration in growth. Crucially, Nvidia said it was not assuming any data center revenue from China in its outlook, where its core product has been caught up in a geopolitical dispute between Beijing and Washington. Nvidia boss Jensen Huang this week said he expected China to eventually open its market to high-end US chips that can train and run artificial intelligence systems. The superpowers are in a fierce race for AI supremacy, and Nvidia's H200 chip had until recently been barred from sale in China by Washington over national security concerns. However, there is no sign that Chinese tech companies are buying them, as Beijing ramps up domestic chip development in a bid to challenge US dominance in the sector. Investors shrugged off the results in the earnings report, with Nvidia shares down more than one percent in after-hours trading.

Jensen Huang: Nvidia's Two-Track Data Center Strategy Fuels Next Wave Of AI Growth

'There are 250,000 enterprise companies around the world. Many of them will have to build or want to build AI factories for themselves to operate. Many industrial companies, there's no choice but to put the computer where the context is, where the action is. You can't put that in the cloud. It has to respond reliably, quickly every single time,' says Nvidia President and CEO Jensen Huang. Nvidia's dual data center strategy is helping the company not only with current growth but is laying the foundation for continued growth into the foreseeable future, according to Nvidia President and CEO Jensen Huang. Huang Wednesday told analysts and investors during company's first fiscal quarter 2027 quarterly financial conference call Wednesday that his company has segmented its data center business into two main parts for simplicity's sake. The first segment serves the hyperscaler business, which is seeing huge growth and expecting CapEx spending of a trillion dollars this year, Huang said. [Related: Nvidia CEO Explains Why He Sees 'Something Very Different' From An AI Bubble] "I have every expectation it's going to grow from here for fundamentally good reasons," he said. "This is the way computing is going to work in the future, and if they don't have the compute, they won't have the revenues. It is very clear: compute is revenues. Compute is profit. And so the world is changing. SaaS didn't used to use as much compute, but AI requires a tremendous amount of compute. But you could do, of course, incredibly more, which is the reason why we heard about the frontier AI companies, both Anthropic and OpenAI, growing at an incredible pace. The fact that they can grow within one month what some of the SaaS companies would have taken a decade to grow tells you something." The second major segment is the AI-native clouds that Huang said are regional. "There are startups all over the world supporting those companies," he said. "There are 250,000 enterprise companies around the world. Many of them will have to build or want to build AI factories for themselves to operate. Many industrial companies, there's no choice but to put the computer where the context is, where the action is. You can't put that in the cloud. It has to respond reliably, quickly every single time. I can't imagine a chip plant being connected to a cloud service provider. It doesn't make any sense." Within that is a whole of data centers where semi-custom chips don't make sense because those data centers want to operate systems and not design and build their own systems, Huang said. "The second category is extremely diverse," he said. "Instead of five or six or seven companies representing the revenues associated with our first category, the second category is hundreds, thousands of companies, and in the future would be hundreds of thousands of companies. A large number of companies with smaller installations. And that category is going to continue to grow at incredible pace." Nvidia is fairly unique in its ability to serve this industry because of its platform which Huang said is built like it's vertically integrated, so that everything works together, or it can be disassembled so customers can build and buy it in the configuration they want. "This second category is fairly poorly understood, because there are just so many companies, and each one of the installations are relatively small compared to, of course, one of the hyperscalers," he said. "And so if you look at the segmentation and the size of each, you could see that in fact we're growing share in the hyperscalers because we now have much bigger support from Anthropic [while] very few companies have exposure into the second category." Nvidia has also become a platform on which to build neoclouds and other AI-native clouds, Huang said. "AI-native clouds don't build chips, don't design their own chips, and they don't want to," he said. "They can't really assemble unrelated parts together into an AI factory, and their tolerance for time to first token is extremely low. And their need for an architecture that has a great deal of off-take so that it runs every model, has customers from everywhere, is incredibly high, and so that's the reason why Nvidia's architecture is so perfect for them. We offer every component, and whatever we don't offer, our ecosystem of partners offers it, and it's all fully integrated. All works together." The number of customers that can rent capacity from AI-native cloud providers is incredibly high, Huang said. "Basically, every single AI builder, every AI-native startup around the world, SaaS companies, enterprise companies, industrial companies," he said. "And so our computing, our architecture, is the most rentable of any computing platform in the world. It's the most performant, it's the easiest to put together, it's the most rentable, has the best TCO, and it's the easiest to finance. All of those properties are quite unique to the needs of AI natives." Nvidia The Dominant Enterprise Infrastructure Nvidia has become the dominant new architecture inside of corporate enterprise infrastructure, but it's being delivered by the kinds of companies channel partners have all worked with for a long time, said Shawn O'Grady, chair and CEO of General Datatech, a Dallas-based solution provider and Nvidia channel partner that focuses on mission-critical infrastructures and AI. General Datatech is ranked No. 51 on CRN's 2025 Solution Provider 500. "It's being delivered by Dell and HPE and Lenovo and Cisco," O'Grady told CRN. "In storage, there are obviously new players we work with there with Nvidia like Vast Data, but our traditional storage partners like NetApp and Everpure also have architected their storage infrastructures in order to handle the massive data that's being driven by AI. We spent a lot of time with Nvidia directly, even though we sell through their OEM partners." Nvidia also works a lot on the networking side with OEM partners like Cisco, particularly in Cisco's Mass Scale Infrastructure group, which is used in the optical technology that General Datatech brings to its carrier customers, O'Grady said. "We're starting to see a need for networking of that substance, specifically in AI workloads," he said. "In a lot of ways, we're building on strengths that we've had, not having to create new stuff to monetize this opportunity. But it has required us to keep very current on new technologies. In some cases, this means developing new partner relationships like with Nvidia, but in a lot of ways, we're building on a core strength." Nvidia By The Numbers For its first fiscal quarter 2027, which ended April 26, Nvidia reported a record revenue of $81.62 billion, up 85 percent over the $44.06 billion the company reported for its first fiscal quarter 2026. That included record data center revenue of $75.2 billion, up 92 percent over last year, as well as edge computing revenue of $6.4 billion, up 29 percent. The company's data center revenue includes revenue from hyperscale, AI clouds, industrial, and enterprise. Total revenue for the quarter beat analyst expectations by $2.65 billion, according to Seeking Alpha. The company also reported GAAP net income of $58.3 million or $2.39 per share, more than double last year's $18.8 million or 76 cents per share. On a non-GAAP basis, Nvidia reported net income of $45.5 million or $1.87 per share, more than double last year's $19.1 million or 78 cents per share. Non-GAAP earnings beat analyst expectations by 10 cents per share, according to Seeking Alpha. Looking ahead, Nvidia expects to report revenue of $91.0 billion plus or minus 2 percent during its second fiscal quarter 2026 compared to last year's $46.7 billion. That number does not include possible revenue from China.

Nvidia posts record $81.6 billion quarterly revenue on AI spending boom

San Francisco, May 21, 2026 -Chip giant Nvidia on Wednesday posted record quarterly revenue of $81.6 billion, blowing past Wall Street forecasts as insatiable demand for its artificial intelligence hardware powered another blockbuster quarter. The results for the first quarter of fiscal 2027, ending April 26, marked an 85 percent jump from the same period a year ago and a 20 percent rise from the prior quarter, underscoring Nvidia's status as the primary beneficiary of a global AI infrastructure buildout.

Nvidia pivots toward a new diversification narrative

As is often the case, the American group reported results significantly above expectations for Q1 2027. The company posted quarterly revenue of $81.6bn, compared to a consensus estimate of $78.8bn. But beyond the financial performance itself, investors appear to have focused primarily on a significant shift in the group's communication. A few hours after the earnings release, Nvidia presented its traditional investor deck, in which one modification particularly caught the eye: the new revenue segmentation for the Data Center division. This business unit is now divided into two distinct sub-segments. The first groups together revenue related to hyperscalers, primarily GPU sales destined for the largest artificial intelligence infrastructures. The second, dubbed ACIE for "AI Clouds, Industrial and Enterprise," represents revenue generated by AI cloud infrastructures, industrial applications and corporate enterprises. Nvidia describes this segment as a source of "growth opportunities across diverse data centers and AI factories purpose-built for artificial intelligence across industries and nations." The two segments now carry almost equivalent weight, with $37.9bn in revenue for hyperscalers versus $37.4bn for the ACIE business over the quarter. Through this new presentation, Nvidia seeks above all to demonstrate that its growth no longer rests solely on the capital expenditures of players like Microsoft, Meta, or Alphabet. The group now intends to highlight a more balanced model, supported by more diversified clients and broader applications of artificial intelligence. This evolution was accompanied by another striking element during the conference call: Nvidia mentioned a $200bn addressable market for its Vera processor. Jensen Huang specifically emphasized that this CPU was designed to meet the growing needs associated with agentic AI. For Frank Lee, an analyst at HSBC, these announcements even "overshadowed the results themselves as well as discussions around capital allocation." He sees it as a "recognition of new challenges through the reclassification of data centers and the arrival of server processors." The analyst also points out that "Nvidia's recent deals in the optics field deserve particular attention given the growing importance of networking and the risks of supply chain tensions." The same observation was made by William Beavington, a semiconductor specialist at Jefferies, who stresses the strategic importance of Nvidia's ambitions in processors. In a recent note, he recalled that AMD and ARM had mentioned addressable markets of $120bn and $100bn, respectively, by 2030. "In its very first year of CPU commercialization, Nvidia already seems positioned to become the world's leading manufacturer in this field, applying the same strategy as in networking, where the company became larger than all its competitors combined within two years," he noted. "It is quite simply remarkable."