We're raising our price target on Nvidia after another knockout quarter and guide

Nvidia reported another blockbuster quarter on Wednesday and issued guidance well above analyst forecasts. The results and conference call reinforced our belief that Nvidia is an essential stock to own during the race to build the best and most profitable AI data centers. Revenue in the company's fiscal 2027 first quarter increased 85% year over year to $81.62 billion, outpacing the $78.89 billion consensus, according to estimates compiled by data provider LSEG. Adjusted earnings per share (EPS) increased 140% to $1.98, also exceeding the consensus estimate of $1.76, per LSEG data. Shares fell a little more than 1% in after-hours trading despite the beat and strong guide. If the move late Wednesday holds throughout Thursday's regular session, it would not tell the full Nvidia story. The stock hasn't traded higher in the session after reporting earnings in a year, since the April quarter of 2025. Still, the stock is up more than 60% over this time period. For 2026, shares are up roughly 20% based on Wednesday's closing price. NVDA 1Y mountain Nvidia's stock performance over the past 12 months. Bottom line It was another impressive quarter from Nvidia. Revenue beat analyst estimates by almost $3 billion, and its revenue guidance for the current quarter is roughly $4 billion above the consensus. The company keeps posting unprecedented growth figures for its size despite having zero computer revenue contribution from China, a region where sales are not expected any time soon. "Demand has gone parabolic," CEO Jensen Huang said on the earnings call, thanks to the rise of new artificial intelligence systems capable of taking actions on behalf of users without human intervention. Known as agentic AI, these systems produce a lot of tokens, which are the basic unit of data in AI computing. "Tokens are now profitable, so model makers are in a race to produce more. In the AI era, compute capacity is revenue and profits," Huang said. AI runs on Nvidia's platform, and the company's quarterly figures continue to blow past what everyone anticipates due to huge sums of money invested annually to support AI infrastructure buildouts. A lot of the money is coming from the so-called hyperscalers — Alphabet, Microsoft, Meta and Amazon — that operate vast networks of data centers. But they are not the only source. During the earnings call, CFO Collete Kress said that with analysts forecasting hyperscaler capital expenditures to exceed $1 trillion in 2027, AI infrastructure spending is on track to reach $3 trillion to $4 trillion annually by the end of the decade. Huang said he believes Nvidia should be growing faster than hyperscale capex because of the level of demand he is seeing from other sources — namely, AI native companies, individual enterprises operating on-premise data centers, and sovereign AI, a broad term used to describe nations investing in their own AI infrastructure. Nvidia's revenue is currently split roughly 50-50 between the hyperscale camp and these other sources (more on that later). "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," he explained on the call. "And so everybody wants to build it in a different way. And the fact that we provide the entire solution, it makes it much easier, it makes it possible at all for people to be able to build these things." Why we own it Nvidia's graphics processing units (GPUs) are the key driver behind the AI revolution, powering the accelerated data centers being rapidly built around the world. The company's chips, including central processing units (CPUs), are part of a platform that includes the hardware and software needed to power AI workloads. Nvidia is also fast-becoming a big investor in AI-related companies and technology. Competitors: AMD, Intel, Broadcom and custom AI chips Most recent buy: Aug 31, 2022 Initiation: March 2019 While Nvidia is best known for its graphics processing units (GPUs) that dominate training for AI models, the rise of agentic AI has made central processing units (CPUs) relevant again and important part to the chip story. This resurgence is the crux of our thesis in Arm Holdings, which is also a longtime collaborator with Nvidia. In March, Nvidia announced the launch of its new Vera CPU, a successor to the Grace CPU that was purpose bult for agentic AI. Kress said on the earnings call that every major hyperscale is working with Nvidia to get these Arm-based chips deployed, and the company has visibility of nearly $20 billion in total CPU revenue this year. This makes Nvidia one of the largest data center CPU providers in the world. For context, analysts expect rival AMD's data center business to about $31 billion this year when including sales of both GPUs and CPUs, according to FactSet. Intel's data center and AI business, which is primarily CPUs, is projected to be about $22.5 billion. The main takeaway is that AI spending isn't