Nvidia Stock Guide: Data Center Revenue, AI Chips and Key Metrics Explained

Nvidia, traded as NVDA on the Nasdaq, designs the chips that sit at the center of the AI boom. Its data center business now drives around 90% of its revenue, which is why data center revenue has become the single most-watched number for the stock. Nvidia leads AI computing, but its share price also carries very high expectations.

Nvidia designs chips, systems, and software for what it calls accelerated computing. It is best known for the graphics processing unit, or GPU, a chip first built to draw video game graphics that turned out to be ideal for AI. Today its business reaches far beyond gaming, and one segment towers over the rest.

One important detail shapes everything: Nvidia is a fabless company. It designs its chips but does not manufacture the most advanced ones itself, relying instead on TSMC to build them, a relationship explained in this guide to the AI semiconductor supply chain. Nvidia's value comes from chip design, system engineering, and above all its software, rather than from owning factories.

NVDA is tied to AI because its GPUs are the chips most widely used to train and run AI models. AI work involves performing enormous numbers of calculations at the same time, and GPUs are built for exactly that parallel processing, which makes them the default hardware for machine learning and generative AI.

Demand has come from many directions at once: cloud providers building AI data centers, internet companies training their own models, enterprises adding AI features, startups building large language models, and governments investing in national AI capacity. All of it flows into one place on Nvidia's income statement, the data center segment. In its 2026 fiscal year, Nvidia reported record revenue of $215.9 billion, up 65% from the year before, with data center revenue of $193.7 billion, according to its results announcement. It had scaled that data center business by roughly 13 times since generative AI took off in early 2023.

For anyone researching Nvidia, data center revenue is the first number to understand. It captures the products sold into cloud, AI, and high-performance computing, so it works as the clearest reading of AI infrastructure demand. When it accelerates, investors read it as proof that AI spending is still strong; when it slows, the market starts to question whether the buildout is cooling.

A few details inside that number matter as much as the headline. Year-over-year growth shows how fast the business is expanding against the prior year, while sequential growth, comparing one quarter to the last, shows whether demand is still speeding up or leveling off. Customer concentration is a second thing to watch, since a large share of demand comes from a small group of hyperscale cloud buyers; in its fiscal fourth quarter, hyperscalers made up slightly over half of data center revenue. Supply is a third, since Nvidia can only sell what its partners can build. And product transitions matter, because customers sometimes pause orders while waiting for a new chip generation.

A common mistake is to think of Nvidia as simply a GPU seller. In reality it sells a full stack of hardware and software, and that breadth is a large part of why it has held its lead.

Beyond the GPUs themselves, Nvidia builds the networking that links thousands of chips into a single AI cluster, and that business has grown quickly, with data center networking revenue rising 142% in its 2026 fiscal year. It also sells complete systems rather than loose components, which raises how much each customer spends.

The deepest advantage, though, is software. CUDA is Nvidia's programming platform, and over many years developers have built their AI tools around it. Because so much AI code is already written for CUDA, moving to a rival's hardware takes real engineering effort, creating switching costs that protect Nvidia's position. This mix of chips, networking, systems, and software is why Nvidia is often called a platform rather than a component maker.

Beyond data center revenue, a handful of measures help build a fuller picture of the business.

Revenue growth and its mix come first: total growth matters, but growth led by the data center is viewed differently from growth in smaller segments. Gross margin comes next, showing how much profit Nvidia keeps on each sale before operating costs; in its 2026 fiscal year gross margin was 71.1%, high by any standard, as its filing with the US Securities and Exchange Commission confirms, though it moves with product transitions and costs. Because Nvidia's fortunes track its largest customers, its results are also closely linked to the capital budgets of Microsoft, Amazon, Alphabet, and Meta.

One forward-looking distinction is worth understanding: training versus inference. Training means building AI models, which needs huge upfront compute; inference means running those models in real applications, which can grow steadily as more people use AI tools. Early AI spending leaned heavily toward training, so whether inference demand keeps growing is one of the key questions for how durable Nvidia's growth proves to be. Traders following these demand signals can track major semiconductor names through stock futures on MEXC.

Even a strong company can see its shares fall when expectations run high, and NVDA is unusually sensitive to that gap. Several pressures are worth understanding.

The largest is a slowdown in AI infrastructure spending: if cloud providers slow their buildouts, Nvidia's data center growth would decelerate, and because so much future demand is already assumed, the reaction could be sharp. Product transitions can create short pauses as customers wait for the next chip. Competition is rising from AMD's AI accelerators and, more importantly, from the custom chips big cloud companies design in-house to reduce their reliance on Nvidia, a rivalry explored in the comparison. Export controls on advanced chips, especially those affecting China, can shrink the market Nvidia is allowed to sell into. And supply bottlenecks in manufacturing, memory, or packaging can cap shipments even when demand is strong.

Valuation ties these together. When a stock trades at a high price relative to earnings, even good results can disappoint if investors expected more, which is why Nvidia can beat expectations and still fall if guidance or margins fall short of hopes. Live pricing for Nvidia and other semiconductor names is available on the MEXC stock markets page.

Reasonable investors disagree about NVDA, and it helps to see both sides clearly. Notably, the bear case does not require believing AI will fail; it mostly argues that expectations are already very high.

The single most useful idea for weighing these views is that company quality and stock performance are not the same thing. Nvidia can remain an excellent business while its stock struggles if growth slows or the valuation contracts, and it can keep rewarding shareholders if AI demand compounds for years. Which case proves right depends largely on the durability of AI spending, a theme running through the whole TSMC stock guide and the wider chip cluster.

Google stock offers access to one of the world's dominant advertising and cloud companies, and to the AI-versus-Search debate at the center of this guide. MEXC offers two routes to that exposure:

Most of Nvidia's recent growth comes from selling AI accelerators used to train and run large AI models. As AI infrastructure spending rises, demand for Nvidia's chips tends to rise with it, which is why the stock tracks the AI theme so closely.

The data center segment is by far Nvidia's largest, at around 90% of revenue, driven by AI GPUs, networking, and systems. Gaming, once the core business, is now a much smaller share.

CUDA is Nvidia's software platform that lets developers build and run programs on its GPUs. Because so many AI tools are already built around CUDA, switching to rival hardware is difficult, which strengthens Nvidia's competitive position.

Large cloud providers such as Microsoft, Amazon, Alphabet, and Meta are among Nvidia's biggest customers, buying GPUs in bulk for AI data centers. In its fiscal fourth quarter, hyperscalers made up slightly over half of data center revenue.

Yes. Nvidia designs its chips but does not manufacture the most advanced ones itself, relying on TSMC to build them. Its strength lies in design, systems, and software rather than in owning factories.