What is Nvidia?
The dominant designer of GPUs and the full-stack AI computing platform — chips, networking, and the CUDA software that powers most of the world's AI.
- Category
- AI computing / Semiconductors
- Headquarters
- Santa Clara, California
- Founded
- 1993
- Employees
- ~42,000 (FY2026)
- FY2026 revenue
- $215.9B (+65% YoY)
- Status
- Public (NASDAQ: NVDA), ~$5T market cap
What is Nvidia?
Nvidia is the world's most valuable company — an American semiconductor and AI-computing firm whose data-center GPUs, networking, and CUDA software stack power the overwhelming majority of the world's AI training and inference. Founded in 1993 to bring 3D graphics to PCs, it reported $215.9 billion in revenue for fiscal 2026 (up 65% year over year) and became the first company ever to reach a $5 trillion market capitalization, a level it still held near $5 trillion as of mid-2026.
Nvidia started by selling graphics cards for gaming and pivoted into accelerated computing when it launched CUDA in 2006, turning GPUs into general-purpose parallel processors. When the deep-learning boom arrived, that bet made Nvidia the default platform for AI: its chips, the NVLink and Spectrum-X networking it gained partly through the 2020 Mellanox acquisition, and the CUDA software libraries together form a hard-to-displace full stack.
The business is now overwhelmingly a data-center company. In fiscal 2026 (ended January 2026), Data Center revenue reached $193.7 billion — roughly 90% of total revenue — as cloud providers AWS, Microsoft Azure, Google Cloud, Oracle, and CoreWeave, plus enterprises like SAP, Eli Lilly, and Hyundai, raced to deploy Nvidia's Blackwell systems. Gaming (GeForce) contributed about $16 billion. Demand kept accelerating into the new fiscal year: in Q1 FY2027 (reported May 2026) Nvidia posted record quarterly revenue of $81.6 billion, with Data Center alone at $75.2 billion (up 92% year over year, ~87% of revenue) and networking at a record $14.8 billion, prompting Jensen Huang to say Blackwell sales were 'off the charts' and cloud GPUs were sold out.
Nvidia holds an estimated 80%+ share of the data-center AI accelerator market and ran at roughly 71% gross margin and $120.1 billion of net income in fiscal 2026, on a workforce of about 42,000. Its biggest competitive risk is not a single rival but the rise of hyperscaler custom silicon (Google TPUs, AWS Trainium, Broadcom-designed ASICs) and AMD's data-center GPUs, alongside U.S. export restrictions that limit China sales.
What does Nvidia offer?
A full-stack AI and accelerated-computing portfolio: data-center GPUs and systems, networking, the CUDA software platform, plus gaming, professional visualization, automotive, and robotics.
- Data Center GPUs (Blackwell, Hopper)· Data Center
- DGX & HGX AI systems· Data Center
- Grace CPU / GB200 superchips· Data Center
- CUDA platform· Software
- cuDNN / TensorRT libraries· Software
- NVIDIA AI Enterprise / NIM microservices· Software
- NVLink & Spectrum-X networking· Networking
- InfiniBand (ex-Mellanox)· Networking
- GeForce RTX GPUs· Gaming
- GeForce NOW cloud gaming· Gaming
- RTX PRO / professional visualization· ProViz
- Omniverse digital twins· Simulation
- DRIVE autonomous vehicle platform· Automotive
- Jetson edge AI / Isaac robotics· Robotics
- DGX Cloud· Cloud
How does Nvidia make money?
Nvidia makes money primarily by selling data-center GPUs, complete AI systems, and networking gear to cloud providers and enterprises, with software and licensing as a growing attach. Hardware is sold per-unit (not per-seat), and the CUDA software moat keeps customers locked to Nvidia silicon.
The vast majority of revenue is hardware sold at high prices: an H100/Hopper-class data-center GPU has typically sold for roughly $25,000–$40,000 per unit, and a rack-scale GB200 NVL72 Blackwell system can run into the low millions of dollars. Customers usually buy through OEMs and cloud providers rather than directly, and a handful of large hyperscalers account for a substantial share of Data Center revenue. On top of the hardware, NVIDIA AI Enterprise software is licensed on a per-GPU subscription — about $4,500 per GPU per year, or roughly $1 per GPU-hour in the cloud — turning the installed base into a recurring software stream.
The unit economics are extraordinary. Fiscal 2026 GAAP gross margin was about 71% and net income was roughly $120.1 billion on $215.9 billion of revenue. Because CUDA and the surrounding libraries are tuned to Nvidia hardware, switching costs are high — the software is the moat that protects the hardware pricing, and it is why even cheaper rival chips struggle to displace Nvidia.
Growth is driven almost entirely by AI-infrastructure buildout: Data Center revenue grew 68% in FY2026 to $193.7 billion and then accelerated to $75.2 billion in a single quarter (Q1 FY2027, up 92% year over year), fueled by the Blackwell ramp across hyperscalers, sovereign-AI projects, and enterprises building 'AI factories.' Consumer gaming (GeForce) and automotive remain meaningful but small relative to the data-center engine, and in May 2026 Nvidia raised its dividend 25-fold and authorized fresh buybacks — a signal of how much cash the model now throws off.
Who leads Nvidia?
Co-founder Jensen Huang has been CEO since 1993. His long-tenured executive team includes CFO Colette Kress and worldwide-sales chief Jay Puri, alongside co-founder Chris Malachowsky.
- Jensen HuangFounder, President & CEOCo-founder · since 1993Set the GPU and CUDA strategy that made Nvidia the platform for modern AI; one of the longest-serving founder-CEOs in tech.
- Chris MalachowskyCo-founder, SVP & NVIDIA FellowCo-founder · since 1993Former Sun Microsystems engineer; remains an engineering leader and company elder statesman.
- Colette KressEVP & Chief Financial OfficerSince 2013Ex-Microsoft and Cisco finance leader; runs financial planning, IR, and corporate development — a key voice on Nvidia's guidance.
- Jay PuriEVP, Worldwide Field OperationsSince 2005Owns global sales, partner, and go-to-market execution — the top commercial decision-maker for enterprise and cloud accounts.
- Debora ShoquistEVP, OperationsSince 2007Runs global supply chain, manufacturing, and operations — critical given GPU supply constraints.
- Tim TeterEVP, General Counsel & SecretarySince 2017Leads legal, IP, and regulatory matters including export-control compliance.
How do you contact Nvidia's leadership?
Nvidia's verified email pattern is first-initial + last name at nvidia.com (e.g. jhuang@nvidia.com), the format used by roughly 94% of employees. The addresses below for named executives follow that verified pattern but are not individually published; for official reach use press@nvidia.com (media) or industry-analyst-relations@nvidia.com (analysts).
jhuang@nvidia.comHow much funding has Nvidia raised?
Nvidia raised only about $20 million of venture capital before going public in January 1999. It has been a public company on NASDAQ (NVDA) ever since and now carries a market capitalization of roughly $5 trillion (mid-2026) — having become, in late 2025, the first company in history to cross $5 trillion.
Nvidia's private funding was tiny by today's standards. At founding in 1993, Sequoia Capital and Sutter Hill Ventures invested $2 million at roughly a $6 million post-money valuation, after LSI Logic CEO Wilf Corrigan introduced Jensen Huang to Sequoia's Don Valentine. The two firms later added roughly $18 million more, bringing total venture funding to about $20 million before the company ever turned to public markets.
Nvidia IPO'd on January 22, 1999 at $12 per share, valuing the company at roughly $600 million — about a 100x step-up on the original 1993 valuation. That IPO is the last time Nvidia raised primary equity at scale; since then it has funded itself entirely from operating cash flow, and in May 2026 it even raised its dividend 25-fold and authorized new buybacks.
As a public company, Nvidia's 'valuation' is its market cap, which has compounded astonishingly: from ~$560 million at IPO to crossing $1 trillion in 2023, $2 trillion in early 2024, $3 trillion in mid-2024, $4 trillion in 2025, and roughly $5 trillion by late 2025–mid-2026 on the back of the AI boom. With ~$120 billion of net income in FY2026 and tens of billions in cash, the company has no need to raise external capital.
How did Nvidia get here?
From a Denny's-table startup in 1993 to the GPU, the CUDA platform, the AI inflection, and a ~$5 trillion market cap.
- 1993Founded in Sunnyvale, CaliforniaJensen Huang, Chris Malachowsky, and Curtis Priem start Nvidia with ~$40K (after agreeing the plan over coffee at a San Jose Denny's) plus $2M from Sequoia and Sutter Hill to build 3D graphics for PCs.
- 1999IPO and the first GPUNvidia goes public on NASDAQ in January at ~$600M valuation; later that year launches the GeForce 256, marketed as the world's first 'GPU.'
- 2006CUDA launchesNvidia releases CUDA, turning GPUs into general-purpose parallel processors — the foundation of its later AI dominance.
- 2020Mellanox acquisition closesNvidia buys Mellanox for ~$6.9B, adding InfiniBand and high-speed Ethernet networking critical for AI clusters.
- 2022Arm deal collapsesThe proposed ~$40B acquisition of Arm is abandoned amid regulatory opposition in the US, UK, and EU; Nvidia pivots to organic CPU (Grace) and software.
- 2023-2026AI supercycle and $1T-to-$5TThe generative-AI boom drives Data Center revenue to $193.7B in FY2026 (then $75.2B in a single quarter in Q1 FY2027); market cap crosses $1T (2023) through ~$5T (2025–26), making Nvidia the world's most valuable company and the first ever to reach $5T.
Who are Nvidia's competitors?
Nvidia's rivals span merchant chipmakers (AMD, Intel), custom-silicon enablers (Broadcom, Marvell), and the hyperscalers building their own AI chips (Google TPU, AWS Trainium).
- AMDThe most credible merchant-GPU challenger; its Instinct MI-series data-center GPUs compete on price/performance and an open (ROCm) software stack vs CUDA.
- IntelPursues AI accelerators (Gaudi) and CPUs, but trails badly in data-center AI share and software ecosystem.
- BroadcomDesigns custom AI ASICs with hyperscalers rather than selling merchant GPUs — the largest enabler of non-Nvidia AI silicon.
- Google (TPU)Builds its own Tensor Processing Units for internal use and cloud customers like Anthropic — a vertically integrated alternative to buying Nvidia.
- AWS (Trainium/Inferentia)Amazon's in-house training and inference chips reduce its dependence on Nvidia for AWS AI workloads.
- CerebrasWafer-scale AI accelerator startup targeting large-model training and fast inference as a niche Nvidia alternative.
Nvidia — frequently asked questions
