Nvidia makes the chips that power artificial intelligence. A 30-year bet on parallel computing turned a gaming graphics company into the most valuable semiconductor company in history.
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Nvidia was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem in a Denny's booth in East San Jose, California. Huang — who has led the company continuously since founding — was 30 years old. The early business was graphics chips for gaming PCs. The GeForce 256 (1999) introduced hardware transform and lighting, establishing Nvidia as the performance leader in graphics processing. But gaming was always the visible product, not the ultimate destination.
The strategic pivot that defined Nvidia's future came in 2006 with the launch of CUDA — Compute Unified Device Architecture — a programming framework that allowed developers to use Nvidia's GPUs for general-purpose computation beyond graphics rendering. Graphics processors are architecturally different from CPUs: where a CPU has a small number of powerful cores optimized for sequential processing, a GPU has thousands of smaller cores optimized for parallel computation. For problems that can be broken into thousands of simultaneous calculations — scientific simulation, machine learning, financial modeling — GPUs are dramatically faster than CPUs. CUDA made Nvidia's hardware accessible to researchers and engineers who had never thought about graphics cards.
The deep learning revolution that transformed AI beginning around 2012 ran on Nvidia GPUs. When researchers at the University of Toronto trained AlexNet — the neural network that won the ImageNet competition by a decisive margin — they did it on two Nvidia GTX 580 gaming cards. The training that would have taken weeks on CPUs took days on GPUs. The entire field noticed simultaneously.
What followed was a decade of compounding advantage. AI researchers built their tools and frameworks (TensorFlow, PyTorch) around CUDA and Nvidia's hardware. Data centers investing in AI infrastructure defaulted to Nvidia. The software ecosystem — libraries, frameworks, trained engineers — all assumed Nvidia's architecture. This created switching costs that go far beyond the hardware itself: moving to a competitor's GPU requires rewriting software, retraining engineers, and rebuilding workflows that have been optimized around Nvidia's tools for years.
The November 2022 launch of ChatGPT created demand for AI compute that the industry had no framework to anticipate. Training large language models requires extraordinary amounts of parallel computation — exactly what Nvidia's latest data center GPUs, particularly the H100 (launched 2022) and subsequent H200, are designed to provide. The H100 became the most sought-after piece of hardware in the technology industry almost overnight.
At peak scarcity in 2023, H100s were trading on secondary markets for $40,000+ each, compared to a list price of around $25,000-30,000. Cloud providers (AWS, Microsoft Azure, Google Cloud) were placing orders for tens of thousands of units. AI startups were raising capital specifically to secure GPU allocation. Nvidia's data center revenue, which had been growing steadily, accelerated to a trajectory that shocked even optimistic analysts — quarterly data center revenue went from ~$3.8 billion in early 2023 to over $22 billion by late 2023.
Nvidia's financial trajectory from 2022 to 2024 is without modern precedent for a company of its size. Annual revenue grew from approximately $27 billion in fiscal 2023 to over $60 billion in fiscal 2024, with guidance for further acceleration. Net income margins expanded to over 50% as data center GPU demand overwhelmed supply at premium prices. Market capitalization grew from approximately $300 billion in early 2023 to over $3 trillion by mid-2024 — adding more than $2.5 trillion in market value in roughly 18 months.
Jensen Huang's net worth grew proportionally, making him one of the wealthiest people in the world. His leather jacket — a consistent personal brand choice across decades of public appearances — became one of the most recognized items of clothing in technology.
Nvidia's current competitive position rests on three reinforcing pillars: hardware performance, the CUDA software ecosystem, and Jensen Huang's strategic vision. AMD produces competing GPUs (the MI300 series) with competitive performance specifications, but the CUDA ecosystem advantage is significant — most AI frameworks are optimized for Nvidia first. Intel is attempting to build a competitive GPU business. Google's TPUs (Tensor Processing Units) are custom AI accelerators that power Google's own infrastructure but are not available as standalone products in the same way.
The most significant long-term threats to Nvidia's position may be internal to its own customers. Microsoft, Google, Amazon, and Meta are all developing custom AI chips designed to reduce their dependence on Nvidia. Apple has demonstrated with its own silicon that custom chip design can outperform general-purpose alternatives for specific workloads. If the hyperscalers successfully internalize chip design, Nvidia's addressable market for its most profitable products could contract — not immediately, but over a 5-10 year horizon. See value vs growth investing and how to read a company's financials.
Market capitalization: approximately $5.09 trillion; fiscal-2026 revenue: $215.9 billion, up 65%; trailing P/E: approximately 27–32x; latest reported fiscal-year gross margin: approximately 75%. These figures reflect market data available on September 14, 2026.
This snapshot is informational, not investment advice. Market prices and valuation multiples change continuously; readers should check the company’s latest filing and current quote before making decisions.