NVIDIA Corporation
Company History
NVIDIA History: Founding, Growth & Key Milestones
Founded 1993 in Sunnyvale, California, United States
When was NVIDIA founded?
NVIDIA was founded in 1993 in Sunnyvale, California, United States by Jensen Huang, Chris Malachowsky and Curtis Priem, making it 33 years old.
- Founded
- 1993
- Founding place
- Sunnyvale, California, United States
- Founders
- Jensen Huang, Chris Malachowsky and Curtis Priem
- Headquarters today
- Santa Clara, California, United States
- Milestones documented
- 12 (1993–2026)
- Current CEO
- Jensen Huang
- Acquisitions
- 9
| Year | Event |
|---|---|
| 1993 | Founding in Santa Clara |
| 1999 | GPU Category Defined |
| 2006 | CUDA Opens GPUs to General Computing |
| 2012 | AlexNet Shows GPU Value for Deep Learning |
| 2016 | DGX-1 AI Supercomputer |
| 2018 | RTX Brings Real-Time Ray Tracing to Graphics |
| 2020 | Mellanox Acquisition Adds Networking |
| 2022 | Arm Acquisition Terminated |
| 2022 | Hopper H100 Announced |
| 2023 | Grace CPU Enters Data Center Roadmap |
| 2024 | Blackwell Architecture Launched |
| 2026 | $215.9B FY2026 Revenue |
- Where was NVIDIA founded?
- NVIDIA was founded in Sunnyvale, California, United States in 1993. en.wikipedia.org
- Who founded NVIDIA?
- NVIDIA was founded by Jensen Huang, Chris Malachowsky and Curtis Priem.
- When did NVIDIA start?
- NVIDIA was founded in 1993.
- Where is NVIDIA headquartered?
- NVIDIA is headquartered in Santa Clara, California, United States.
- How old is NVIDIA?
- NVIDIA is 33 years old as of 2026.
Founding Story
In 1993, Jensen Huang, a young engineer, was sitting in a Denny's diner in California eating a Grand Slam breakfast with two friends. They were looking at the large, highly pixelated, terrible graphics of early video games. Standard computer chips processed data one piece at a time. They sketched out a highly radical idea on a napkin: a chip that could process thousands of tiny mathematical equations simultaneously. They built this 'GPU' to make video games look better. Thirty years later, they realized that 'simultaneous math' was the exact architecture required to make a computer think like a human.
Founders
Jensen Huang
Nvidia possesses a gritty, prescient founding story, rooted entirely in a specific, technical conversation at a well-known American diner, and driven by a CEO who possessed an unshakable conviction in an unproven computational theory. The company was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem. The famous, foundational origin occurred at a Denny's diner in San Jose, California (where Huang had actually worked as a young, struggling busboy years earlier). At the time, the PC revolution was exploding, heavily dominated by Intel's powerful Central Processing Units (CPUs), which processed complex tasks sequentially. However, the three founders possessed a specific 'eureka' moment: they believed the future of computing would require amounts of fast, repetitive calculations to render complex graphics. They founded Nvidia specifically to pioneer a radical new architecture: the GPU. Their foundational key move was resilience. Their very first chip (the NV1) was a commercial failure, nearly bankrupting the company. However, they pivoted, heavily focusing on 3D graphics for the exploding PC gaming market (eventually launching the successful GeForce 256 in 1999). The defining clever of Jensen Huang occurred in 2006. He mandated the expensive creation of CUDA (a software platform that allowed developers to use GPUs for general-purpose computing). For over a decade, Wall Street hated this investment, deeming it wasteful. However, when the AI revolution finally arrived, CUDA ensured Nvidia had a ten-year head start, transforming a niche gaming company into the foundational bedrock of the modern digital future. Jensen Huang was born in 1963 in Tainan. Education: Aloha High School.
Chris Malachowsky
Chris Malachowsky co-founded NVIDIA in 1993 and became a central figure in the company's technical foundation. His contribution was especially important during the period when NVIDIA had to recover from early product misalignment and compete against companies such as 3dfx, ATI, S3, and Matrox. He helped shape the engineering discipline behind NVIDIA's graphics processors and supported the company's transition from consumer graphics into broader accelerated computing. Over time, Malachowsky moved into senior technology and advisory roles, remaining part of NVIDIA's long-term technical culture rather than becoming a public-facing CEO figure. His influence shows up in the company's engineering bias: product cadence, platform thinking, and respect for developer ecosystems. He also helped maintain continuity between the original graphics mission and the later CUDA/data-center strategy. While Jensen Huang became the public strategist, Malachowsky helped make the company technically credible enough for that strategy to survive multiple industry cycles. His legacy is quiet but durable: NVIDIA still behaves like engineering execution is a strategic asset. Chris Malachowsky was born in 1959. Education: University of Florida; Santa Clara University; Ocean Township High School.
Curtis Priem
Curtis Priem co-founded NVIDIA in 1993 and served as one of the company's original architects. His role was especially important in the early years, when NVIDIA was trying to define a path through a crowded graphics market and recover from architectural choices that did not align with emerging standards. Priem helped establish the company's technical ambition around specialized processors for visual computing. He later stepped back from day-to-day executive prominence, but his influence remained in NVIDIA's willingness to pursue hard architecture problems rather than commodity graphics. Priem's lasting contribution is the idea that graphics chips could become a serious computing platform. That belief looked narrow in the 1990s, when the market was mostly gaming and multimedia, but it became central to the company's later move into CUDA, scientific computing, and AI infrastructure. His founder legacy is visible whenever NVIDIA treats rendering, simulation, and AI as variations of parallel computation. He also represents the less-public technical founding layer behind NVIDIA's later market mythology. Curtis Priem was born in 1958. Education: Rensselaer Polytechnic Institute.
Key Milestones
Founding in Santa Clara
Jensen Huang, Chris Malachowsky, and Curtis Priem founded NVIDIA on April 5, 1993, with a focus on 3D graphics for gaming and multimedia. The founding set the company on a parallel-processing path long before AI infrastructure became the main market.
Source: nvidia.comGPU Category Defined
NVIDIA introduced the graphics processing unit concept in 1999. The milestone mattered because it framed graphics hardware as a specialized processor, a technical idea that later carried into accelerated computing and AI workloads.
Source: nvidia.comCUDA Opens GPUs to General Computing
CUDA opened GPU parallel processing to scientists, researchers, and developers outside graphics. It became the software foundation that made NVIDIA hardware useful for machine learning, simulation, and other compute-heavy workloads.
Source: nvidia.comAlexNet Shows GPU Value for Deep Learning
The AlexNet breakthrough used NVIDIA GPUs to win the ImageNet competition. The event helped convince researchers and engineers that GPUs could accelerate neural-network training at a scale CPUs could not match.
Source: nvidia.comDGX-1 AI Supercomputer
Nvidia introduced the DGX-1 deep-learning supercomputer in 2016, its first system built specifically for artificial intelligence.
Source: en.wikipedia.orgRTX Brings Real-Time Ray Tracing to Graphics
NVIDIA RTX brought real-time ray tracing into the company's graphics roadmap. The product line kept gaming and professional visualization tied to AI-enhanced rendering while Data Center was becoming more important.
Source: nvidia.comMellanox Acquisition Adds Networking
NVIDIA completed the Mellanox acquisition for about $7 billion. The deal mattered because large AI and high-performance-computing clusters depend on fast networking between accelerators, not only on individual GPU speed.
Source: nvidianews.nvidia.comArm Acquisition Terminated
NVIDIA and SoftBank terminated the proposed Arm acquisition because of significant regulatory challenges. The failed deal showed how closely governments watch control points in semiconductor architecture.
Source: nvidianews.nvidia.comHopper H100 Announced
Nvidia announced the Hopper-architecture H100 data-center GPU in March 2022.
Source: en.wikipedia.orgGrace CPU Enters Data Center Roadmap
NVIDIA introduced its first data-center CPU, Grace, for large-scale AI and high-performance computing. The milestone expanded the platform from GPUs and networking into CPU-GPU system design.
Source: sec.govBlackwell Architecture Launched
NVIDIA launched Blackwell as a data-center-scale architecture combining GPUs, CPUs, DPUs, interconnects, switch chips, systems, and networking adapters. The architecture became central to FY2026 Data Center demand.
Source: sec.gov$215.9B FY2026 Revenue
NVIDIA reported $215.9 billion of FY2026 revenue, $193.7 billion of Data Center revenue, and $120.1 billion of net income. The result showed how far AI infrastructure had moved the company beyond its original PC graphics market.
Source: investor.nvidia.comMajor Acquisitions
Mellanox Technologies
2020NVIDIA acquired Mellanox to add high-speed networking, InfiniBand, Ethernet, switches, adapters, and data-center interconnect expertise to its GPU business. The goal was to control more of the AI and high-performance computing cluster stack.
3dfx Interactive assets
2000NVIDIA acquired core assets of 3dfx Interactive, including patents and engineering talent, after the Voodoo graphics pioneer collapsed. The purpose was to remove a rival, resolve litigation, and strengthen NVIDIA's graphics intellectual property.
AGEIA Technologies
2008NVIDIA acquired AGEIA to bring PhysX physics simulation technology into its GPU ecosystem. The goal was to make gaming and simulation more realistic while giving developers another reason to improve for NVIDIA hardware.
Icera
2011NVIDIA acquired Icera to strengthen its mobile modem and baseband capabilities as it tried to compete in smartphone and tablet processors through Tegra. The deal was meant to fill a connectivity gap against Qualcomm and integrated mobile rivals.
Cumulus Networks
2020NVIDIA acquired Cumulus Networks to add open networking software to its data-center portfolio after the Mellanox deal. The goal was to strengthen software-defined networking for modern cloud and AI infrastructure.
Excelero
2022NVIDIA acquired Excelero to improve high-performance block storage technology for large-scale enterprise and AI workloads. The goal was to reduce bottlenecks around data movement and storage access in accelerated computing environments.
Run:ai
2024NVIDIA acquired Run:ai to improve orchestration, scheduling, and utilization of AI infrastructure. The software helps teams allocate expensive GPU resources more efficiently across users, models, and workloads.
Groq (technology license and team)
2025In December 2025 NVIDIA agreed to a non-exclusive license of Groq's inference chip technology and hired key Groq leaders, including founder Jonathan Ross. Press reports put the deal at about $20B in cash. It was structured so that Groq continued as an independent company.
SchedMD
2025NVIDIA Corporation acquired SchedMD in 2025.
Keep researching NVIDIA
NVIDIA Company History FAQ
What are the historic milestones that define the rise of NVIDIA Corporation?
Key milestones include: founding at Denny's (1993), launch of RIVA 128 (1997), IPO on NASDAQ (1999), invention of the GPU with GeForce 256 (1999), acquisition of 3dfx (2000), launch of CUDA (2006), deep learning breakthrough with AlexNet (2012), delivery of first DGX-1 supercomputer to OpenAI (2016), launch of RTX real-time ray tracing (2018), acquisition of Mellanox (2020), Hopper H100 AI revolution (2022-2023), Blackwell B200 debut (2024), and reaching $3.5T market cap (2024).
How did the 2012 AlexNet moment ignite the modern Deep Learning revolution on NVIDIA GPUs?
In 2012, University of Toronto researchers Alex Krizhevsky and Ilya Sutskever trained the 'AlexNet' convolutional neural network on two NVIDIA GeForce GTX 580 GPUs, heavy all rivals in the ImageNet computer vision competition and proving to Jensen Huang that GPUs were the ultimate computing engine for deep learning.
How did Jensen Huang personally deliver the world's first AI supercomputer to OpenAI in 2016?
In August 2016, Jensen Huang personally hand-delivered the very first NVIDIA DGX-1 AI supercomputer (signed: 'To Elon and the OpenAI team! To the future of computing and humanity!') to OpenAI researchers in San Francisco, providing the computational horsepower that later trained GPT-1, GPT-2, GPT-3, and ChatGPT.
When did NVIDIA introduce Real-Time Ray Tracing with GeForce RTX in 2018?
In August 2018 at Gamescom, Jensen Huang unveiled the GeForce RTX 2080 based on the Turing architecture, introducing dedicated RT Cores and Tensor Cores that achieved the long-sought goal of computer graphics: real-time cinematic ray tracing and AI neural rendering via DLSS.
How did the launch of ChatGPT in November 2022 trigger the global generative AI explosion?
The public release of ChatGPT in late 2022 demonstrated the transformative power of Large Language Models to the world, sparking a global multi-billion-dollar arms race among tech giants (Microsoft, Google, Meta, Amazon) to acquire tens of thousands of NVIDIA H100 GPUs, driving NVIDIA's revenues into the stratosphere.
What was the unveiling of the Blackwell architecture at GTC 2024?
In March 2024 at the SAP Center in San Jose ('AI Woodstock'), Jensen Huang unveiled the Blackwell B200 GPU and GB200 NVL72 liquid-cooled supercomputer, calling it 'the engine of the new industrial revolution' to train multi-trillion parameter generative AI models.
How did NVIDIA join the Dow Jones Industrial Average in November 2024?
In November 2024, S&P Dow Jones Indices officially added NVIDIA Corporation to the blue-chip Dow Jones Industrial Average (DJIA), replacing rival Intel Corporation and symbolizing the historic transition of the global computing industry from legacy CPUs to accelerated AI computing.
What is NVIDIA's long-term vision for the era of Physical AI and Sovereign Intelligence?
NVIDIA envisions building the computing foundation for the next industrial revolution: powering a world of billions of autonomous AI agents, humanoid robotics (Physical AI), digitized industrial factories (Omniverse), and sovereign intelligence systems that accelerate scientific discovery and human flourishing.
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