NVIDIA was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem in a Denny's restaurant in San Jose, California. Huang, who had been a chip designer at LSI Logic and a microprocessor engineer at AMD, became CEO and has run the company continuously for over three decades — one of the longest tenures in tech. For most of its history, NVIDIA was a graphics card company. They invented the GPU in 1999 with the GeForce 256, dominated PC gaming through the 2000s, and built a steady business selling to gamers and professional visualization users. The AI pivot was not an accident — it was the result of a bet Huang made starting around 2006, when NVIDIA released CUDA, a programming framework that let researchers use GPUs for general-purpose parallel computing. At the time, almost nobody cared. A decade later, it turned out to be the most consequential strategic decision in the history of computing.
The deep learning revolution of the 2010s ran on NVIDIA hardware. When Alex Krizhevsky won the ImageNet competition in 2012 using a neural network trained on two GTX 580 GPUs, it was not because GPUs were designed for AI — it was because their massively parallel architecture happened to be perfect for the matrix multiplications that neural networks require. NVIDIA recognized this faster than anyone and began designing chips specifically for AI workloads. The Tesla (later renamed to avoid confusion with the car company), Volta, Ampere, Hopper, and Blackwell GPU architectures each brought massive improvements in AI training and inference performance. The H100, released in 2023, became the most sought-after chip in the world, with hyperscalers and AI labs spending billions to secure allocations. The subsequent H200 and B200 (Blackwell) pushed performance further, with the GB200 NVL72 server rack designed as a complete AI supercomputer. By 2025, NVIDIA was selling data center GPUs faster than they could make them.
NVIDIA's dominance is not just about hardware — it is about the software ecosystem that makes switching costs astronomical. CUDA has become the de facto standard for GPU programming, with millions of developers, thousands of libraries, and every major AI framework (PyTorch, TensorFlow, JAX) deeply optimized for it. TensorRT for inference optimization, cuDNN for deep learning primitives, NCCL for multi-GPU communication, Triton Inference Server for deployment — NVIDIA provides the entire stack from silicon to software. Competitors like AMD (with ROCm) and Intel (with oneAPI) have tried to offer alternatives, but the ecosystem gap remains enormous. When a researcher writes CUDA code, they are writing code that only runs on NVIDIA hardware, and the cumulative weight of a decade of CUDA-optimized libraries, tutorials, and tooling creates a moat that no amount of competitive silicon can easily cross.
NVIDIA's market capitalization crossed $1 trillion in May 2023, $2 trillion in February 2024, and briefly exceeded $3 trillion in June 2024, making it the most valuable company in the world. The stock price increase reflected a genuine explosion in demand — data center revenue grew from $3.6 billion in fiscal Q4 2023 to $18.4 billion in fiscal Q4 2024, a roughly 5x increase in a single year, driven almost entirely by AI training and inference demand. Jensen Huang became one of the wealthiest people on the planet. The speed of NVIDIA's ascent was unprecedented for a company of its size, and it reshaped the semiconductor industry, with TSMC (which fabricates NVIDIA's chips) struggling to keep up with demand and nations treating GPU access as a matter of national security.
NVIDIA has steadily expanded beyond selling GPUs into selling complete AI platforms. DGX systems are turnkey AI supercomputers. NVIDIA AI Enterprise is a software suite for deploying AI in production. Omniverse is a platform for building digital twins and 3D simulations. NIM (NVIDIA Inference Microservices) packages optimized AI models as deployable containers. The company has also pushed into networking with its acquisition of Mellanox ($6.9 billion in 2020), giving it control over the InfiniBand interconnects that link GPUs together in data centers. The Blackwell architecture introduced NVLink networking that can connect up to 576 GPUs as a single system. Every one of these moves is designed to ensure that as AI infrastructure scales from individual GPUs to warehouse-scale compute, NVIDIA provides not just the chips but the entire stack — making them as close to indispensable as any company in the technology industry has ever been.