NVIDIA history from 1993 founding to the CUDA AI revolution

The 1993 foundation and the gaming driver

Jensen Huang, Chris Malachowsky, and Curtis Priem founded NVIDIA on April 5, 1993, with $40,000 in the bank. Huang previously worked as a director at LSI Logic, while Malachowsky and Priem came from Sun Microsystems. They believed accelerated computing could solve problems general-purpose computing could not. The company received $20 million in venture capital from Sequoia Capital to fund research. The first product, the NV1, launched in 1995 and sold poorly, nearly ending the business. This failure occurred because the NV1 used curved quadrilaterals while Microsoft’s Direct3D standard used triangles. The RIVA 128 of 1997 saved the company, selling one million units in its first four months. This chip embraced the Direct3D standard and combined fast 2D and 3D capabilities. In 1999, NVIDIA released the GeForce 256, which they marketed as the world’s first GPU. This device moved transform and lighting work from the CPU to the graphics chip.

CUDA and the shift to parallel computing

In 2004, NVIDIA hired Ian Buck and paired him with John Nickolls to transform the Brook language into CUDA. They released CUDA in 2006 with the GeForce 8800 GTX. This software allows developers to use C-style code to run tasks across thousands of GPU cores. The programming model organizes computation into threads, blocks, and grids. Within a warp, 32 threads execute the same instruction in lockstep. To solve the memory wall, CUDA uses shared memory as a fast scratchpad for threads, which provides higher bandwidth than texture lookups. Programmers must also use memory coalescing to ensure that 32 threads in a warp access contiguous memory locations simultaneously. By pairing the software capabilities of CUDA with massive parallel hardware, NVIDIA transitioned from a company that merely rendered pixels for gamers into an industrial provider of the compute necessary for modern AI training. You already know NVIDIA dominates, but the specific shift from pixels to parallel math defined the company. In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton trained AlexNet on two NVIDIA GeForce GTX 580 cards. This training demolished the ImageNet leaderboard with a top-5 error rate of 15.3 percent, far beating the runner-up’s 26.2 percent. The company built a deep software moat through libraries like cuDNN, TensorRT, and NCCL. These tools ensure that most AI frameworks run best on NVIDIA hardware first.

The trillion-dollar AI era

NVIDIA reached a market value of $5.5 trillion in May 2026. The company reported $215.9 billion in revenue for the fiscal year ending January 25, 2026, which was a 65 percent increase. Data center revenue hit $193.7 billion, which accounts for 90 percent of the total revenue. This growth follows NVIDIA’s 2023 milestone as the first chip company to exceed a $1 trillion market value. The data center business overtook gaming revenue in fiscal 2023, when it reached $15.0 billion against $9.1 billion for gaming. The Rubin platform entered full production on May 31, 2026, using the Vera CPU and Rubin GPU. This rack-scale system uses the NVLink 6 switch to connect 72 Rubin GPUs and 36 Vera CPUs. While the H100 chip became a massive success in 2023, the Blackwell architecture in 2024 introduced more transistors for handling transformer models. AMD’s MI300X holds a 32 percent theoretical TFLOPS advantage over the H100, yet the H100 leads by 46 percent in multi-GPU training workloads.

NVIDIA’s growth stems from a diverse product portfolio. They produce the GeForce series for gamers and the Quadro/RTX series for professionals. For data centers, they provide the Tesla series, while the Tegra series supports mobile and automotive needs. The company also provides DGX systems and Jetson modules for edge AI. This business relies on the scale of the Magnificent Seven, which reached a combined market cap of $21.5 trillion in mid-November. Will the industry continue to rely on a single vendor for its most expensive training clusters?