health strategy institute (hsi)
management and technology review
for in-person health, digital health
and hybrid health strategy
Joaquim Cardoso MSc.
Senior Research and Strategy Officer (CRSO),
Chief Editor and Senior Advisor
January 19, 2023
Nvidia, once known for designing graphics accelerator chips for PCs, has evolved into a trillion-dollar company by expanding its reach into various industries.
This transformation was fueled by the rising importance of specialty chips in business and the accelerating interest in generative AI.
The company’s journey began with the realization that its GPUs could be used for more than just gaming.
In 2006, Nvidia introduced CUDA, a programming architecture that simplified the use of GPUs for parallel computing, leading to applications in fields like oil and gas exploration.
The market for general-purpose GPUs expanded in 2009 with the release of OpenCL, making Nvidia’s technology more accessible to enterprises.
In recent years, the demand for Nvidia’s chips has been driven by AI and cryptocurrency mining.
The need for massive calculations to train machine learning models, including OpenAI’s GPT-4, has been a significant driver.
Additionally, the energy-efficient nature of GPUs made them ideal for cryptocurrency mining, creating a surge in demand.
Beyond AI and gaming, Nvidia tapped into the manufacturing industry with its Omniverse software platform — a metaverse used for creating and viewing digital twins of products and production lines in real-time.
This technology found applications in marketing and design collaborations.
To stay in the trillion-dollar club, Nvidia continues to expand its offerings.
It supplies GPUs for PCs, gaming consoles, server manufacturers, hyperscalers, and supercomputers.
The company is also making chips for self-driving cars and providing cloud infrastructure services for various industries.
In addition to hardware, Nvidia is a software vendor, developing libraries of code to accelerate calculations on its hardware, along with specific tools for semiconductor manufacturing optimization.
