Be it its CEO Jensen Huang’s “rockstar-ish” last moment hitchhike with President Trump’s entourage to China, or the soaring stock prices and a market cap hovering near $5 trillion or the fact that the ongoing International Conference on Machine Learning (ICML) 2026 has about 2000 accepted papers citing its GPUs, NVIDIA has clearly dominated the markets, academia and headlines.

So how did Huang and NVIDIA manage to become so ubiquitous and carve out a commanding position across much of the AI stack?

To really grasp NVIDIA’s sharp ascent, we need to understand how AI models are built and the infrastructure and tools they’re built using.

What are AI Models, Their Training and Infrastructure ?

AI models are essentially machine learning algorithms – be it neural networks or some other type – that have been trained on ginormous datasets, and post training, they are phenomenal at pattern recognition and prediction. They generate text, audio, video, process data and offer actionable insights from them.

Now because the datasets that are used to train AI models are so huge in size, normal CPUs ( Central Processing Units ) having highly capable but a limited number of cores- becomes highly time inefficient and almost impractical for AI models to be trained upon.

The training of AI models has always been very suitable to another piece of technology which Huang and co have been developing and perfecting / improving upon over years at NVIDIA , the Graphics Processing Unit (GPU) .

How are GPUs different from CPUs?

The GPU as its name suggests was hardware originally built to render graphics , primarily for video games. GPU’s unlike their cousin CPU’s have far many cores. Albeit less powerful , these cores can parallelly perform tasks and execute operations – making the GPU much more time efficient and perfectly suitable for training AI models which essentially entails the repetition of the same operation a great many times , the perfect case for parallelization and thus the ideal workload for a GPU.

Founded in 1993, NVIDIA witnessed its first major success when it released GeForce 256 in 1999 which was at the time marketed as the world’s first true GPU.

Following this NVIDIA also secured the contract to build the graphics hardware for Microsoft’s original Xbox. While GPUs were originally built to render graphics , primarily for video games, in 2006 NVIDIA’s decision to launch CUDA (Compute Unified Device Architecture ) completely shifted the paradigm for computing .

What is CUDA?

CUDA is NVIDIA’s platform for accelerated computing and provides the software layer that enables applications to harness the power of GPUs. With CUDA , NVIDIA opened the gates to the use of GPUs for highly intensive parallel processing tasks . Prior to the release of CUDA , only through tedious and cumbersome techniques could GPUs be applied to general purpose computing.

In the years that followed CUDA was being used in a number of areas and Machine learning was one of them.
CUDA had a tremendous impact on research as researchers across fields leveraged CUDA enabled GPU processing to tackle problems as diverse as molecular studies and cryptography, that only parallel processing could potentially solve.

Alex Net: The Big Bang of Modern AI

2012 proved to be the point of inflection. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton successfully built AlexNet, an eight layer Convolutional Neural network featuring 60 million parameters. This event is even dubbed as the “Big Bang” of modern AI and it relied entirely on NVIDIA GPUs and CUDA. Today CUDA is widely considered to be the software backbone of modern artificial intelligence.

AlexNet’s success turned a new page in the story of AI and that of NVIDIA’s. NVIDIA subsequently directed its hardware and software teams entirely towards AI workloads. By 2016, NVIDIA had gone all-in on AI and had also delivered its first purpose-built AI supercomputer, the DGX -1 , to Open AI.

Demand for GPUs has only skyrocketed further post the release of generative AI tools such as ChatGPT. NVIDIA hardware has become the gold standard as tech giants seek to secure greater resources as they seek to establish new data centers and infrastructure.

Of all the major players in the AI space today, NVIDIA is perhaps the only company that produces both the hardware and the software behind AI. It not only dominates the GPU space with almost 95% market share , it has also trained and engineered its own AI models and released them. One such example is Nemotron family of open models released by NVIDIA.

What is Nemotron by NVIDIA?

NVIDIA Nemotron is a family of open models released by NVIDIA. What makes Nemotron models appealing is that these models are transparent and NVIDIA has made available the training data, the model weights and recipes on Hugging Face.

Technical reports that outline the necessary steps to recreate these models are also freely available. Unlike other models like Llama , Mistral and Qwen which are only open weight – meaning the model parameters can be downloaded and fine tuned but the datasets and the recipe used to train the model remain undisclosed and inaccessible,

Nemotron is truly open (open source for all practical purposes but not called so always because of legal framing and them not being (OSI) Open source initiative compliant.)

Open source models enable researchers to study how AI functions at a deeper level as it permits them to examine the training process in its entirety and permits much needed experimentation that garners crucial insights and fuels further research.

Conclusion

NVIDIA has continually adapted and modified its offerings to the tech world based on changing times and needs. It has strived to democratize technology and built a tech stack layer upon layer. From making cutting edge GPUs, to making them accessible for compute through CUDA to now making an array of highly capable trained models open source , a thorough examination at the companies journey reveals that its omnipresence in the AI space is no stroke of luck but the fruit of solid R&D and bold maneuvering.

Also Read: NVIDIA’s ‘Ising’ AI Models Aim to Fix Quantum Computing’s Biggest Weaknesses