This month, the semiconductor technology company Nvidia became the world's newest tech titan and a trillion dollar company. The company initially focused on high-end graphics processing units (GPUs) for video games and business visualization applications. Thereafter, its focus shifted to artificial intelligence (AI) and has grown to be a fierce competitor in the segment. For running AI models, Nvidia's GPUs are frequently regarded as the best in the business. In fact, the world's famous OpenAI, which debuted the chatbot ChatGPT in November 2022, is powered by Nvidia GPU chips.

Nevertheless, Nvidia’s financial situation took a swing and today this 1993-founded company is deemed as the most valuable tech company in the US among Apple, Microsoft, Alphabet, and Amazon. After reaching a market capitalization of over $ 1 trillion, its stock has climbed by 170 percent since the dawn of the year. To examine its rise into the AI space, it's necessary to look at its starting points, which roots back to gaming and computer graphics.

From Gaming and Computer Graphics to AI

Gaming and computer graphics have been at the helm of Nvidia's priorities. But the priorities changed after Facebook and Google became popular with their services that are enhanced by using machine learning (ML), deep learning (DL), and AI models. In turn these models required enormous amounts of computer power, as thousands of chips operate simultaneously. Nvidia chips were ideal for the task and this distinctively garnered clout for it apart from its core gaming and graphics markets.

How? Nvidia's GPUs excel at AI applications as they can handle several calculations at once, which makes them perfect for the intricate mathematical calculations needed by AI algorithms. Additionally, Nvidia created specialized hardware and software for AI, such as its Tensor Cores, which are intended to speed up deep learning algorithms.

After that, it went on to form alliances with top tech companies such as Google, Microsoft, and Amazon, eventually contributing more success in its AI endeavor. This led to positioning itself as a pioneer in the field of AI and creating new products and technologies that cater to the needs of its customers.

 

GPUs from NVIDIA: NVIDIA GPUs are highly designed for parallel computing, which is necessary for training big language models. Since they can handle numerous computations simultaneously, NVIDIA GPUs became the right fit for deep learning tasks like NLP (Natural Language Processing).

Ease of Training ChatGPT Language Models

Going into more detail, employing NVIDIA GPUs to train ChatGPT Large-scale language models, like ChatGPT, demand a lot of computational power to train. The training procedure gets accelerated through NVIDIA GPUs, enabling the training of bigger and more complicated models. Another acceleration is done into mixed-precision training thorugh NVIDIA's Tensor Cores, which lowers the amount of memory needed for training and speeds up the training process.

Revolutionizing the Gaming Sector with AI

The rapid expansion of Nvidia's data center segment recently surpassed gaming as the company's top revenue generator. The Nvidia Avatar Cloud Engine (ACE), the company's most recent AI-based tool for game makers, has the potential to revolutionize the gaming industry and solidify Nvidia's commanding lead over Advanced Micro Devices.

Even with the introduction of the GeForce RTX gaming GPUs in 2018, Nvidia transformed gaming. These graphics cards' ray tracing technology uses AI to generate more realistic sceneries, but Nvidia is now utilizing AI to make in-game characters appear more lifelike.

Natural language models, the processing technique behind OpenAI's ChatGPT, are used by game characters created with Nvidia's ACE. As a result, the NPCs' dialogue will be less scripted and more dynamic for players to hear. The player's dialogue choices will be able to elicit intelligent responses from NPCs that are in line with the NPC's narrative past.

What ACE will mean for revenue is hard to measure. Nvidia's Omniverse development platform, which is used to produce 3D visuals and apps, contains a technology called ACE. For the purpose of developing virtual avatars or characters for video games, developers can use the ACE foundry service, which runs on Omniverse.

The revenue growth of Nvidia's professional visualization division, which includes Omniverse, could potentially be impacted by ACE. ACE serves as yet another illustration of why Nvidia now appears unstoppable. With an 84 percent market share in discrete GPUs, it already dominates AMD by a wide margin. Additionally, Nvidia is thought to hold a market share of over 80 percent for AI chips, while AMD may begin to overtake Nvidia as it increases its investment in AI computing.

Tough Competitor

On June 13, AMD unveiled the MI300X, their AI graphics processor. This is the hardest challenge Nvidia has faced so far. The AMD CPU is reported to be ready until the end of this year, but AWS is considering utilizing one. By providing CPUs at a lower price than Nvidia, AMD hopes to attract customers.

To analyze massive volumes of data for AI, companies like Google, Amazon, Microsoft, Meta, and others have started to build their own processors. Tensor Processing Units (TPUs), developed by Google, are powerful data processing devices that are optimized for use in neural networks. The Mac's graphics processors were created by Apple.

For its augmented reality (AR) ambitions, Meta is working with Qualcomm to develop its own CPUs. Involved parties include Apple, Google, Qualcomm, and even Meta. There will eventually be numerous participants. Possibly 15 years ago, many believed Intel was unbeatable. While they might not be able to completely compete with Nvidia, it is said that working together with AMD and Intel is in their best interests to prevent Nvidia from dominating the market in the long term.

Spearheading with AI

The government introduced a $ 10 billion incentive program for fabs and chip design in December 2021. India has a shortage of manufacturing, yet it has more than 1 lakh chip designers. This accounts for about 20 percent of the world's competence in chip design at international design firms.

The walled-garden technique employed by Nvidia encourages rivals to react swiftly. Only Nvidia processors are compatible with its CUDA software. As long as their strategy fits with how the market is restructuring, Nvidia has a significant role to play.

The latest crypto collapse, one of Nvidia's many difficulties, has lowered demand for its chips. Nvidia has largely been overshadowed by giants in the chip sector like AMD and Intel. That was before AI swept the world and tech companies realized Nvidia's processors were the best at making machines appear intelligent. The company currently enjoys a considerable advantage in AI.