Nvidia AI GPU chips have changed the world of artificial intelligence forever. In June 2024, the company’s focus on AI earned it the title of the world’s most valuable company, knocking Microsoft off the top spot with a market cap of $3.3 trillion.
Now, NVIDIA stands as the poster child for not just the generative AI boom but the future of artificial intelligence. CEO, Jensen Huang believes Nvidia will be at the forefront of what he calls the “next industrial revolution.”
So, how did Nvidia reach this point? What took the company from just another chip maker, competing with the likes of Intel and AMD, to one of the most important names in AI, responsible for between 70% and 90% of the chips used to power models like OpenAI’s GPT?
Here’s everything you need to know about how Nvidia is powering the age of artificial intelligence, and how it rose to dominance.
The Rise of NVIDIA AI GPU Chips: What are GPUs?
Nvidia didn’t achieve supremacy in the AI game overnight. It’s been rising to the top of the AI food chain for years, ever since it emerged as a GPU pioneer. GPUs (Graphics Processing Units) are processors designed to accelerate the rendering of images and 3D graphics on devices like smartphones, computers, and gaming consoles.
These chips are incredibly effective at performing calculations concurrently, in a way that powers cutting-edge AI models, like Google’s Gemini and OpenAI’s ChatGPT. These chips allow AI models to analyze huge volumes of data, and perform mathematical calculations with incredible efficiency.
Before Jensen Huang, Curtis Priem, and Chris Malachowsky gathered at a diner in Silicon Valley to discuss the creation of GPUs, and form Nvidia, CPUs (Central Processing Units) dominated the market. These chips, created by companies like Intel and AMD dominated the chip sector, but they lacked the power of GPUs. Unlike CPUs, GPUs can:
- Leverage parallel processing to perform specialized tasks simultaneously
- Increase computer performance to the level of a “supercomputer”
- Work alongside software components to create intricate ecosystems
The power of GPUs prompted countless companies to follow in Nvidia’s footsteps, creating their own unique chips. However, Nvidia benefited from an early lead in the space, and has been accelerating forward at a break-neck speed ever since. In fact, many analysts suggest that Nvidia has now created a “moat” around the AI GPU chip landscape.
The History of NVIDIA AI GPU Chips
Nvidia’s success in the AI space doesn’t just stem from its early lead in the GPU market. Ever since Nvidia was established in 1993, it’s been taking aggressive steps forward to change the computing landscape. After establishing the first widely available GPU, the Geforce 256, Nvidia continued to innovate. In 2006, Huang announced a new software technology, “CUDA”.
The CUDA proprietary parallel computing platform and application programming interface helped users program their GPUs for new tasks. It turned these chips into single-purpose solutions into general-purpose powerhouses, suitable for advanced use cases.
In 2012, researchers used Nvidia’s GPUs to spark the age of “modern AI”, leveraging them to recognize specific items in images with human-like accuracy. Next, Nvidia took computer graphics to a new level in 2018, with the first GPU capable of simulating how light behaves in the real-world, through ray-tracing.
Then, in 2022, Nvidia spurred forward by the pandemic, and the impact it had on the AI revolution, cemented its focus on artificial intelligence. The company introduced the new industry-favorite Nvidia AI GPU chips, known as H100s.
Since 2022, Nvidia’s AI focus on AI has continued, with the introduction of the GH200 (Grace Hopper Superchip), designed for LLM training. In 2024, Nvidia announced it had made over $14 billion of profit in a single quarter thanks to its AI chips.
The company even introduced the new “Blackwell GPU architecture”, enabling organizations everywhere to build, run, and train generative AI on trillion-parameter LLMs, with 25 times less energy consumption and cost.
What Nvidia AI GPU Chips are Doing Today
Over more than 10 years, Nvidia has developed an almost impregnable lead in the AI GPU chip market. Its solutions can power complex AI solutions capable of everything from image, facial and speech recognition, as well as generative AI bots like ChatGPT. Nvidia now offers a range of AI solutions spanning across hardware and software for:
- Generative AI, with its GPU chips and Nvidia NIM
- Data Analytics
- AI training and inference
- Conversational AI development
- Vision AI with API-driven building blocks
- Cybersecurity AI
The industry leader has consistently raised the bar in the AI space, specifically tailoring chips to support AI development before its competitors even woke up to the opportunity.
More than just a leader in the AI GPU chip market, Nvidia has become a one-stop-shop for AI development, offering customers computing services, software, and specialized systems to address a range of use cases, from digital human development, to biomolecular generation.
Nvidia’s CEO has publicly said that computing today is going through its biggest shift in over 60 years. GPUs and special-purpose chips are replacing microprocessors, and AI chatbots are replacing complex software coding processes, and Nvidia is behind the charge.
To further extend its influence in this space, Nvidia has also forged partnerships with big tech companies, and invested in high-profile startups. For instance, Nvidia delivered $1.3 billion in funding to Inflection AI, which was used to purchase more than 22,000 H100 chips.
Creating a Competitive Moat Around the AI Chip Market
Nvidia is far from the only company offering AI GPU chips to the masses, but the popularity and power of their technology, as well as the company’s industry-leading software, has given the firm a significant edge. In 2023, some clients reported being happy to wait almost a year to get their hands on Nvidia’s chips, rather than simply purchasing off-the-shelf alternatives.
Given the incredible demand for its chips, Nvidia has the power to decide who gets them, and how many they can purchase, despite the high price-tag of its products. Some tech leaders believe this power gives Nvidia an unfair advantage in the AI landscape.
While other manufacturers might be able to compete with Nvidia on price, most customers don’t seem to mind paying a big price tag for their tech. In fact, Huang has suggested that the superior computing power of the chips makes them well worth the price. After all, the higher the performance of the chip, the less time (and money) you’ll spend training AI models.
Unlike most chip companies, Nvidia is also happy to openly compete with their customers, like Google. However, those customers don’t seem to be complaining, for the most part. Google, for instance, has been creating AI GPU chips for a while now, but still relies on Nvidia GPUs for a lot of its AI work – like training Gemini.
How Long will Nvidia’s AI GPU Chip Dominance Last?
Today, analysts estimate that Nvidia controls up to 95% of the market for AI chips, but competition is rising. Countless other companies are investing in building similar chips, which cost a lot less money. The transition from “training” AI models, to “inference” (or deploying the models), could allow other companies to outsell Nvidia’s GPUs.
Many companies aiming to compete with Nvidia are betting that different architectures and trade-offs could allow them to build a better chip for different tasks. Additionally, there are some geopolitical issues to consider. For instance, in 2022, the US began restricting the export of Nvidia’s chips to Hong Kong and China, though some reports stay shipments are still taking place.
However, it’s difficult to deny that Nvidia remains the top solution for training and running AI models. The company has become synonymous with AI, rising not just as a GPU computing company, but a fully-fledged AI supercomputing business.
Nvidia’s comprehensive platform strategy and software-focused approach is very hard to beat. While other plays might offer chips or systems, Nvidia has built a comprehensive ecosystem that includes a full selection of software, development systems, associated hardware, and chips.
The company is also incredibly effective at integrating new capabilities into its systems quickly and efficiently. Many other startups haven’t invested much in software tooling, which gives Nvidia a lead in improving the programming experience. For the time being, it seems like the AI game is Nvidia’s to win or lose, and the company doesn’t plan on losing.
The Future of NVIDIA AI GPU Chips
Despite its significant lead in the AI GPU chip market, Nvidia recognizes the competition is heating up, and that it needs to continue innovating. The company has already committed to designing new chips every year, rather than once every two years, to stay one step ahead of the game.
As mentioned above, in 2024, Nvidia announced its series of Blackwell AI chips, intended to power the expensive data centers used to train cutting-edge AI models. The Blackwell B200, offers a significant upgrade over the previous H100 chip. For instance, training a model like GPT-4 would have taken around 8,000 H100 chips and 15 megawatts of power.
Alternatively, companies could train the same model with just 4 megawatts of power, and 2,000 B200s. On top of that, Nvidia also introduced the GB200 superchip, which combines two B200 chips, and a Grace CPU, to deliver a 30 times performance increase. Nvidia is even working on a GB200 NVL72 server rack. Plus, some analysts believe an entirely new architecture is coming in 2025.
Nvidia has also introduced various other innovations this year, such as project GRooT, a new foundation model developed to control humanoid robots, and it’s even exploring the consumer landscape, with its GeForce RTX SUPER GPUs.
In terms of focus, Nvidia’s said that it believes the automotive industry will be its largest enterprise vertical this year, pointing to Telsa’s purchase of 35,000 H100 chips to train its self-driving system. However, consumer internet companies promise to be a good vertical too.
Simply put, it seems like Nvidia isn’t going to lose its lead in the AI market any time soon. For years now, they company has been pursuing an era of accelerated computing, powering new breakthroughs in deep learning and AI.
