Why NVIDIA CEO Jensen Huang’s Big Bet on A.I. Is Paying Off

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Introduction
NVIDIA's Gaming GPU Origins
How NVIDIA Made the Shift from Gaming to AI
Why NVIDIA’s Technology Works So Well
What’s Next for NVIDIA?
What AI Leaders Rely on NVIDIA?
Last Words on Huang’s Big Bet on AI

Why NVIDIA CEO Jensen Huang’s Big Bet on A.I. Is Paying Off

Curated by

cdteliot

4 min read

185

NVIDIA CEO Jensen Huang's early investment in artificial intelligence is now yielding significant returns, with the company's technology powering cutting-edge AI systems like ChatGPT and positioning NVIDIA at the forefront of the AI revolution.

NVIDIA's Gaming GPU Origins

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Founded in 1993 by Jensen Huang, Chris Malchowsky, and Curtis Priem, NVIDIA has made a massive impact on the computing hardware industry

. The tech giant was first known for its GPUs (graphics processing units), which have taken the gaming industry to new heights

. If you've found yourself playing a video game and thinking how amazing and life-like the graphics have become, you can probably thank NVIDIA for making it possible

. GPUs are specialized processors that have been innovated to make the flow of images, animations, and videos faster during gameplay

. Back in the day, this was done by CPUs (central processing units) that could only handle a few tasks at a time, rather than numerous all at once

. The results were exciting for the time, as humanity experienced simple (yet for that time, cool) graphics and sometimes slow processing times

. It was all fine then, but soon gamers were demanding more from their beloved games

. As the gaming industry evolved, NVIDIA's GPUs quickly became the leading processors

. This inspired Huang to usher in a future where GPUs can do so much more than bring to life brilliant gaming environments

. He began to think his GPUs could be used to accelerate much more complex projects, especially AI and deep learning

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How NVIDIA Made the Shift from Gaming to AI

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As the actual computational demands of AI became clearer in the early 2010s, NVIDIA began investing in adapting its GPU techs to get ahead of the curve. This included investing in a better understanding of deep learning and exploring whether its GPUs handle its demands.

The company decided that it needed to develop specialized hardware and software that were specifically tailored for AI's complexity. The first step was to introduce the CUDA (Compute Unified Device Architecture) platform to allow developers to use GPU acceleration for computing tasks that went far beyond delivering amazing graphics.

Huang was thrilled when his innovation gamble produced its first success. NVIDIA's GPUs worked exceptionally well for AI and deep learning, as they were able to perform thousands of operations at a time and process massive datasets.

When the word about NVIDIA's breakthrough hit the streets, the brand became the go-to for leading AI innovators, including OpenAI. Without this leap in technology, AI simply wouldn't be where it is today.

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Why NVIDIA’s Technology Works So Well

Now, let's take a deeper look at why NVIDIA's GPUs have become the gold standard in the AI industry: Advanced Hardware Design
NVIDIA's GPUs, including the Tesla, H100, and A100, have been carefully innovated for maximum performance where computing and AI workloads are concerned

. Their wildly powerful computational power makes training neural networks quick, easy, and more efficient as datasets grow over time. Their Tensor Cores are specifically designed to accelerate AI computations

. These are specialized processing units within NVIDIA GPUs that can deal with the demands of complex math operations that must occur for AI and deep learning to work

. When combined with NVIDIA's Volta, Turing, and Ampere architectures, AI giants can properly train and deploy neural networks faster and more efficiently

. Carefully Designed Software Ecosystem
Beyond its hardware, NVIDIA has spent countless hours developing its software ecosystem. CUDA gives developers the ability to write software that benefits from GPU acceleration and leads to powerful AI applications

. Flexibility and Scaleability
NVIDIA has provided scalable solutions. Whether you're an individual developer or a large-scale data center, it's possible to capitalize on its technologies

. In recent days, its GPUs are even being used for supercomputers, including Elon Musk's Colossus.

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What’s Next for NVIDIA?

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As the industry evolves, NVIDIA is expected to greatly extend its reach. This includes data centers, AI within new industries, in Edge computing, AI research, and more

. For example, cloud service providers are going through an evolution that includes enhancing their AI capabilities. The writing is on the wall for the cloud—either cloud computing providers evolve to meet the increasing demands of AI or get lost in the mix. Tech giants like Microsoft, Amazon, and Google have been racing to amp their cloud computing infrastructures with NVIDIA's GPUs

. NVIDIA is also working to expand its influence in robotics, healthcare, automotive, and other key industries

. Its DRIVE platform is working to help autonomous vehicles become safer and more efficient by mastering the game of real-time AI processing. Next, the company decided to jump into playing a role in the "Internet of Things" (IoT) and edge computing. If you're not familiar with IoT, it's a network of physical and network objects that are connected to the internet, including voice assistance, wearables, ingestible devices, self-checkout tools, and infrastructure monitoring (for bridges, railways, and more). This is all made possible by AI processing outside traditional data centers

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What AI Leaders Rely on NVIDIA?

The question should be, what AI leaders don't rely on NVIDIA's technologies? The list is constantly growing, but it's common knowledge that the leaders include:

Open AI to train its large-scale language models.
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DeepMind for its research on AI that has included advancements for reinforcement learning and neural networks.
Tesla for its Autopilot and Full Self-Driving features.
Microsoft for Azure AI and its growing number of research projects.
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Google and Amazon for their cloud services so that they can offer AI and machine learning services to their customers.
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You can expect this list to grow by the day, as more budding organizations dive into AI-specific products and services. In recent news reports, tech giants, including Elon Musk have admitted to pleading for more GPU from NVIDIA to help them complete their supercomputers. Elon secured 100k GPU units for his xAI supercomputer and has announced he will be doubling that amount in the coming months. Plus, he has already been hard at work building the supercomputer for Tesla, which he also says will be doubling in GPU power by 2025.

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Last Words on Huang’s Big Bet on AI

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Jensen Huang's gumption and big bet on AI have officially paid off. NVIDIA's rise from gaming GPU innovator to golden standard for AI GPU units is opening the door for innovation beyond anyone's wildest dreams

. This journey illustrates what strategic vision and technical prowess can do to shape the future as we all know it. In the coming months and years, you can expect NVIDIA to expand its reach far beyond AI language models and into industries that are in desperate need of innovation

. From healthcare to transportation, to everyday human activities, its technologies will increasingly be impacted by its GPUs and evolving platforms