How NVIDIA’s AI Chips Are Accelerating Faster Than Moore’s Law

Learn how NVIDIA's AI chips surpass Moore's Law, driving groundbreaking advancements in technology and reshaping the future of AI.

The world of computing and artificial intelligence is surging forward at an unprecedented pace, and one company—NVIDIA—is leading the charge. At the 2025 Consumer Electronics Show (CES) in Las Vegas, NVIDIA CEO Jensen Huang made a bold announcement that has everybody in tech sitting up and paying attention: the company’s AI chips are advancing faster than Moore’s Law—a concept that has defined the evolution of technology for decades.

But what does this mean for the future of AI, computing, and innovation? Let’s dive into how NVIDIA is rewriting the rules and paving the way for hyper-accelerated progress in artificial intelligence.

What Is Moore’s Law, and Why Is NVIDIA Surpassing It?

At its core, Moore’s Law is the principle named after Gordon Moore, Intel’s co-founder, who predicted in 1965 that the number of transistors on a microchip would double every year, subsequently doubling computing performance. This theory proved remarkably accurate for decades, revolutionizing technology and driving down costs for consumers.

However, in recent years, the rate of progress predicted by Moore’s Law has slowed, sparking concerns about whether computing innovation is reaching its limits. Enter NVIDIA.

Huang’s claim that NVIDIA’s AI chips are advancing "way faster than Moore’s Law" marks a departure from traditional computing expectations. According to Huang, this remarkable progress stems from NVIDIA’s ability to innovate across the entire technology stack—from architecture and chip design to systems, libraries, and algorithms. 

“If you innovate at every level, you break free from the constraints of Moore’s Law,” said Huang in an interview.

This bold statement positions NVIDIA not just as a leader in hardware development but as a transformational force capable of pushing computing boundaries way beyond what was once thought possible.

A Closer Look at NVIDIA’s AI Advancements  

So, what exactly is NVIDIA doing to outpace Moore’s Law?  

1. Unprecedented AI Inference Performance

NVIDIA’s latest GB200 NVL72 superchip has shattered previous records by delivering a jaw-dropping 30 to 40 times the performance of its predecessor, the H100 chip, for running AI inference workloads. This advancement is a massive leap forward, especially for tasks like test-time compute (the process in which an AI model refines its output during real-time interactions).

AI inference models such as OpenAI's O3, which require intense computational power, rely heavily on robust hardware to run efficiently. In fact, NVIDIA’s innovations promise to make such models not only faster but also significantly more affordable over time—a critical factor for widespread AI adoption.

2. Scaling AI Beyond the Limits

Huang also introduced a concept that could revolutionize the way AI progresses: three new AI scaling laws. These laws cover:

• Pretraining: The foundation stage where AI models analyze large datasets to learn patterns.  
• Posttraining: The refining process where AI is improved using advanced fine-tuning techniques.
• Test-time compute: The phase where complex AI models take more time to “think,” producing highly accurate outputs.  

By optimizing hardware for each of these phases, NVIDIA is targeting the entire AI lifecycle—not just isolated parts.

Why NVIDIA’s Innovation Matters

NVIDIA’s claims come at a pivotal point in the tech world, where concerns over the cost and scalability of advanced AI models are growing. For instance, training and running a model like OpenAI’s GPT-based O3 can cost millions of dollars, creating obstacles for broader AI accessibility.

By increasing AI inference efficiency and driving down costs, NVIDIA is not only making AI more accessible for companies and researchers—it’s setting the stage for transformation across industries such as healthcare, transportation, and robotics.

Huang even suggested that advancements in inference models could improve the pretraining and posttraining phases of AI, creating a self-reinforcing loop of innovation that propels progress even further.

“The same way Moore’s Law reduced computing costs, we’ll see inference costs drop dramatically,” Huang noted.

How NVIDIA Stays on Top of the AI Game

NVIDIA’s dominance in the AI hardware market isn’t just about performance—it’s about strategy. By developing solutions that serve every stage of modern AI demands, the company has become indispensable to industry leaders like Google, OpenAI, and Anthropic, who rely on its hardware for both training and running their AI systems.

Even amidst doubts about whether AI progress has hit a plateau, Huang aims to silence skeptics. He points to the fact that NVIDIA’s AI chips today are 1,000 times more powerful than they were just ten years ago, outracing the exponential growth Moore’s Law once promised.

1. Focus on Inference Computing

As the world shifts its focus from training AI models to scaling their real-world applications, NVIDIA is keeping pace with inference-heavy designs. These are particularly important for AI reasoning models and applications like ChatGPT.

Huang showcased the GB200 NVL72 superchip as the solution to ensuring that inference becomes faster and cheaper, so developers can push the boundaries of AI-powered tools without worrying about prohibitive costs.

2. Holistic Innovation Across the Tech Stack

Unlike most companies that specialize in isolated elements of technology, NVIDIA has the unique ability to innovate at every layer—architecture, systems, libraries, and algorithms. This approach allows NVIDIA to accelerate progress across all areas of AI development faster than their competition, fueling a momentum many in the industry didn’t believe possible.

A Glimpse of the Future: Affordable, Scalable AI

The implications of faster, more affordable AI technology extend far beyond research labs and tech giants. With models gradually becoming cheaper to run and deploy, we can expect to see smarter tools and services reshaping our everyday lives.

For example, smarter AI assistants in customer service or real-time language models could break down language barriers entirely. NVIDIA’s advancements also hint at AI’s growing role in training systems such as self-driving cars, robotics healthcare solutions, and even personalized medical AI advisors.

As Huang aptly put it, “This is just the beginning.”

Conclusion: NVIDIA’s Bold Vision

Jensen Huang’s vision for NVIDIA isn’t just focused on short-term technological gains—it’s about revolutionizing computing as we know it. By operating at a pace faster than Moore’s Law, NVIDIA is sparking a seismic wave of innovation across artificial intelligence and beyond.

Whether it’s the development of scalable AI models, more efficient chips, or breakthroughs at every level of the technology stack, NVIDIA is proving that the future of computing isn’t just bright—it’s here. So, keep an eye on this tech giant, as it’s safe to say that NVIDIA is paving the way for a new era of AI-powered possibilities.