In the rapidly evolving world of technology, NVIDIA’s accelerated computing platform is making significant strides in supercomputing benchmarks, which were once dominated by traditional CPUs. This transition is not just a shift in hardware but a revolution in computing efficiency that is driving advancements in artificial intelligence (AI), scientific research, business processes, and overall computing performance across the globe.
The once-celebrated Moore’s Law, which suggested that the number of transistors on a microchip doubles approximately every two years, leading to an increase in performance, has reached its limits. The future now belongs to parallel processing, and NVIDIA’s GPU (Graphics Processing Unit) platforms are at the forefront of this evolution. They are uniquely positioned to meet the demands of the three scaling laws essential for the next generation of AI technologies: pretraining, post-training, and test-time compute. These advancements are pivotal for developing cutting-edge recommender systems, large language models (LLMs), AI agents, and beyond.
### How NVIDIA is Revolutionizing the Computing Landscape
NVIDIA has fundamentally transformed the foundation of computing by enabling AI pretraining, post-training, and inference to push the boundaries of what is possible. The company’s efforts in integrating AI into various applications have changed how hyperscalers—companies that manage vast data centers—utilize AI to revolutionize search functions and recommender systems.
At the SC25 conference, NVIDIA’s founder and CEO, Jensen Huang, highlighted the changing supercomputing landscape. Within the TOP100, a subset of the TOP500 list of supercomputers, over 85% of systems now utilize GPUs. This represents a historic shift from the traditional serial-processing paradigm of CPUs to the massively parallel architectures of GPUs. This is not just a hardware transition; it signifies platforms unlocking new realms of science. GPUs offer significantly more operations per watt, making the pursuit of exascale computing feasible without imposing unsustainable energy demands.
Recent results from the Green500 list, which ranks the world’s most energy-efficient supercomputers, emphasize the stark contrast between GPUs and CPUs. The top five performers, all utilizing NVIDIA GPUs, achieved an average of 70.1 gigaflops per watt. In contrast, the top CPU-only systems managed only 15.5 flops per watt on average. This 4.5 times greater energy efficiency of GPUs over CPUs highlights the substantial total cost of ownership (TCO) advantages in migrating these systems to GPU technology.
### The Impact of NVIDIA’s GPU Technology
The implications of NVIDIA’s GPU technology extend beyond mere energy efficiency. Researchers can now train models with trillions of parameters, simulate complex systems like fusion reactors, and accelerate drug discovery at scales that would be impossible with CPUs alone. NVIDIA’s impressive performance on the Graph500 benchmark further illustrates the energy-efficiency and performance differential between CPUs and GPUs. The company achieved a record-breaking result of 410 trillion traversed edges per second, significantly outpacing the next highest score by using 8,192 NVIDIA H100 GPUs to process a graph with 2.2 trillion vertices and 35 trillion edges. This achievement required a fraction of the hardware footprint compared to CPU-based systems, resulting in substantial savings in time, money, and energy.
NVIDIA’s accomplishments at SC25 showcased that their AI supercomputing platform extends beyond GPUs. Networking, CUDA libraries, memory, storage, and orchestration are co-designed to create a comprehensive full-stack platform. Enabled by CUDA, NVIDIA’s full-stack platform includes open-source libraries and frameworks within the CUDA-X ecosystem, resulting in significant speed improvements.
Snowflake, a data cloud company, recently announced the integration of NVIDIA A10 GPUs into its data science workflows. Snowflake ML now comes preinstalled with NVIDIA’s cuML and cuDF libraries, accelerating popular machine learning algorithms with these GPUs. This native integration allows Snowflake’s users to accelerate model development cycles without requiring any code changes. NVIDIA’s benchmark tests demonstrate that tasks like Random Forest and HDBSCAN can be completed up to 200 times faster on NVIDIA A10 GPUs compared to CPUs.
### The Significance of The Three Scaling Laws
The transition from CPUs to GPUs is not merely a milestone in supercomputing; it is the foundation for the three scaling laws that represent AI’s next frontier: pretraining, post-training, and test-time scaling. These scaling laws outline the roadmap for AI’s workflow and highlight the critical role of GPUs in this journey.
#### Pretraining Scaling
Pretraining scaling was the first law to aid the industry. Researchers discovered that as datasets, parameter counts, and compute resources grew, model performance improved predictably. Doubling the data or parameters led to significant leaps in accuracy and versatility. In the latest MLPerf Training industry benchmarks, NVIDIA’s platform delivered the highest performance on every test and was the only platform to participate in all tests. Without GPUs, the era of scaling AI research would have stalled due to power budget constraints and time limitations.
#### Post-Training Scaling
Post-training scaling extends the story further. Once a foundation model is established, it needs refinement—fine-tuning for specific industries, languages, or safety requirements. Techniques such as reinforcement learning from human feedback, pruning, and model distillation require substantial additional compute resources. In some cases, these demands rival those of pretraining itself. GPUs provide the necessary computing power, enabling continuous fine-tuning and adaptation across various domains.
#### Test-Time Scaling
Test-time scaling, the latest scaling law, might prove to be the most transformative. Modern models powered by mixture-of-experts architectures can reason, plan, and evaluate multiple solutions in real time. Chain-of-thought reasoning, generative search, and agentic AI require dynamic, recursive compute capabilities—often exceeding pretraining requirements. This stage will drive exponential demand for inference infrastructure, from data centers to edge devices.
Together, these three scaling laws explain the growing demand for GPUs in new AI workloads. Pretraining scaling has made GPUs indispensable, post-training scaling has reinforced their role in refinement, and test-time scaling ensures that GPUs remain critical long after training ends. This marks the next chapter in accelerated computing: a lifecycle where GPUs power every stage of AI, from learning to reasoning to deployment.
### Broader Implications of Accelerated Computing
Accelerated computing is not limited to AI. CUDA-X libraries accelerate workloads across various industries and applications, including engineering, finance, data analytics, genomics, biology, chemistry, telecommunications, robotics, and more. “The world has a massive investment in non-AI software,” said Huang during NVIDIA’s recent earnings call. “From data processing to science and engineering simulations, representing hundreds of billions of dollars in compute cloud computing spend each year.” Many applications that once ran exclusively on CPUs are now rapidly transitioning to CUDA GPUs. “Accelerated computing has reached a tipping point. AI has also reached a tipping point and is transforming existing applications while enabling entirely new ones,” he added.
### The Future: Generative, Agentic, and Physical AI
The world of AI is expanding beyond traditional recommenders, chatbots, and text generation. Vision Language Models (VLMs) are AI systems that combine computer vision and natural language processing to understand and interpret images and text. Recommender systems, which drive personalized shopping, streaming, and social feeds, are just one example of how the shift from CPUs to GPUs is reshaping AI.
Generative AI is revolutionizing fields from robotics and autonomous vehicles to software-as-a-service companies, representing a significant investment in startups. NVIDIA platforms are the only ones capable of running all leading generative AI models and handling 1.4 million open-source models.
Once constrained by CPU architectures, recommender systems struggled to capture the complexity of user behavior at scale. With CUDA GPUs, pretraining scaling enables models to learn from massive datasets of clicks, purchases, and preferences, uncovering richer patterns. Post-training scaling fine-tunes these models for specific domains, enhancing personalization for industries ranging from retail to entertainment. Even a 1% improvement in the relevance accuracy of recommendations on leading global online sites can result in billions more in sales.
Electronic commerce sales are projected to reach $6.4 trillion worldwide by 2025, according to Emarketer. The world’s hyperscalers, a trillion-dollar industry, are transforming search, recommendations, and content understanding from classical machine learning to generative AI. NVIDIA CUDA excels at both and is the ideal platform for this transition, driving infrastructure investment measured in hundreds of billions of dollars.
Now, test-time scaling is revolutionizing inference itself: recommender engines can dynamically evaluate multiple options in real time to deliver context-aware suggestions. The result is a leap in precision and relevance—recommendations that feel less like static lists and more like intelligent guidance. GPUs and scaling laws are transforming recommendation systems into a frontline capability of agentic AI, enabling billions of people to navigate through trillions of items on the internet with an ease that would otherwise be impossible.
What started as conversational interfaces powered by large language models (LLMs) is now evolving into intelligent, autonomous systems poised to reshape nearly every sector of the global economy. We are witnessing a foundational shift—from AI as a virtual technology to AI entering the physical world. This transformation necessitates explosive growth in computing infrastructure and new forms of collaboration between humans and machines.
Generative AI has demonstrated its capability to create not only new text and images but also code, designs, and even scientific hypotheses. Now, agentic AI is emerging—systems that perceive, reason, plan, and act autonomously. These agents behave less like tools and more like digital colleagues, executing complex, multistep tasks across various industries. From legal research to logistics, agentic AI promises to enhance productivity by serving as autonomous digital workers.
Perhaps the most transformative leap is physical AI—the embodiment of intelligence in robots of various forms. To create physical AI-embodied robots, three computers are required: NVIDIA DGX GB300 to train the reasoning vision-language action model, NVIDIA RTX PRO to simulate, test, and validate the model in a virtual world built on Omniverse, and Jetson Thor to run the reasoning VLA at real-time speed.
A breakthrough in robotics is anticipated within the coming years, with autonomous mobile robots, collaborative robots, and humanoid robots set to disrupt industries such as manufacturing, logistics, and healthcare. Morgan Stanley predicts there will be 1 billion humanoid robots generating $5 trillion in revenue by 2050.
This signals how deeply AI will integrate into the physical economy, representing just a glimpse of what lies ahead. NVIDIA CEO Jensen Huang recently showcased a lineup of advanced humanoid robots during his keynote address at the GTC DC 2025 conference. These robots, including models from Boston Dynamics, Figure, Agility Robotics, and Disney Research, were brought together to highlight NVIDIA’s new Project GR00T, aimed at advancing the capabilities of humanoid robots and artificial intelligence.
AI is no longer just a tool. It is becoming a transformative force, poised to impact every market globally, valued at $100 trillion. The virtuous cycle of AI is fundamentally altering the entire computing stack, transitioning all computers into new supercomputing platforms capable of tapping into vast new opportunities.
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