The Big Flip from CPUs to GPUs
For decades, CPUs were the heart of high performance computing. That picture has changed fast. Today, the most powerful supercomputers in the world are powered primarily by GPUs, and NVIDIA is leading that transformation.
In the TOP100, a subset of the TOP500 list of the fastest supercomputers, over 85 percent of systems now use GPUs. This is a historic flip from serial CPU computing to massively parallel GPU computing.
The reason comes down to what modern workloads actually need. Traditional software was mostly about following fixed rules and logic. That ran well on CPUs. Once deep learning took off around 2012, especially after AlexNet used gaming GPUs to crush image recognition tasks, it was clear that AI learns best from massive amounts of data processed in parallel. That is exactly what GPUs are built for.
This shift is not only about raw power. It is also about efficiency. GPUs deliver far more operations per watt than CPUs, which is critical when you are talking about exascale systems and huge data centers.
On the Green500 list of the most energy efficient supercomputers, the top five all use NVIDIA GPUs, averaging about 70.1 gigaflops per watt. The best CPU only systems average around 15.5 flops per watt. That is roughly a four and a half times efficiency advantage for GPUs. For anyone paying the power bill or planning data center capacity, this is a big total cost of ownership win.
The performance gap shows up in real benchmarks too. On the Graph500 breadth first search list, NVIDIA hit 410 trillion traversed edges per second using 8,192 H100 GPUs on an enormous graph. The next best result needed around 150,000 CPUs to get close. That is a huge reduction in hardware footprint, energy use and complexity.
But NVIDIA’s platform is more than just chips. Networking, memory, storage, CUDA software and orchestration are co designed as a full stack to keep data moving and workloads optimized end to end. Accelerated computing is now a platform strategy, not just a single component swap.
CUDA, Software and the Three Scaling Laws of AI
Modern AI needs much more than a powerful GPU. It needs a software stack that squeezes every bit of performance out of that hardware. This is where CUDA and the CUDA X ecosystem come in.
CUDA gives developers direct access to GPU acceleration. On top of that, NVIDIA and the open source community have built specialized libraries for data science, machine learning, analytics, simulations and more. These libraries are where a lot of the real world speedups happen.
For example, Snowflake has integrated NVIDIA A10 GPUs together with CUDA X libraries like cuML and cuDF into Snowflake ML. Users can accelerate machine learning workflows without changing their existing code. NVIDIA benchmarks show Random Forest training can run about 5 times faster and HDBSCAN clustering up to 200 times faster on A10 GPUs compared to CPUs. That sort of improvement quickly pays off in data center and cloud costs.
This acceleration backbone connects directly to what NVIDIA calls the three scaling laws of AI. These describe how compute demand grows across the full life cycle of an AI model:
- Pretraining scaling: The first wave of progress came from training bigger models on more data. The pattern was simple. Increase dataset size and parameter count and models get more accurate and more capable in predictable ways. GPUs made this possible within realistic time and power budgets.
- Post training scaling: After pretraining a general foundation model, you still need to specialize it. That includes fine tuning for specific industries or languages, reinforcement learning from human feedback, pruning and distillation. These steps often require compute similar to or even approaching pretraining. Again, GPUs keep this practical.
- Test time scaling: The newest shift involves how much compute models use during inference. With mixture of experts architectures, chain of thought reasoning and agent like behavior, models are starting to reason and branch at runtime. This can demand dynamic and recursive compute that sometimes rivals training itself, especially at scale.
Together, these three scaling laws explain why GPU demand is still rising even after a model has been trained. GPUs are not just for the training phase anymore. They are needed throughout the entire life cycle: from learning, to fine tuning, to real time reasoning in production.
On industry benchmarks like MLPerf Training, NVIDIA’s platform has posted top results across every test and is often the only one to submit across the full suite. Without GPU acceleration, the larger is better era of AI would have been stalled by energy, time and cost limits.
From Generative AI to Physical Robots
The impact of this GPU powered revolution is reaching far beyond basic chatbots or simple recommenders.
Vision language models combine image understanding with natural language. Recommender systems now learn from vast interaction logs and can be retrained and refined frequently. With CUDA GPUs and the scaling laws, these systems can move from static scoring engines to dynamic reasoning engines that evaluate multiple options in real time. Even a one percent gain in relevance accuracy can translate into billions in additional sales for the largest ecommerce platforms.
Hyperscalers that operate search, streaming and ecommerce platforms are investing heavily to move from classic machine learning to full generative AI for search, recommendations and content understanding. NVIDIA says its platforms can run all leading generative models and support around 1.4 million open source models, making it a default choice for this transition.
The trend does not stop at virtual worlds. Agentic AI is turning models into systems that can perceive, plan and act with relative autonomy, almost like digital colleagues. These agents can handle multi step workflows in areas like logistics or research, freeing humans to focus on higher level decisions.
Then there is physical AI: robots and machines equipped with advanced reasoning and perception. NVIDIA outlines a three computer approach for humanoid and mobile robots:
- NVIDIA DGX GB300 for training huge vision language action models that define how the robot understands the world and decides what to do.
- NVIDIA RTX Pro GPUs for simulating, testing and validating these models in virtual environments using Omniverse. This virtual training grounds helps refine behavior before real world deployment.
- Jetson Thor as the on device brain that runs the trained model in real time on the robot itself.
Analysts like Morgan Stanley expect up to one billion humanoid robots by 2050, with trillions in associated revenue. NVIDIA’s Project GR00T, a foundation model aimed at humanoid robots, is an example of how GPUs, simulation and AI models all come together.
The bigger picture is that AI is no longer just a tool you call occasionally. It is becoming a core worker inside every digital and physical workflow. That shift is forcing the entire computing stack to evolve away from CPU centric designs toward GPU accelerated platforms built for parallelism, energy efficiency and AI scaling at every stage.
Original article and image: https://blogs.nvidia.com/blog/gpu-cuda-scaling-laws-industrial-revolution/
