The NVIDIA accelerated computing platform is leading supercomputing benchmarks once dominated by CPUs, enabling AI, science, business and computing efficiency worldwide. Moore's Law has run its course, and parallel processing is the way forward. With this evolution, NVIDIA GPU platforms are now uniquely positioned to deliver on the three scaling laws—pre-training, post-training and test-time compute—for everything from next-generation recommender systems and large language models (LLMs) to AI agents and beyond.

The CPU-to-GPU Transition: A Historic Shift in Computing
At SC25, NVIDIA founder and CEO Jensen Huang highlighted the shifting landscape. Within the TOP100, a subset of the TOP500 list of supercomputers, over 85% of systems use GPUs. This flip represents a historic transition from the serial‑processing paradigm of CPUs to massively parallel accelerated architectures. Before 2012, machine learning was based on programmed logic. Statistical models were used and ran efficiently on CPUs as a corpus of hard-coded rules. But this all changed when AlexNet running on gaming GPUs demonstrated image classification could be learned by examples. Its implications were enormous for the future of AI, with parallel processing on increasing sums of data on GPUs driving a new wave of computing.