GPUs began as graphics accelerators, built to rasterize triangles and shade pixels. Then they trained deep nets. Now they run the large models behind climate research, protein design, materials discovery, and automated lab work. What happens when these engines start to plan, reason, and act in the real world? Few people have had a closer view of that arc than Ian Buck, Nvidia’s vice president of hyperscale and HPC and the architect of CUDA.
With CUDA, Nvidia turned the GPU into a software platform, enabling parallel programming that supports most deep learning and HPC workloads. Under Buck’s leadership, Nvidia’s focus has widened from chips to full systems that treat the datacenter as an AI factory. The Blackwell platform, rack-scale NVLink designs, liquid cooling, and accelerated networking are all parts of the same push: make AI training and inference faster, cheaper, and easier to operate at scale.
The impact reaches into science, healthcare, and industry. Buck points to agentic and physical AI for healthcare, logistics, and industrial workflows, and to science domains where speed and fidelity matter: digital biology pipelines, Earth-2 climate simulation, and robotics tools for autonomous experimentation. We spoke with Buck about the milestones that changed his view of what GPUs can do, the shift to system-level design, and the hard problems that come next. Here is what he had to say: