Interview Scientific computing is about to undergo a period of rapid change as workloads inject AI.

So says Ian Buck, Nvidia’s VP and General Manager of Hyperscale and HPC, who told The Register he expects that within a year or two, the application of AI will be pervasive throughout high performance computing and scientific workloads.

“Today we’re in the phase where we have luminary workloads. We have amazing examples of where AI is going to make scientific discovery so much faster and more productive,” he said.

He therefore predicts that scientific computer designs will change to run those workloads, and cited the gradually-then-suddenly appearance of GPU-powered machines on the Top500 list of earth’s mightiest supercomputers as an example of the change he expects.

“Just like accelerated computing took maybe five years to hit a curve where the Top500 started to flip over and today it’s like 80 plus percent [GPU accelerated].”

While Buck expects AI to play a much bigger role in scientific computing, he said Nvidia isn’t trying to replace workloads with generative AI models.

“In the beginning there was a lot of confusion. AI was confused with replacing simulation and that is an inexact science,” he said. “AI is statistics. It’s machine learning. It’s taking data and making a prediction. Statistics works in probabilities, which is not 64-bit floating point, it’s how close to 1.0 are you.

“The narrative of ‘Will AI replace simulation?’ was the wrong question. It wasn’t ever going to replace simulation. AI is a tool, one of many tools to be able to do scientific discovery.”

One way Nvidia is looking to do this is by using AI and machine learning help researchers focus their attention on the most likely candidates worthy of deeper investigation.

“Trying to figure out what is the crystalline structure of a new alloy of aluminum or metal or steel that’s going to make jet engines run faster, lighter, hotter, and more efficiently requires searching the space for every possible molecular compound,” Buck explained. “We can simulate all those, but it would take millennia. We’re using AI to predict, or approximate, or weed out the ones that we should simulate.”

Enabling the AI-HPC merger

Nvidia has already developed a wide variety of software frameworks and models to assist researchers, including frameworks like Holoscan for sensor processing, BioNeMo for drug discovery, and Alchemi for computational chemistry.

On Monday, the company unveiled Apollo – a new family of open models designed to accelerate industrial and computation engineering. Nvidia has integrated Apollo into industrial design software suites from Cadence, Synopsys, and Siemens.

In the quantum realm, a field Nvidia expects will play a key role in scientific computing, the GPU giant unveiled NVQLink, a system connecting quantum processing units with Nvidia-based systems to enable large-scale quantum-classic workloads using its CUDA-Q platform.

FP64 isn’t going anywhere

While these models have the potential to accelerate scientific discovery they don’t replace the need for simulation.

“In order to build a great supercomputer, it needs to be good at three things: It has to be great at simulation; it has to be great at AI; and it also has to be a quantum supercomputer,” Buck said.

That means Nvidia accelerators must support both the ultra-low precision datatypes favored by generative AI models and the hyper-precise FP64 compute traditionally associated with academic supercomputers.

FP64 is a requirement, Buck says.

Nvidia’s commitment to the data type has been called into question in recent years, particularly as AI models have trended toward low-precision data types like FP8 or FP4.

When Blackwell was revealed in early 2024, Nvidia confused many in the scientific community by cutting the FP64 matrix performance from 67 teraFLOPS on Hopper to 40 to 45 teraFLOPS depending on the SKU.

But while FP64 matrix performance used by benchmarks like high-performance linpack declined gen-on-gen, FP64 vector performance – a category better suited to workloads like the High Performance Conjugate gradient (HPCG) benchmark – rose from 34 teraFLOPS to 45.

This increase didn’t become apparent until months after Nvidia unveiled the chip. Further complicating the matter, Nvidia’s Blackwell Ultra accelerators announced a year later, effectively reclaimed the die area previously dedicated to FP64 to boost dense 4-bit floating point performance for AI inference.

While this certainly gave the impression that Nvidia had ceded the high-performance computing market to rival AMD in its pursuit of the broader AI market, Buck insists this is simply not the case.

Instead, he explains, Blackwell Ultra was simply a specialized design aimed at AI inference. “For those customers that wanted the max inference performance, and now actually some of the training capabilities with NVFP4, we had it available as GB300.”

We’re told Nvidia’s next-generation AI accelerators, codenamed Rubin, will follow a similar trend by offering some devices that offer a blend of hyper-precise and low-precision data types, and others optimized primarily for AI applications.

“For cases where we can provide a version of the architecture which maxes out in one direction and maybe it doesn’t have the FP64, we’ll do that too.”

Nvidia has already announced one such chip with Rubin CPX, which is specifically designed to offload LLM inference prefill operations from the GPUs, speeding up prompt processing and token generation in the process.

Buck’s vision for AI accelerated supercomputing is already catching on with national research labs and academic institutions around the world.

In the last year Nvidia has won more than 80 new supercomputing contracts totaling 4,500 exaFLOPS of AI compute. The latest of these is the Texas Advanced Computing Center’s (TACC) Horizon supercomputer.

Due to come online in 2026, the machine will comprise 4,000 Blackwell GPUs plus 9,000 of Nvidia’s next-gen Vera CPUs. The resulting system will pack 300 petaFLOPS of FP64 compute or 80 exaFLOPS of AI compute (FP4).

Horizon will support the simulation of molecular dynamics to advance research into viruses, explore the formation of stars and galaxies, and map seismic waves to provide advanced warning in the advance of earthquakes. ®