Nvidia has committed $1 billion to help secure American scientific computing dominance, as it prepares to fortify the US government's computing arsenal with at least seven AI-optimized supercomputers.
Nvidia on Thursday committed $1 billion, about 1/60th of its quarterly profits, to fund US scientific discovery over the next five years. The commitment, announced at an event in Washington, DC, aims to support research and development of AI (or is it "super intelligence" now?) in fields including quantum computing, healthcare, and energy security.
Nvidia's choice of technologies is not surprising, as it has long aspired to fuse AI and high-performance computing. While LLM training and inference pay Nvidia's bills, the GPU giant has continued to introduce new CUDA libraries specifically for AI-assisted quantum computing, healthcare, drug discovery, and physics simulation.
The initiative is part of the US government's broader Genesis Mission, announced late last year by the Trump administration, which aims to use AI to drive the scientific discoveries needed to ensure the country's continued technological leadership. The program marks Nvidia's return to US supercomputing in a big way. The last flagship Department of Energy (DoE) supercomputers powered by Nvidia, Summit and Sierra, were commissioned as far back as 2018. The A100-based Perlmutter system, launched in 2021, is also notable but fell far short of the older systems.
Nvidia never stopped building supercomputers for the US government, but they tended to be significantly smaller than the massive AMD-based platforms such as Frontier and El Capitan. That changed last year when Nvidia revealed it would supply the US government with no fewer than seven new supercomputers across Argonne, Los Alamos, and Lawrence Berkeley National Laboratories. Argonne will house Solstice, a 100,000-GPU Blackwell-based system built in collaboration with Oracle. The machine is largely aimed at emerging AI workloads, which don't depend on ultra-high-precision computation and can instead get by with 16, eight, or even four bits of precision. That puts Nvidia in a strong position to meet the US government's goals under the Genesis Mission.
Over the past few generations of GPUs, Nvidia has traded much of its accelerators' double-precision FP64 performance for higher throughput at the precisions used by more lucrative AI workloads. For its upcoming Vera Rubin platform, Nvidia relies heavily on FP64 emulation based on the Ozaki scheme, which we looked at in more detail earlier this year. Los Alamos' upcoming Mission and Vision systems will be powered by these same chips and will take advantage of Nvidia's Quantum-X800 InfiniBand. FP64 emulation comes with compromises, but for workloads that are predominantly AI-optimized and only occasionally need higher precision, it can be worthwhile for the additional matrix FLOPS.
While Nvidia is playing a much bigger role in US scientific computing, the DoE's biggest supercomputer, at least for pure FP64, will still be powered by AMD. Oak Ridge National Laboratory's Discovery system, expected to come online in 2029, will be built by HPE's Cray division and feature AMD's FP64-optimized MI430X GPUs and Venice CPUs. Discovery is expected to offer peak theoretical performance of between 3.3 and 8.5 exaFLOPS, depending on whether Oak Ridge gets a facility power upgrade.
Meanwhile, China's LineShine leads the Top500 ranking of the world's most powerful computers. The system debuted this northern spring with 2.2 exaFLOPS measured out of 2.7 exaFLOPS of theoretical performance. With Uncle Sam's nose bloodied by China's return to the ranking, Oak Ridge may not have to fight hard for the extra power.