Nvidia has formally launched the Vera Rubin platform, a combination CPU and GPU platform billed as a major step forward in the convergence of artificial intelligence and high-performance computing (HPC) for scientific research.

Announced at the ISC High Performance 2026 conference in Hamburg, the new platform combines Nvidia’s Vera CPUs, Rubin GPUs, networking technologies, and software stack into what the company describes as a rack-scale supercomputer. Nvidia is targeting scientific workloads, from climate modeling and computational fluid dynamics to quantum chemistry, energy exploration, and large data center operators.

“Nvidia’s roots are firmly planted in scientific computing, and native FP64 precision remains absolutely vital for accurate fluid dynamics, climate modeling, and geoscience,” said Dion Harris, senior director of HPC and AI factory solutions at Nvidia, on a conference call. “We are committed to maintaining that support moving forward.

At the heart of the platform is a tightly integrated architecture combining Nvidia Rubin GPUs and Vera CPUs linked through NVLink-C2C interconnects, ConnectX-9 SuperNICs, and BlueField-4 DPUs. The systems are built around direct liquid cooling and support up to 144 GPUs in a single rack.

Nvidia claims a fully configured Vera Rubin system can deliver more than seven exaflops of AI performance for scientific workloads alongside five petaflops of native double-precision (FP64) computing performance. That would put a Rubin system well ahead of the top supercomputers TOP500 ranking. Updated rankings are due later this week.

The Vera Rubin architecture increases memory bandwidth by 2.8 times compared to Blackwell, the previous generation GPU. “We are projecting up to four times performance boosts for memory-bound fluid dynamic applications,” said Harris. “With Rubin, we are ensuring that the fundamental mathematical workloads driving scientific discovery run faster, more efficiently, and with greater precision than ever before.”

The new platform is designed to support both traditional HPC simulations and emerging AI-driven scientific applications. Researchers will be able to train foundation models, deploy surrogate models, run simulations and perform real-time data analysis on a single infrastructure.

“AI is shifting from a tool that simply answers questions to an autonomous system that executes complex tasks,” said Harris. “Early data shows [agentic AI] increases simulation demand by up to ten times.”

Nvidia also announced that several leading research institutions announced plans to build next-generation systems based on the new architecture. The Leibniz Supercomputing Centre (LRZ) in Germany will deploy Vera Rubin in its upcoming Blue Lion supercomputer, scheduled to enter service in 2027.

Blue Lion is a second-generation exascale-class HPE Cray system, and it is expected to deliver approximately 30 times the computing power of LRZ’s current system, supporting research in astrophysics, environmental science and life sciences.

In the U.S., the National Energy Research Scientific Computing Center (NERSC) will use Vera Rubin technology in Doudna, the next flagship supercomputer for the Department of Energy at Lawrence Berkeley National Laboratory. This system is being built by Dell Technologies and will support large-scale HPC simulations, AI training and data-intensive research.

Meanwhile, Los Alamos National Laboratory has selected Vera Rubin technology for three new supercomputers: Mission, Vision and Veritas. Mission will focus on national security workloads, while Vision will support open scientific research and AI-driven discovery. Veritas is specifically designed to enable agentic AI applications in scientific research, combining Rubin GPUs with standalone Vera CPU partitions.

Vera Rubin NVL4-based systems from Dell and Super Micro were also announced at the event (see related story).