Doudna is a new supercomputer being built at Lawrence Berkeley National Laboratory in Berkeley, California.
“It will advance scientific discovery from chemistry to physics to biology and all powered by — unleashing this power — of artificial intelligence,” says US energy secretary Chris Wright.
Also known as NERSC-10, Doudna is named for Nobel laureate and CRISPR pioneer Jennifer Doudna. The next-generation system is designed not just for speed but for impact.
Powered by Dell Technologies infrastructure with the Nvidia Vera Rubin architecture, and set to launch in 2026, Doudna is tailored for real-time discovery across the US Department of Energy’s most urgent scientific missions.
Unlike traditional systems that operate in silos, Doudna merges simulation, data and AI into a single seamless platform.
“The Doudna supercomputer is designed to accelerate a broad set of scientific workflows,” says NERSC director Sudip Dosanjh. “Doudna will be connected to DOE experimental and observational facilities through the Energy Sciences Network (ESnet), allowing scientists to stream data seamlessly into the system from all parts of the country and to analyze it in near real time.”
It’s engineered to empower over 11 000 researchers with almost instantaneous responsiveness and integrated workflows, helping scientists explore bigger questions and reach answers faster than ever.
“We’re not just building a faster computer,” said Nick Wright, advanced technologies group lead and Doudna chief architect at NERSC. “We’re building a system that helps researchers think bigger and discover sooner.”
Here’s what Wright expects Doudna to advance:
- Fusion energy: Breakthroughs in simulation that unlocks clean fusion energy.
- Materials science: AI models that design new classes of superconducting materials.
- Drug discovery acceleration: Ultrarapid workflow that helps biologists fold proteins fast enough to outpace a pandemic.
- Astronomy: Real-time processing of data from the Dark Energy Spectroscopic Instrument at Kitt Peak to help scientists map the universe.
Doudna is expected to outperform its predecessor, Perlmutter, by more than 10-times in scientific output, all while using just 2-time to 3-times the power.
This translates to a 3-times to 5-times increase in performance per watt, a result of innovations in chip design, dynamic load balancing and system-level efficiencies.
Doudna will power AI-driven breakthroughs across high-impact scientific fields nationwide. Highlights include:
- AI for protein design: David Baker, a 2024 Nobel laureate, used NERSC systems to support his work using AI to predict novel protein structures, addressing challenges across scientific disciplines.
- AI for fundamental physics: Researchers like Benjamin Nachman are using AI to “unfold” detector distortions in particle physics data and analyze proton data from electron-proton colliders.
- AI for materials science: A collaboration including Berkeley Lab and Meta created “Open Molecules 2025,” a massive dataset for using AI to accurately model complex molecular chemical reactions. Researchers involved also use NERSC for their AI models.
Doudna isn’t a standalone system. It’s an integral part of scientific workflows. DOE’s ESnet will stream data from telescopes, detectors and genome sequencers directly into the machine with low-latency, high-throughput Nvidia Quantum-X800 InfiniBand networking.
This critical data flow is prioritised by intelligent quality-of-service mechanisms, ensuring it stays fast and uninterrupted, from input to insight.
This will make the system incredibly responsive. At the DIII-D national fusion ignition facility, for example, data will stream control-room events directly into Doudna for rapid-response plasma modeling, so scientists can make adjustments in real time.
“We used to think of the supercomputer as a passive participant in the corner,” Wright says. “Now it’s part of the entire workflow, connected to experiments, telescopes, detectors.”
Doudna supports traditional HPC, AI, real-time streaming and even quantum workflows.
The Mayall 4-Meter Telescope, which will be home to the Dark Energy Spectroscopic Instrument, seen at night at Kitt Peak National Observatory.
This includes support for scalable quantum algorithm development and the codesign of future integrated quantum-HPC systems, using platforms like Nvidia CUDA-Q.
All of these workflows will run on the next-generation Nvidia Vera Rubin platform, which will blend high-performance CPUs with coherent GPUs, meaning all processors can access and share data directly to support the most demanding scientific workloads.
Researchers are already porting full pipelines using frameworks like PyTorch, the Nvidia Holoscan software development kit, TensorFlow, Nvidia cuDNN and Nvidia CUDA-Q, all optimized for the system’s Rubin GPUs and Nvidia NVLink architecture.
Over 20 research teams are already porting full workflows to Doudna through the NERSC Science Acceleration Program, tackling everything from climate models to particle physics.