The exascale offensive: America's race to rule AI HPC
Feature A silent arms race is accelerating in the world's most advanced laboratories. While headlines focus on chatbots and consumer AI, the United States is orchestrating something far more consequential: a massive expansion of supercomputing power that may reshape the future of science, security, and technological supremacy.
The stakes couldn't be higher. Across three fortress-like national laboratories, a new generation of machines is rising – systems so powerful they dwarf anything that came before. These aren't just faster computers. They are weapons in a global war where leadership in artificial intelligence is expected to determine which nations shape the 21st century.
The Department of Energy's audacious plan will deploy nine cutting-edge supercomputers across the Argonne, Oak Ridge, and Los Alamos National Laboratories through unprecedented public-private partnerships. The scale is staggering: systems bristling with hundreds of thousands of next-generation processors, capable of quintillions of calculations per second, purpose-built to unlock AI applications – and to ensure America's rivals don't get there first.
At Argonne, two flagship systems named Solstice and Equinox will anchor what may become the world's most formidable AI computing infrastructure. Solstice alone will harness 100,000 Nvidia Blackwell GPUs, creating the largest AI supercomputer in the DOE's network – a silicon leviathan designed to push the boundaries of what's computationally possible.
In addition to these, Argonne will include three smaller systems – Minerva, Tara, and Janus – aimed at specialized tasks. Minerva and Tara will focus on AI-based predictive modeling, while Janus is intended to support workforce development in AI. Together, these five systems at Argonne will form a multi-tier computing ecosystem to serve applications from material discovery and climate modeling to AI-driven experimental design.
Oak Ridge, which is home to the Frontier supercomputer, will receive two AI-accelerated machines built with AMD and HPE technology. The first is Lux, an AI cluster powered by AMD Instinct MI355X GPUs and EPYC CPUs, scheduled for deployment in early 2026. The system will provide a secure, open AI software stack to tackle urgent research priorities from fusion energy simulations and fission reactor materials to quantum science.
The second system, Discovery, will be based on the HPE Cray Supercomputing GX5000, slated for 2028, and will use next-generation AMD hardware. It will include EPYC Venice processors and Instinct MI430X GPUs. Discovery is expected to significantly outperform Frontier (the world's second-fastest) with performance well beyond one exaFLOPS.
Finally, Los Alamos will get two supercomputers focused on national security science, in partnership with HPE and Nvidia. The purpose-built AI systems are named Mission and Vision for nuclear security modeling and simulation. Mission will be dedicated to atomic stockpile stewardship, explicitly intended to assess and improve nuclear weapons reliability without live testing. On the other hand, Vision will support a broad range of open science projects in materials science, energy modeling, and biomedical research.
With the rapid development, a major question is why the US is ramping up supercomputing power at this time. A clear driver is the explosive growth of AI and the need for research infrastructure to support it. AI has been a state priority for both the Trump and Biden administrations, and these supercomputers reflect that focus.
AI at the heart of the initiative
This surge aligns directly with Washington's AI Action Plan and its renewed emphasis on "AI-enabled science." Beyond chasing speed, the US is positioning supercomputers as the backbone of national AI infrastructure, necessary for climate modeling, materials discovery, healthcare simulation, and defense.
Modern science generates enormous datasets, from particle accelerators to genomic research, and AI algorithms become far more potent when paired with faster supercomputers. The DOE's Office of Science notes that AI is an ideal tool for extracting insights from big data, and that it becomes more useful as the speed and computational power of today's supercomputers grow.
New machines like Oak Ridge's Discovery and Lux are designed to leverage AI for science, expanding America's leadership in AI-powered scientific computing. These systems integrate traditional simulation with machine learning, allowing researchers to train frontier AI models for open science and analyze data at unprecedented speeds.
The result is a step change in capability. Complex problems, from climate modeling to biomedical research, can be tackled with AI-enhanced simulations, accelerating the cycle from hypothesis to discovery. This directly supports the AI Action Plan's call to invest in AI-enabled science.
There is also a sense of urgency arising from international competition. US policymakers view leadership in AI and supercomputing as a strategic asset, highlighting economic competitiveness, scientific leadership, and national security. The Trump administration has been vocal about winning the AI race and not ceding ground to rival nations.
Staying ahead in the global supercomputing race
There's also a geopolitical reason why. Other major powers are rapidly expanding their own HPC infrastructure. China, for instance, has been a formidable player in supercomputing for more than a decade. By 2020-21, reports indicate China built at least two exascale-class supercomputers, often referenced as an upgraded Sunway system and the Tianhe-3 system.
These supercomputers achieved exascale performance before the US did, without public benchmarking. China has stopped submitting its top supercomputers to the international TOP500 list, so their true capabilities are somewhat opaque. US officials and experts believe this is partly due to trade tensions and sanctions. The details could expose China's systems to US export controls or give away strategic information.
Regardless, it's understood that China is at technological parity in HPC and possibly even ahead in some aspects. This competitive pressure is a primary rationale for US policymakers to make sustained HPC investment. The US's response has been twofold: out-compute China by fielding superior machines (hence the drive for exascale and beyond) and slow China's progress via export controls on advanced semiconductors.
Europe, meanwhile, has been organizing a collective effort to boost its HPC capabilities through the EuroHPC Joint Undertaking. In September, Europe inaugurated its first exascale supercomputer, Jupiter in Germany, which received roughly €500 million of joint investment and runs on Nvidia's Grace Hopper platform.
By commissioning nine supercomputers essentially at once, the US is trying not only to maintain its lead but to widen it. As of now, DOE machines hold the top three spots in the world TOP500 rankings. The forthcoming systems – Solstice, Equinox, Discovery, Lux, and Vision – are intended to strengthen that dominance in both traditional HPC and AI-specific computing for years to come.
The global HPC landscape in 2025 is one of rapid advancement and one-upmanship. China likely has multiple exascale systems but keeps them under wraps, while the US has publicly claimed the fastest benchmarks and is now pivoting to AI-centric upgrades. By infusing its new supercomputers with AI capabilities and deploying them more quickly through partnerships, the US aims to set the pace of innovation.
Technological lead: Beyond exascale
The new supercomputers are significant not just for the number of systems or their geopolitical context, but also for the latest technologies they introduce. The availability of the next-generation hardware has dramatically boosted performance. Both Nvidia and AMD are rolling out new chips around 2025-26 that promise order-of-magnitude gains in AI and simulation capacity. The DOE is seizing the moment to incorporate these into national lab systems.
We are witnessing the rise of a new generation of supercomputers that go beyond traditional CPUs and GPUs, incorporating specialized hardware and novel architectures optimized for AI.
One headline innovation is the Nvidia Vera Rubin platform, which will debut on the Los Alamos machines and may later be deployed at other labs. This platform splits the namesake across a CPU (Nvidia Vera) and a GPU (Nvidia Rubin), which represents the company's first foray into designing its own CPU for HPC alongside its GPUs.
By integrating these with Quantum-2/X800 InfiniBand networks at massive scale, the Vera Rubin systems are expected to handle mixed workloads far more efficiently. For example, they will use lower numerical precision where possible to get a huge 2,000-plus exaFLOPS AI throughput, without sacrificing the high precision needed for physics in other parts of the calculation.
On the AMD side, Oak Ridge's Discovery system offers a peek into AMD's HPC technology roadmap. It will use AMD's Venice EPYC processors and Instinct MI430X GPUs, which are not yet on the market and presumably two generations beyond today's hardware. AMD has been focusing on heterogeneous computing as well; its Instinct MI300 series already combines CPU and GPU in a single package, and the future MI400 series might push this further.
The timing of America's supercomputing push is no coincidence. It directly reflects the imperatives laid out in the country's AI strategy. From AI-enabled science breakthroughs to national security advantages, and from infrastructure building to workforce development, the new DOE supercomputers are accelerators for each pillar of the US AI Action Plan.
As HPC networks grow more intelligent and more powerful, we may look back on this moment as when the era of exascale truly took off into the era of AI-driven exa-intelligence. Assuming the bubble doesn't burst. ®
