About our Resources

ALCF resources include leadership-class supercomputers, visualization clusters, advanced data storage systems, high-performance networking capabilities, and a wide variety of software tools and services to help facility users achieve their science goals.

Machine Summary Key Details
Aurora The Aurora supercomputer provides researchers with a powerful platform to pursue scientific discovery through exascale simulation, AI, and data capabilities. Users that wish to run their project on Aurora need an allocation award. You can learn more about our allocation awards here.
Polaris ALCF’s Polaris supercomputer enables researchers to tackle complex challenges in science and engineering with advanced simulation, AI, and analysis capabilities. Users that wish to run their project on Aurora need an allocation award. You can learn more about our allocation awards here.
ALCF AI Testbed The ALCF AI Testbed provides an infrastructure for the next-generation of AI-accelerator machines. It aims to help evaluate the usability and performance of machine learning-based high-performance computing applications running on these accelerators Systems available to users with an allocation award include Cerebras CS-3, SambaNova Dataflow, Metis (SambaNova SN40L), GroqRack, and Graphcore Bow Pod64.
Sophia Sophia is an NVIDIA DGX A100-based system built for training AI datasets and AI integration to advance scientific discovery.  
Crux Crux enables users to execute pre- and post-processing tasks including experimental data analysis, not yet GPU-enabled, helping them to gain deeper insights into simulations and data generated on the facility’s supercomputers.  
Minerva Minerva is designed to accelerate AI inference - the process of using a trained AI model to make predictions, identify patterns or generate insights from new data. Coming online in 2026
Janus Janus will support the development of the next-generation AI workforce, providing them with the capabilities needed to excel in AI-driven research and applications. Coming online in 2026