What Is HPC?_
High-Performance Computing uses clusters of processors (CPUs and/or GPUs) to solve complex computational problems that require massive parallel processing. In the AI era, HPC and AI infrastructure have converged: the same GPU clusters used for scientific simulation are now used for training large language models. HPC facilities have some of the most demanding infrastructure requirements in computing.
Technical Details
HPC systems are characterized by their aggregate computing capability, measured in FLOPS (floating-point operations per second). Modern GPU-based HPC systems achieve petaFLOPS to exaFLOPS performance levels. HPC infrastructure shares many characteristics with AI training infrastructure: high-speed interconnects for inter-node communication, parallel storage systems for data throughput, liquid cooling for thermal management, and specialized facility design for power and density. The convergence of HPC and AI means that infrastructure built for one workload type can often serve both, though the network traffic patterns differ (HPC often involves more structured, predictable communication patterns than AI training).
How Leviathan Systems Works with HPC
Leviathan Systems deploys GPU infrastructure for both HPC and AI training workloads, with experience across NVIDIA platforms used in national laboratories, research institutions, and commercial AI companies.