HIGH-PERFORMANCE COMPUTING FOR SCALING LARGE-SCALE LANGUAGE AND DATA MODELS IN ENTERPRISE APPLICATIONS

Abstract

This quantitative study investigates the efficiency, scalability, and computational performance of high-performance computing (HPC) infrastructures in training and operationalizing large-scale language and data models within enterprise environments. HPC, characterized by parallel processing, distributed memory architectures, and high-bandwidth interconnects, serves as the backbone for scaling advanced artificial intelligence (AI) workloads. The research analyzed 120 performance observations across GPU-, TPU-, ASIC-, and hybrid-based clusters, focusing on metrics including throughput-per-dollar, inference latency, energy-per-token, time-to-recovery (TTR), and mean-time-between-failure (MTBF). Statistical analyses, encompassing correlation, regression, and structural equation modeling, revealed that scaling strategy and compute capacity were the most influential predictors of computational throughput (β = .45, p < .001 and β = .32, p < .001, respectively). Hybrid HPC configurations achieved optimal trade-offs between speed, reliability, and cost efficiency, outperforming homogeneous systems in energy proportionality and fault tolerance. The inclusion of sustainability parameters—such as energy optimization and adaptive checkpointing—significantly improved model explanatory power (ΔR² = .08, p < .001), demonstrating that sustainable computing practices reduce both operational costs and energy consumption. Reliability modeling confirmed that higher thermal efficiency and optimized interconnects enhanced system uptime and reduced recovery times. Validity testing (Cronbach’s α = .90; AVE > .63) established robust construct integrity across performance dimensions, while regression diagnostics verified predictor independence (VIF < 2.5). The findings conclude that performance, cost, and sustainability form an interdependent triad defining enterprise HPC efficiency. The study proposes a quantitative framework integrating energy optimization, parallel scaling, and reliability modeling to guide enterprise decision-making in AI deployment. It emphasizes that HPC-enabled AI scaling is not solely a technical enhancement but a strategic enabler of operational predictability, energy discipline, and long-term cost stability—advancing both computational science and enterprise digital transformation.