Enterprise Distributed Infrastructure for Scalable Generative AI Workloads

Abstract

Enterprise deployment of generative artificial intelligence (GenAI) at production scale exposes a category of infrastructure problems that classical data-center engineering does not anticipate. Unified graphics processing unit (GPU) pools suffer from chronic underutilization and unpredictable tail latency, while naive multi-tier partitioning sacrifices elasticity to obtain predictability. This article develops a dynamic multi-tier compute architecture that reallocates GPU capacity across inference, training, and operational tiers on a two-to-four-hour prediction horizon. The architecture is evaluated against unified and statically partitioned baselines using an eighteen-month observational deployment profile and published industry benchmarks. Dynamic allocation reduces infrastructure cost by 40–55 percent relative to unified pools while improving p95 latency consistency by 25–35 percent; GPU utilization rises from a 62–68 percent unified baseline to 78–81 percent under dynamic control, exceeding the 68–78 percent utilization band reported in vendor benchmarks. A complementary multi-vector-store knowledge fabric with intelligent query routing reduces retrieval latency by 40–55 percent and increases semantic recall to 99.2 percent, while a twelve-region active-active deployment with content-aware routing reduces p95 latency by 72 percent and compresses tail variance sixfold. The article formalizes a latency-cost Pareto frontier that lets enterprise operators reason explicitly about where to sit on the trade-off curve rather than pursuing unbounded cost reduction. Results hold across transformer families spanning 7 billion to 405 billion parameters and across mixed inference-training-operational workload profiles.