Generative AI-based Agentic Applications: A Bifurcated Lifecycle Analysis of Training and Inference Paradigms

Abstract

This article explores the bifurcated lifecycle of generative AI applications, examining the distinct computational regimes of training and inference phases. The training phase encompasses data acquisition protocols, parameter optimization techniques, and computational requirements for developing large language models and multimodal systems. The inference phase addresses autoregressive decoding mechanisms, latency optimization strategies, throughput maximization approaches, and performance benchmarking across deployment frameworks. Technical challenges, including memory constraints, network architecture bottlenecks, and hardware-software co-design imperatives, are analyzed, along with cost-performance tradeoffs between training and inference workloads. Industry trends reveal a transition from training-dominant to inference-focused hardware, the emergence of application-specific integrated circuits and specialized chiplets, and vertical integration of hardware development pipelines by major technology providers. These developments reshape computational infrastructure requirements across technology sectors while establishing frameworks for next-generation generative AI deployments.