Peak Workload Engineering for Generative AI Platforms: A Framework for Sustained Performance Validation at Scale

Peak Workload Engineering (PWE) is a structured methodology for sustained performance validation of Generative AI (GenAI) platforms operating under production-scale workloads. Unlike traditional web applications, GenAI systems exhibit highly variable token throughput, non-deterministic inference latency, GPU memory pressure, and session-dependent context amplification that conventional benchmarking frameworks fail to characterize adequately. This record presents the PWE framework, integrating session-based load modeling, token-economy metrics, GPU-aware bottleneck analysis, and causality-driven observability into a unified validation lifecycle. The framework defines seven primary GenAI-specific bottleneck classes, proposes a five-tier observability architecture (T1–T5), and formalizes workload characterization using a multidimensional workload tuple and Sustainable Peak Operating Point (SPOP) model. PWE is designed to be reproducible, toolchain-agnostic, and extensible to emerging architectures including retrieval-augmented generation (RAG), mixture-of-experts (MoE), and multi-agent orchestration systems. The contribution is methodological in nature, providing engineering teams with a systematic foundation for capacity planning, workload validation, and operational reliability analysis for large-scale AI inference infrastructure. This pre-print is accompanied by configuration schemas, observability dashboard templates, and the open-source PWE Causality Engine. Practitioners are encouraged to reproduce, validate, and extend the framework in their own deployment contexts.