Structural optimization principles for edge AI in motorsport telemetry

In motorcycle motorsport, engineering decisions are constrained by mass, stiffness, fatigue life, and thermal margins. In a comparable way, deploying large language models (LLMs) for edge or trackside assistance is limited by memory capacity, bandwidth, latency, and power. This paper presents a systems-engineering framework that relates structural lightweighting and post-training quantization (PTQ) through second-order sensitivity. In mechanics, safe material removal is governed by the stiffness matrix [Formula: see text]; in neural networks, safe precision reduction is governed by curvature information encoded in the loss Hessian [Formula: see text]. To ground this analogy, we combine a structural case study based on topology optimization of a Formula Student upright with an edge-oriented evaluation of low-precision LLM deployment using established PTQ pipelines (GPTQ and AWQ). The structural example illustrates how sensitivity-guided material removal preserves load paths and mechanical integrity, while the LLM study examines how sensitivity-aware quantization can reduce computational footprint while maintaining operational usefulness in telemetry-related inference. To assess deployment feasibility, we define two practical metrics: a Digital Factor of Safety ([Formula: see text]), based on a perplexity-derived integrity threshold, and Intelligence-per-Watt (IPW), which captures energy-aware inference efficiency. For a 32B-class operating point, INT4 weight-only deployment reduces memory footprint from 61 GB to 18 GB, improves throughput from 26.0 to 69.9 tok/s, and lowers measured power from 295 W to 165 W while remaining within the proposed integrity envelope. The contribution is methodological rather than algorithmic: we do not introduce a new quantization method, but a reproducible framework for analyzing when low-precision LLM deployment becomes feasible under motorsport-inspired resource constraints. The results support the practical relevance of stiffness-informed digital lightweighting, while also highlighting that the evidence is limited to the structural and computational case studies considered here.