AGENTIC-SYSTEMSJuly 16, 20261 min read

Cost-Optimal Foundation Model Deployment Portfolio for Transportation Management

A new study introduces the FMDP framework to help transportation management centers optimize AI deployment costs by up to 97%. By dynamically balancing open-source and closed-source APIs, the solution addresses the trade-offs between performance, latency, and hardware budgets.

Computer Science > Artificial Intelligence

Title:Cost-Optimal Foundation Model Deployment Portfolio for Transportation Management

View PDF HTML (experimental)Abstract:Foundation models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used for transportation management center (TMC) tasks such as anomaly detection, incident reporting, and traveler information. Deploying multiple such models across TMC functions raises a portfolio question: which model should serve each function, in which deployment mode, and under what shared hardware budget? We formulate this as the Foundation Model Deployment Portfolio (FMDP) problem, a mixed-integer program minimizing total cost of ownership (TCO) subject to per-function quality, latency, and safety constraints over shared GPU capacity. We prove the problem NP-hard by reduction from the 0-1 knapsack problem and propose a polynomial-time greedy heuristic. In an illustrative case study with five TMC functions and 19 candidate (model, mode) pairs, FMDP identifies a mixed portfolio costing $34/mo (97% below the cheapest feasible all-closed-API baseline) by routing four functions to open-source APIs and the one function whose quality floor no open-source model meets to a closed API. Break-even analysis shows that on-premise GPU investment becomes reasonable only above approximately 309 vision queries/hour or if API prices double.

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Source: arXiv cs.AI Recent

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