mlsanityData to 7 Oct 2026

Cost-Optimal Foundation Model Deployment Portfolio for Transportation Management

arXiv 2607.13239 ↗First posted 14 Jul 2026cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 14 Jul 2026.

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20 of 43 references.

References · 20

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  4. 2025Qwen2.5-VL Technical Reportcited by 5,418
  5. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,534
  6. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  7. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,107
  8. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 791
  9. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 633
  10. 2024Hybrid LLM: Cost-Efficient and Quality-Aware Query Routingcited by 354
  11. 2023Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modelingcited by 94
  12. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 85
8 more
  1. 2023LLMLight: Large Language Models as Traffic Signal Control Agentscited by 73
  2. 2024A Unified Approach to Routing and Cascading for LLMscited by 71
  3. 2023Open-TI: Open Traffic Intelligence with Augmented Language Modelcited by 38
  4. 2024M'elange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneitycited by 30
  5. 2024Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Taskscited by 15
  6. 2025A Cost-Benefit Analysis of On-Premise Large Language Model Deployment: Breaking Even with Commercial LLM Servicescited by 15
  7. 2025ThriftLLM: On Cost-Effective Selection of Large Language Models for Classification Queriescited by 14
  8. 2024A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentialitycited by 8

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