Locational Pricing for Generative-AI Services via Token-Flow Market Clearing

2026-05-09

AI Summary

This work addresses the challenges of geographically distributed and resource-intensive generative AI infrastructure by proposing a network-constrained token-flow market model that jointly optimizes the routing and processing of computational tasks. The model achieves load balancing and regional pricing while respecting compute capacity and bandwidth constraints. It innovatively introduces a dual-variable-based marginal service pricing mechanism that is both region- and load-specific, and extends the formulation to explicitly model physical data transmission, thereby disentangling bandwidth congestion rents. Combining linear programming with network flow optimization and duality theory, empirical analysis reveals that in a 5-node U.S. scenario, four backbone links become saturated; tightening the latency bound from 100 ms to 15 ms increases local prices by 117%; and experiments on a 20-node topology validate the efficacy of scheduling based on marginal cost ordering.

Abstract

GenAI services are in an early yet fast expanding phase. Providers compete on model capability and service quality, while the underlying infrastructure remains expensive and heterogeneous across regions, workloads, and compute assets. If these services diffuse into routine daily use, the relevant engineering problem becomes not only better models but also efficient dispatch on a geographically distributed AI service infrastructure. To address this, we formulate a network-constrained token-flow market that clears AI workloads across compute nodes and communication links. The baseline model is a linear program that co-optimizes routing and processing subject to compute-capacity and bandwidth constraints; its dual variables define location- and workload-specific marginal service prices. We further introduce a transfer-aware extension that prices data movement in physical units and isolates bandwidth congestion rents. In a 5-node U.S. case study, the transfer-aware model uncovers four saturated backbone links and raises total operating cost by 2.7\% relative to the token-equivalent baseline, while tightening the chatbot latency limit from 100~ms to 15~ms increases one locational price by 117\%. A 20-node scale-up exhibits the same merit-order dispatch logic and becomes infeasible once demand exceeds aggregate capacity. These results suggest that locational pricing is a useful organizing principle for operating an emerging AI service infrastructure and, over time, for designing competitive markets around it.

Problem

• Locational Pricing
• Generative AI Services
• Token-Flow Market
• Geographically Distributed Infrastructure
• Workload Dispatch

Innovation

• locational pricing
• token-flow market
• generative AI infrastructure
• network-constrained optimization
• transfer-aware pricing