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A Techno-Economic Framework for Cost Modeling and Revenue Opportunities in Open and Programmable AI-RAN

Gabriele Gemmi, Michele Polese, Tommaso Melodia

Abstract

The large-scale deployment of 5G networks has not delivered the expected return on investment for mobile network operators, raising concerns about the economic viability of future 6G rollouts. At the same time, surging demand for Artificial Intelligence (AI) inference and training workloads is straining global compute capacity. AI-RAN architectures, in which Radio Access Network (RAN) platforms accelerated on Graphics Processing Unit (GPU) share idle capacity with AI workloads during off-peak periods, offer a potential path to improved capital efficiency. However, the economic case for such systems remains unsubstantiated. In this paper, we present a techno-economic analysis of AI-RAN deployments by combining publicly available benchmarks of 5G Layer-1 processing on heterogeneous platforms -- from x86 servers with accelerators for channel coding to modern GPUs -- with realistic traffic models and AI service demand profiles for Large Language Model (LLM) inference. We construct a joint cost and revenue model that quantifies the surplus compute capacity available in GPU-based RAN deployments and evaluates the returns from leasing it to AI tenants. Our results show that, across a range of scenarios encompassing token depreciation, varying demand dynamics, and diverse GPU serving densities, the additional capital and operational expenditures of GPU-heavy deployments are offset by AI-on-RAN revenue, yielding a return on investment of up to 8x. These findings strengthen the long-term economic case for accelerator-based RAN architectures and future 6G deployments.

A Techno-Economic Framework for Cost Modeling and Revenue Opportunities in Open and Programmable AI-RAN

Abstract

The large-scale deployment of 5G networks has not delivered the expected return on investment for mobile network operators, raising concerns about the economic viability of future 6G rollouts. At the same time, surging demand for Artificial Intelligence (AI) inference and training workloads is straining global compute capacity. AI-RAN architectures, in which Radio Access Network (RAN) platforms accelerated on Graphics Processing Unit (GPU) share idle capacity with AI workloads during off-peak periods, offer a potential path to improved capital efficiency. However, the economic case for such systems remains unsubstantiated. In this paper, we present a techno-economic analysis of AI-RAN deployments by combining publicly available benchmarks of 5G Layer-1 processing on heterogeneous platforms -- from x86 servers with accelerators for channel coding to modern GPUs -- with realistic traffic models and AI service demand profiles for Large Language Model (LLM) inference. We construct a joint cost and revenue model that quantifies the surplus compute capacity available in GPU-based RAN deployments and evaluates the returns from leasing it to AI tenants. Our results show that, across a range of scenarios encompassing token depreciation, varying demand dynamics, and diverse GPU serving densities, the additional capital and operational expenditures of GPU-heavy deployments are offset by AI-on-RAN revenue, yielding a return on investment of up to 8x. These findings strengthen the long-term economic case for accelerator-based RAN architectures and future 6G deployments.

Paper Structure

This paper contains 13 sections, 19 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. RAN Architectures and Cost Model
  4. Baseband Capacity Metric
  5. Platform Comparison
  6. RAN Demand and Deployment Model
  7. LLM Demand Model
  8. Revenue Model for AI-on-RAN
  9. Scenario-Based Evaluation
  10. GPUs Allocation
  11. LLM Revenues
  12. ROI Analysis
  13. Conclusion

Figures (9)

  • Figure 1: system architecture and role of AI-for-RAN (increasing RAN efficiency and performance with AI), AI-on-RAN (value-added edge services co-deployed with the RAN and with access to RAN data and telemetry), and AI-and-RAN (orchestration and management to support the coexistence of RAN, AI-for-RAN, and AI-on-RAN).
  • Figure 2: for 10 Gbps aggregate peak throughput over 10 years.
  • Figure 3: Weekly usage patterns for and workloads showing slightly complementary demand cycles.
  • Figure 4: Hourly allocation at deployment ($w=0$) for clusters sized for Scenario 1 (top) and Scenario 2 (bottom). The total deployed capacity $G_\mathrm{total}$ is split at each hour between processing and inference.
  • Figure 5: Weekly-averaged allocation ( plus ) over the 10-year horizon. Top: Scenario 1. Bottom: Scenario 2 .
  • ...and 4 more figures