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.

翻译：5G网络的大规模部署并未给移动网络运营商带来预期的投资回报，这引发了业界对未来6G网络部署经济可行性的担忧。与此同时，人工智能推理与训练工作负载的激增正在挤压全球计算资源容量。AI-RAN架构中，基于图形处理单元加速的无线接入网平台在非高峰时段与AI工作负载共享闲置算力，为提升资本效率提供了潜在路径。然而，此类系统的经济论证尚未得到充分验证。本文通过整合多项公开数据——包括异构平台（涵盖配备信道编码加速器的x86服务器与新型GPU）上5G Layer-1处理性能基准测试、真实业务流量模型以及面向大语言模型推理的AI服务需求画像——对AI-RAN部署展开技术经济分析。我们构建了联合成本-收益模型，用以量化基于GPU的RAN部署中可用的闲余计算容量，并评估将闲置算力租赁给AI租户的经济回报。研究结果表明，在涵盖令牌折旧、动态需求变化及不同GPU服务密度等多重场景下，GPU密集型部署所增加的资本支出与运营成本可通过AI-on-RAN收益得到补偿，并实现最高达8倍的投资回报率。这些发现强化了基于加速器的RAN架构及未来6G部署的长期经济可行性。