AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid
TL;DR
This article presents a joint connectivity-compute view of AI-native 6G, and introduces AI Grid as a distributed AI infrastructure platform for placing workloads according to latency, cost, policy, and service-level constraints.
Abstract
The sixth-generation (6G) of mobile networks will be shaped not only by artificial intelligence (AI)-enabled network automation and optimization, but also by the need to serve AI as a 6G-native workload. Emerging AI services introduce traffic and compute demands that differ from conventional mobile broadband. Their user experience depends on how quickly useful information is delivered, how bursty and asymmetric multimodal flows are handled, and where inference, retrieval, caching, and content processing are executed. This article presents a joint connectivity-compute view of AI-native 6G. We first characterize representative AI service traffic in terms of uplink/downlink throughput skew, burstiness, and token latency. Next, we discuss how fifth-generation extended reality awareness mechanisms can evolve toward AI traffic characteristics awareness in 6G. Finally, we introduce AI Grid as a distributed AI infrastructure platform for placing workloads according to latency, cost, policy, and service-level constraints. Together, AI-aware connectivity and AI Grid enable 6G as a distributed intelligence platform.