Massive AI Model Orchestration for 6G Networks: Architecture, Optimization, and Energy-Efficient Deployment

Sixth-generation (6G) networks will serve large pre-trained artificial intelligence (AI) foundation models from cloud, edge and device tiers, but placing, compressing and scheduling them under latency, energy and accuracy constraints remains open. This paper proposes Massive AI Model Orchestration (MAIMO), a three-tier framework that manages model residency and request routing inside the radio access and core networks. The orchestration problem is formulated as a mixed-integer multi-objective program for which a Pareto-optimal solution is proved to exist and whose offline placement-routing version is shown to be NP-hard. An online controller couples a bidirectional long short-term memory (BiLSTM) traffic predictor with proximal policy optimization (PPO). The evaluation is simulation-only, in a purpose-built system-level simulator calibrated to standardized urban-macrocell channel models. Over 20 paired replications, MAIMO halves the mean end-to-end latency of cloud-only service (11.3 ms against 22.7 ms), serves ultra-reliable low-latency vehicle-to-everything requests in 2.1 ms on average with warm caches and attains the lowest service-level-agreement violation rate among schemes meeting the accuracy floor, while tying deep Q-network and contextual-bandit controllers on mean latency. A transparent analytic energy model gives 5.46 J per inference against 16.73 J for cloud-only service, and constrained carbon-aware workload shifting reduces operational carbon by 27.2%.