aceMassive 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…
In simulation, the MAIMO orchestration framework roughly halves end-to-end latency and cuts energy per inference to about a third of cloud-only serving, with carbon-aware shifting lowering operational carbon.
The evaluation is simulation-only, using a purpose-built system-level simulator, and energy figures come from an analytic model, so real deployment results are not demonstrated.
Evidence
- Peer-reviewedFuture Internet2026-10-20
How should this claim be treated?
Truvace Impact Record TRV-2026-2122, v1: “Massive AI Model Orchestration for 6G Networks: Architecture, Optimization, and Energy-Efficient Deployment.” Truvace, 2026-10-21. /record/TRV-2026-2122 (accessed at citation time). sha256 8419bb18be5d708c…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2122 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.