Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI

• URL: http://arxiv.org/abs/2512.01039v1
• Date: Sun, 30 Nov 2025 19:16:30 GMT
• Title: Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI
• Abstract summary: We introduce a framework in which both the spatial placement and internal segmentation of foundation models are elevated to runtime-resolved constructs.<n>We introduce architectural and algorithmic components, along with a representative use case in 6G multi-access edge computing.
• Score: 43.38839324900098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference over large-scale foundation models within heterogeneous edge environments necessitates a fundamentally reconfigurable orchestration substrate. Static partitioning of model layers presumes temporal stability across compute and network resources, which is misaligned with the volatility of real-world deployments. We introduce a framework in which both the spatial placement and internal segmentation of foundation models are elevated to runtime-resolved constructs. The orchestration problem is formalized as a constrained optimization over layer-wise assignments, subject to evolving latency, utilization, and privacy gradients. The framework implements reactive inference composition responsive to infrastructural fluctuations by integrating model-aware capacity profiling with dynamic graph re-partitioning and reallocation. We introduce architectural and algorithmic components, along with a representative use case in 6G multi-access edge computing.

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