mlsanityData to 7 Oct 2026

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

arXiv 2512.01039 ↗First posted 30 Nov 2025cs.DC cs.LG cs.NIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 30 Nov 2025.

Builds on

16 of 30 references.

References · 16

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2024A Survey on Efficient Inference for Large Language Modelscited by 197
  3. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  4. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 89
  5. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  6. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  7. 2024LLM Inference Serving: Survey of Recent Advances and Opportunitiescited by 46
  8. 2024A Comprehensive Study on Quantization Techniques for Large Language Modelscited by 34
  9. 2024Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6Gcited by 30
  10. 2024Efficient Training of Large Language Models on Distributed Infrastructures: A Surveycited by 26
  11. 2025SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Mergingcited by 25
  12. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
4 more
  1. 2022SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environmentscited by 11
  2. 2023Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiencycited by 8
  3. 2025AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environmentscited by 6
  4. 2024R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Modelscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Predictioncited by 1