mlsanityData to 7 Oct 2026

Mobile Edge Intelligence for Large Language Models: A Contemporary Survey

arXiv 2407.18921 ↗First posted 9 Jul 2024cs.NI cs.AI cs.LGcited by 125

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Abstract (arXiv)

On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest since they are more cost-effective, latency-efficient, and privacy-preserving compared with the cloud paradigm. Nonetheless, the performance of on-device LLMs is intrinsically constrained by resource limitations on edge devices. Sitting between cloud and on-device AI, mobile edge intelligence (MEI) presents a viable solution by provisioning AI capabilities at the edge of mobile networks, enabling end users to offload heavy AI computation to capable edge servers nearby. This article provides a contemporary survey on harnessing MEI for LLMs. We begin by illustrating several killer applications to demonstrate the urgent need for deploying LLMs at the network edge. Next, we present the preliminaries of LLMs and MEI, followed by resource-efficient LLM techniques. We then present an architectural overview of MEI for LLMs (MEI4LLM), outlining its core components and how it supports the deployment of LLMs. Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We hope this article inspires researchers in the field to leverage mobile edge computing to facilitate LLM deployment, thereby unleashing the potential of LLMs across various privacy- and delay-sensitive applications.

Key facts. Cited by 125 papers in the map (data through 7 Oct 2026). Builds on 204 of its 305 references that are in the map. First posted on arXiv 9 Jul 2024.

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204 of 305 references.

References · 204

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 14; 2025: 61; 2026: 50.

125 papers.

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  1. 2024VisionZip: Longer is Better but Not Necessary in Vision Language Modelscited by 298
  2. 2025Large Language Model Agent: A Survey on Methodology, Applications and Challengescited by 194
  3. 2024Large Language Model-Brained GUI Agents: A Surveycited by 160
  4. 2025VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learningcited by 41
  5. 2024Large Language Model Inference Acceleration: A Comprehensive Hardware Perspectivecited by 36
  6. 2025Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latencycited by 32
  7. 2025Internet of Agents: Fundamentals, Applications, and Challengescited by 31
  8. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 30
  9. 2025Vision-Language Models for Edge Networks: A Comprehensive Surveycited by 29
  10. 2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
  11. 2024CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaborationcited by 25
  12. 2024BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devicescited by 25
38 more
  1. 2025SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMscited by 23
  2. 2025TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Modelscited by 19
  3. 2025Security of Internet of Agents: Attacks and Countermeasurescited by 17
  4. 2024Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guaranteescited by 15
  5. 2024CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perceptioncited by 13
  6. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  7. 2025LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networkscited by 12
  8. 2025A Survey on Collaborative Mechanisms Between Large and Small Language Modelscited by 12
  9. 2025Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptationcited by 12
  10. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  11. 2026ApET: Approximation-Error Guided Token Compression for Efficient VLMscited by 11
  12. 2025Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directionscited by 10
  13. 2025PRISM: Privacy-Aware Routing for Adaptive Cloud-Edge LLM Inference via Semantic Sketch Collaborationcited by 10
  14. 2025MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAGcited by 9
  15. 2025Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exitscited by 8
  16. 2025From Prompts to Protection: Large Language Model-Enabled In-Context Learning for Smart Public Safety UAVcited by 8
  17. 2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
  18. 2025Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systemscited by 7
  19. 2025SlimCaching: Edge Caching of Mixture-of-Experts for Distributed Inferencecited by 7
  20. 2025Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compressioncited by 7
  21. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  22. 2025MoE2: Optimizing Collaborative Inference for Edge Large Language Modelscited by 6
  23. 2025Masking Teacher and Reinforcing Student for Distilling Vision-Language Modelscited by 6
  24. 2025Offline Learning for Combinatorial Multi-armed Banditscited by 5
  25. 2025Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniquescited by 5
  26. 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inferencecited by 4
  27. 2025Towards Pervasive Distributed Agentic Generative AI -- A State of The Artcited by 4
  28. 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoringcited by 4
  29. 2025DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurancecited by 4
  30. 2025Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a surveycited by 4
  31. 2026ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Schedulingcited by 4
  32. 2024Efficient Driving Behavior Narration and Reasoning on Edge Device Using Large Language Modelscited by 3
  33. 2025Visual Fidelity Index for Generative Semantic Communications with Critical Information Embeddingcited by 3
  34. 2025Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Reviewcited by 3
  35. 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
  36. 2025SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mambacited by 3
  37. 2026ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inferencecited by 3
  38. 2026OrderMoE: An expert similarity driven distributed edge MoE inferencecited by 3

50 most cited of 125 All 125 (JSON)