mlsanityData to 7 Oct 2026

Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

arXiv 2309.16739 ↗First posted 28 Sep 2023cs.LG cs.AIcited by 89

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Abstract (arXiv)

Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, the status quo cloud-based deployment faces some critical challenges: 1) long response time; 2) high bandwidth costs; and 3) the violation of data privacy. 6G mobile edge computing (MEC) systems may resolve these pressing issues. In this article, we explore the potential of deploying LLMs at the 6G edge. We start by introducing killer applications powered by multimodal LLMs, including robotics and healthcare, to highlight the need for deploying LLMs in the vicinity of end users. Then, we identify the critical challenges for LLM deployment at the edge and envision the 6G MEC architecture for LLMs. Furthermore, we delve into two design aspects, i.e., edge training and edge inference for LLMs. In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs. This article serves as a position paper for thoroughly identifying the motivation, challenges, and pathway for empowering LLMs at the 6G edge.

Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

13 of 35 references.

References · 13

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  4. 2022Emergent Abilities of Large Language Modelscited by 2,483
  5. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
  6. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,615
  7. 2021Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LMcited by 621
  8. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  9. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
  10. 2023Split Learning in 6G Edge Networkscited by 68
  11. 2023Training Transformers with 4-bit Integerscited by 58
  12. 2023Large Language Models Empowered Autonomous Edge AI for Connected Intelligencecited by 55
1 more
  1. 2022GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edgecited by 32

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 28; 2025: 40; 2026: 19.

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  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  2. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  3. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  4. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
  5. 2024A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicinecited by 45
  6. 2024When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignmentcited by 43
  7. 2024AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learningcited by 40
  8. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  9. 2024TelecomGPT: A Framework to Build Telecom-Specfic Large Language Modelscited by 38
  10. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  11. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  12. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 30
38 more
  1. 2025Vision-Language Models for Edge Networks: A Comprehensive Surveycited by 29
  2. 2024TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specificationscited by 26
  3. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  4. 2023Green Edge AI: A Contemporary Surveycited by 24
  5. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  6. 2024AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Mergingcited by 22
  7. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  8. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  9. 2024SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensingcited by 14
  10. 2026Multimodal Multi-Agent Empowered Legal Judgment Predictioncited by 14
  11. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  12. 2024NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attentioncited by 12
  13. 2024TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networkscited by 12
  14. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  15. 2025LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networkscited by 12
  16. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  17. 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
  18. 2025A Contemporary Survey of Large Language Model Assisted Program Analysiscited by 10
  19. 2025Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoringcited by 10
  20. 2024Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Taskscited by 9
  21. 2025Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exitscited by 8
  22. 2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
  23. 2024Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networkscited by 7
  24. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 7
  25. 2025SlimCaching: Edge Caching of Mixture-of-Experts for Distributed Inferencecited by 7
  26. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  27. 2025LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learningcited by 6
  28. 2025Rethinking Membership Inference Attacks Against Transfer Learningcited by 5
  29. 2025Revisiting Outage for Edge Inference Systemscited by 5
  30. 2025Otters: An Energy-Efficient SpikingTransformer via Optical Time-to-First-Spike Encodingcited by 5
  31. 2026Conflict-Aware Client Selection for Multi-Server Federated Learningcited by 5
  32. 2025Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterizationcited by 4
  33. 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inferencecited by 4
  34. 2025FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoringcited by 4
  35. 2026NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learningcited by 4
  36. 2026Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorialcited by 4
  37. 2026Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneitycited by 4
  38. 2024Privacy-preserving Universal Adversarial Defense for Black-box Modelscited by 3

50 most cited of 89 All 89 (JSON)

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  2. 2024EdgeShard: Efficient LLM Inference via Collaborative Edge Computing
  3. 2025Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
  4. 2024Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices
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  7. 2023Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
  8. 2025EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices