mlsanityData to 7 Oct 2026

An Overview on Generative AI at Scale with Edge-Cloud Computing

arXiv 2306.17170 ↗First posted 2 Jun 2023cs.DC cs.AI cs.SYcited by 9

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Abstract (arXiv)

As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what is created by humans. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 90 of its 151 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

90 of 151 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 1.

9 papers.

  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  2. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  3. 2024VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Frameworkcited by 10
  4. 2024Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancementcited by 7
  5. 2023Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunitiescited by 5
  6. 2025Descrip3D: Enhancing Large Language Model-based 3D Scene Understanding with Object-Level Text Descriptionscited by 3
  7. 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspectivecited by 1
  8. 2025Match Chat: Real Time Generative AI and Generative Computing for Tenniscited by 0
  9. 2026Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networkscited by 0