# Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning

arXiv: https://arxiv.org/abs/2408.02549 · HTML: https://mlsanity.com/paper/2408.02549/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2024.

## Built on by (most cited first, 6 of 6)

- [Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications](https://mlsanity.com/paper/2409.05314/) (2024)
- [FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring](https://mlsanity.com/paper/2507.10134/) (2025)
- [Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving](https://mlsanity.com/paper/2410.08854/) (2024)
- [Distillation-Enabled Knowledge Alignment for Generative Semantic Communications of AIGC Images](https://mlsanity.com/paper/2506.19893/) (2025)
- [Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2604.07148/) (2026)
- [Goodput Maximization for Large Language Model Edge Inference: A Two-Phase Maskable PPO Approach](https://mlsanity.com/paper/2608.25543/) (2026)

## Abstract (arXiv)

Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted considerable interest from academia and telecom industry. This work considers a novel edge-cloud deployment of foundation models in 6G networks. Specifically, it aims to minimize the service delay of foundation models by radio resource allocation and task offloading, i.e., offloading diverse content generation tasks to proper LLMs at the network edge or cloud. In particular, we first introduce the communication system model, i.e., allocating radio resources and calculating link capacity to support generated content transmission, and then we present the LLM inference model to calculate the delay of content generation. After that, we propose a novel in-context learning method to optimize the task offloading decisions. It utilizes LLM's inference capabilities, and avoids the difficulty of dedicated model training or fine-tuning as in conventional machine learning algorithms. Finally, the simulations demonstrate that the proposed edge-cloud deployment and in-context learning task offloading method can achieve satisfactory generation service quality without dedicated model training or fine-tuning.