# Fog Computing and Large Language Models: A vision for mutual beneficiaries

arXiv: https://arxiv.org/abs/2606.29483 · HTML: https://mlsanity.com/paper/2606.29483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI](https://mlsanity.com/paper/2607.20937/) (2026)

## Abstract (arXiv)

Fog computing utilizes proximal computational resources for sensor data processing and actuation, and addresses the latency, network load, and privacy issues of cloud-centric Internet of Things. On the other hand, Large Language Models (LLMs) are a type of deep learning AI models, which are trained on enormous text data, that perform various natural language processing tasks such as translation, question answering, text summarization, and code generation. LLMs are generally cloud-centric, requiring abundant GPU memory and computing capabilities, again face the same issues that led to fog computing. This pushes the necessity for LLM support in the proximity on fog infrastructure, requiring LLM optimizations such as parameter-weight quantization, pruning, low-rank adaptation etc. Meanwhile, fog computing also gets benefit from LLM's ability for code generation, in the dynamic deployment of fog-based applications. The paper addresses how both fog computing and LLMs can be mutual beneficiaries, discussing the state-of-the-art and future research scope.