# LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication

arXiv: https://arxiv.org/abs/2401.02675 · HTML: https://mlsanity.com/paper/2401.02675/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021)
- [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [Resource allocation for text semantic communications](https://mlsanity.com/paper/2201.06023/) (2022)
- [Machine Intelligence at the Edge with Learning Centric Power Allocation](https://mlsanity.com/paper/1911.04922/) (2019)
- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [Semantic Information Market For The Metaverse: An Auction Based Approach](https://mlsanity.com/paper/2204.04878/) (2022)
- [Economics of Semantic Communication System in Wireless Powered Internet of Things](https://mlsanity.com/paper/2110.01423/) (2021)
- [Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design](https://mlsanity.com/paper/2212.06463/) (2022)
- [Learning Centric Power Allocation for Edge Intelligence](https://mlsanity.com/paper/2007.11399/) (2020)
- [Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks](https://mlsanity.com/paper/2206.05885/) (2022)
- [Economics of Semantic Communication System: An Auction Approach](https://mlsanity.com/paper/2208.05040/) (2022)
- [Game-based Pricing and Task Offloading in Mobile Edge Computing Enabled Edge-Cloud Systems](https://mlsanity.com/paper/2101.05628/) (2021)
- [Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines](https://mlsanity.com/paper/2301.12088/) (2023)

## Built on by (most cited first, 2 of 2)

- [Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks](https://mlsanity.com/paper/2505.02413/) (2025)
- [Keep, Customize, or Exit: Default Design and Token Pricing in LLM Reasoning Services](https://mlsanity.com/paper/2608.13315/) (2026)

## Abstract (arXiv)

The next generation of communication is envisioned to be intelligent communication, that can replace traditional symbolic communication, where highly condensed semantic information considering both source and channel will be extracted and transmitted with high efficiency. The recent popular large models such as GPT4 and the boosting learning techniques lay a solid foundation for the intelligent communication, and prompt the practical deployment of it in the near future. Given the characteristics of "training once and widely use" of those multimodal large language models, we argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LMaaS). However, the trading and pricing problem is quite complex with heterogeneous and dynamic customer environments, making the pricing optimization problem challenging in seeking on-hand solutions. In this paper, we aim to fill this gap and formulate the LMaaS market trading as a Stackelberg game with two steps. In the first step, we optimize the seller's pricing decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes the prices of large models iteratively by reasoning customers' future rental decisions, which is able to achieve a near-optimal pricing solution. In the second step, we optimize customers' selection decisions by designing a robust selecting and renting (RSR) algorithm, which is guaranteed to be optimal with rigorous theoretical proof. Extensive experiments confirm the effectiveness and robustness of our algorithms.