# MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference

arXiv: https://arxiv.org/abs/2509.16995 · HTML: https://mlsanity.com/paper/2509.16995/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Adaptive Guidance Semantically Enhanced via Multimodal LLM for Edge-Cloud Object Detection](https://mlsanity.com/paper/2509.19875/) (2025)
- [AVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems](https://mlsanity.com/paper/2511.18151/) (2025)
- [Adapter-Augmented Bandits for Online Multi-Constrained Multi-Modal Inference Scheduling](https://mlsanity.com/paper/2603.06403/) (2026)
- [DAT: Dual-Aware Adaptive Transmission for Efficient Multimodal LLM Inference in Edge-Cloud Systems](https://mlsanity.com/paper/2604.05375/) (2026)
- [MHR-Router: Multimodal Heterogeneous Retrieval-Enhanced Adaptive Routing for VLM Inference](https://mlsanity.com/paper/2610.13278/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) enable powerful cross-modal inference but impose significant computational and latency burdens, posing severe challenges for deployment in resource-constrained environments. In this paper, we propose MoA-Off, an adaptive heterogeneous modality-aware offloading framework with edge-cloud collaboration for efficient MLLM inference. MoA-Off introduces a lightweight heterogeneous modality-aware module that estimates the complexity of heterogeneous inputs through multi-dimensional feature analysis. Then, an adaptive edge-cloud collaborative offloading strategy is proposed that dynamically schedules workloads between edge and cloud based on modality-aware complexity scores and real-time system states. The experimental results demonstrate that MoA-Off can achieve over 30% reduction in latency and 30%-65% decrease in resource overhead while maintaining competitive accuracy compared to traditional approaches.