MSAO: A New Paradigm for Edge-Cloud Collaborative Optimization of Multimodal Large Model Inference

MSAO proposes an adaptive offloading framework based on modality sparsity awareness. It quantifies the necessity of each modality using the MAS metric and achieves dynamic edge-cloud collaboration with speculative execution. This reduces latency by 30% while increasing throughput by 1.5-2.3 times, solving the problems of high resource consumption and long inference latency when deploying Multimodal Large Language Models (MLLMs) on edge devices.

Multimodal Large Language Models (MLLMs) are powerful, but their deployment faces severe challenges: huge computational resource consumption, high inference latency, and difficulty running on edge devices. Traditional solutions are in a dilemma: running entirely on the edge leads to slow responses, while relying fully on the cloud has network latency and privacy risks. Thus, an optimal balance between edge and cloud needs to be found.

The MSAO framework has two core innovations: 1. A lightweight heterogeneous modality-aware fine-grained sparsity module that calculates the Modality Activation Sparsity (MAS) metric to quantify the necessity of each modality; 2. An adaptive speculative edge-cloud collaborative offloading mechanism that dynamically schedules based on MAS scores and system status, using speculative execution to hide communication latency.

Modality Activation Sparsity (MAS) Metric

MAS accurately identifies sparsifiable modalities through three-dimensional joint analysis: spatial (feature map importance), temporal (cross-frame information flow), and modal (contribution to output).

Adaptive Offloading Mechanism

Dynamic scheduling based on MAS scores and system status (network bandwidth, edge load, cloud availability); Speculative execution mechanism: After the edge sends a request, it continues processing based on context-predicted results. When the cloud result returns, it verifies and corrects, removing the critical path of network latency.

In VQAv2 and MMBench benchmark tests, MSAO performed excellently: end-to-end latency reduced by 30%, resource overhead reduced by 30%-65%, throughput increased by 1.5-2.3 times, while maintaining competitive accuracy, providing an engineering solution for practical deployment.

MSAO has potential for implementation in scenarios such as smart homes (local processing of simple commands + cloud-based complex reasoning), autonomous driving, and industrial quality inspection; its sparsity-aware adaptive offloading concept can be extended to other model optimization and resource scheduling problems.

MSAO achieves efficient inference of MLLMs through modality sparsity awareness and edge-cloud collaboration; in the future, with enhanced edge computing and the popularization of 5G/6G, edge-cloud collaboration will become mainstream. MSAO provides technical reserves for this trend, promoting the penetration of AI into more scenarios.