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arXiv · 2603.07949

RAPID: Redundancy-Aware and Compatibility-Optimal Edge-Cloud Partitioned Inference for Diverse VLA Models

Zihao Zheng·Sicheng Tian·Hangyu Cao·Chenyue Li·Jiayu Chen·Maoliang Li·Xinhao Sun·Hailong Zou·Guojie Luo·Xiang Chen
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Abstract

Vision Language Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) inference offers an effective fix by easing edge-device computing pressure to meet real-time needs. However, existing ECC frameworks are suboptimal for VLA models due to two challenges: (1) Mainstream environment-oriented edge-cloud partitioning methods are susceptible to interference from visual noise; (2) Existing edge-cloud partitioning methods overlook the step-wise redundancy unique to embodied tasks, thereby disrupting the physical continuity of motion. To address these issues, we propose a novel ECC inference framework, termed RAPID. Specifically, we developed an implementation tailored to the proposed framework. Experiments demonstrate this achieves a speedup of up to 1.73x with only 5%~7% overhead.

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Zihao Zheng, Sicheng Tian, Hangyu Cao, Chenyue Li, Jiayu Chen, Maoliang Li, Xinhao Sun, Hailong Zou, Guojie Luo, Xiang Chen. 2026-03-09. RAPID: Redundancy-Aware and Compatibility-Optimal Edge-Cloud Partitioned Inference for Diverse VLA Models. https://arxiv.org/abs/2603.07949

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