CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding
Vision-language models (VLMs) have demonstrated strong capabilities in multimodal perception and reasoning. However, deploying large VLMs on mobile devices remains challenging due to their substantial computational and memory demands. A practical alternative is device-edge co-inference, where a lightweight draft VLM on the mobile device collaborates with a larger target VLM on the edge server via speculative decoding. Nevertheless, directly extending speculative decoding to VLMs suffers from severe inefficiency due to excessive visual-token computation and high communication overhead. To address these challenges, we propose CoVSpec, an efficient collaborative speculative decoding framewor...
| Authors | Yuanyuan Jia, Shunpu Tang, Qianqian Yang |
|---|---|
| Primary category | cs.AI |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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