CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding
📰 ArXiv cs.AI
Learn how CoVSpec enables efficient device-edge co-inference for vision-language models via speculative decoding, improving performance on mobile devices
Action Steps
- Implement CoVSpec on a mobile device using a lightweight draft VLM
- Configure the edge server with a larger target VLM
- Apply speculative decoding to collaborate between the device and edge server
- Test the co-inference performance using benchmark datasets
- Compare the results with traditional inference methods
Who Needs to Know This
AI engineers and researchers working on vision-language models can benefit from this approach to improve model performance on mobile devices
Key Insight
💡 Speculative decoding enables efficient co-inference between mobile devices and edge servers for vision-language models
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📱💻 CoVSpec: Efficient device-edge co-inference for vision-language models via speculative decoding
Key Takeaways
Learn how CoVSpec enables efficient device-edge co-inference for vision-language models via speculative decoding, improving performance on mobile devices
Full Article
Title: CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding
Abstract:
arXiv:2605.02218v1 Announce Type: new Abstract: 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 extendi
Abstract:
arXiv:2605.02218v1 Announce Type: new Abstract: 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 extendi
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