Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training
arXiv:2509.24050v4 Announce Type: replace Abstract: Device-cloud collaboration holds promise for deploying large language models (LLMs), leveraging lightweight on-device models for efficiency while relying on powerful cloud models for superior reasoning. A central challenge in this setting is determining, for each incoming query, whether it should be processed locally or offloaded to the cloud. Existing approaches typically rely on external routers, which often struggle to determine difficulty from the prompt itself, especially for tasks involving complex reasoning. Motivated by this limitatio
The proliferation of LLMs and increasing demands for efficient, private, and powerful AI inference necessitate novel architectural solutions that balance local and cloud capabilities.
This work directly addresses a core challenge in LLM deployment, potentially enabling more ubiquitous, cost-effective, and robust AI applications for businesses and end-users.
The ability to dynamically and intelligently route LLM queries between on-device and cloud models could lead to more efficient resource utilization, enhanced privacy, and better user experiences.
- · AI hardware manufacturers
- · Cloud AI providers
- · Edge computing platforms
- · Mobile device manufacturers
- · Monolithic cloud-only AI service providers
- · AI models with high inference costs
Improved efficiency and user experience in applications leveraging large language models.
Increased adoption of AI on personal devices due to better performance and privacy guarantees.
New business models emerging around hybrid on-device/cloud AI services, further fragmenting the AI market.
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Read at arXiv cs.LG