Practice of Mobile Application LLM System Driven by End-Edge-Cloud Collaboration

Xin Yu

Artificial Intelligence and Digital Technology, 2026, vol. 3, issue 1, 62-68

Abstract: Large language models (LLMs) have rapidly become a general-purpose capability layer for mobile applications, yet "cloud-only LLM" deployment faces persistent bottlenecks in privacy-sensitive data access, inference cost, and real-time reliability. This paper presents a practical system design for a mobile application LLM stack driven by end-edge-cloud collaboration and coordinated "large-small model" execution. We summarize why a single large model cannot adequately address (i) user-level data richness and privacy constraints on-device, (ii) the high marginal cost of cloud inference at scale, and (iii) responsiveness and stability requirements under variable networks. We propose an architecture that assigns personalized, latency-critical, and privacy-preserving functions to on-device small models and local runtimes; delegates cacheable, low-latency coordination and retrieval services to the edge; and reserves cloud LLMs for complex reasoning and generation. We further describe orchestration mechanisms, routing policies, and optimization techniques, including context condensation, selective retrieval, speculative execution, and feedback-driven adaptation. Results are reported as system-level outcomes in terms of latency, cloud token reduction, and robustness under network degradation, concluding that end-edge-cloud collaboration can improve user experience while materially reducing cloud-side cost and expanding privacy-respecting capability coverage.

Keywords: end-edge-cloud collaboration; large-small model coordination; mobile LLM system; privacy-preserving inference; cost-aware routing; on-device intelligence (search for similar items in EconPapers)
Date: 2026
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