HybridFlow: Resource-Adaptive Subtask Routing for Efficient Edge-Cloud LLM Inference
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918312650211328 |
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| author | Dong, Jiangwen Li, Jiayu Zheng, Tianhang Lin, Wanyu |
| author_facet | Dong, Jiangwen Li, Jiayu Zheng, Tianhang Lin, Wanyu |
| contents | Edge-cloud collaborative inference is becoming a practical necessity for LLM-powered edge devices: on-device models often cannot afford the required reasoning capability, while cloud-only inference could be prohibitively costly and slow under strict latency and token/API budgets. However, existing edge-cloud collaboration methods often route per query or fixed steps simply based-on the estimated difficulty. Such coarse and static heuristics overlook subtask dependencies, missing opportunities for parallel execution and budget-adaptive routing. To this end, we propose \textbf{HybridFlow}, a resource-adaptive edge-cloud inference framework that (i) builds a dependency-aware DAG for each query and executes newly unlocked subtasks in parallel, reducing end-to-end latency; (ii) routes each subtask online to the edge or cloud via a learned benefit--cost utility model that dynamically trades accuracy gains against token/API and latency budgets, thereby reducing unnecessary cloud usage while preserving reasoning quality. Across GPQA, MMLU-Pro, AIME24, and LiveBench-Reasoning, HybridFlow improves the cost-accuracy trade-off, reducing latency and cloud API usage while maintaining competitive accuracy against strong structured reasoning baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22137 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HybridFlow: Resource-Adaptive Subtask Routing for Efficient Edge-Cloud LLM Inference Dong, Jiangwen Li, Jiayu Zheng, Tianhang Lin, Wanyu Distributed, Parallel, and Cluster Computing Edge-cloud collaborative inference is becoming a practical necessity for LLM-powered edge devices: on-device models often cannot afford the required reasoning capability, while cloud-only inference could be prohibitively costly and slow under strict latency and token/API budgets. However, existing edge-cloud collaboration methods often route per query or fixed steps simply based-on the estimated difficulty. Such coarse and static heuristics overlook subtask dependencies, missing opportunities for parallel execution and budget-adaptive routing. To this end, we propose \textbf{HybridFlow}, a resource-adaptive edge-cloud inference framework that (i) builds a dependency-aware DAG for each query and executes newly unlocked subtasks in parallel, reducing end-to-end latency; (ii) routes each subtask online to the edge or cloud via a learned benefit--cost utility model that dynamically trades accuracy gains against token/API and latency budgets, thereby reducing unnecessary cloud usage while preserving reasoning quality. Across GPQA, MMLU-Pro, AIME24, and LiveBench-Reasoning, HybridFlow improves the cost-accuracy trade-off, reducing latency and cloud API usage while maintaining competitive accuracy against strong structured reasoning baselines. |
| title | HybridFlow: Resource-Adaptive Subtask Routing for Efficient Edge-Cloud LLM Inference |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.22137 |