Collaborative Large Language Model Inference via Resource-Aware Parallel Speculative Decoding

• URL: http://arxiv.org/abs/2511.01695v2
• Date: Tue, 04 Nov 2025 02:59:57 GMT
• Title: Collaborative Large Language Model Inference via Resource-Aware Parallel Speculative Decoding
• Authors: Jungyeon Koh, Hyun Jong Yang,
• Abstract summary: Speculative decoding offers a promising solution by partitioning token generation between a lightweight draft model on mobile devices and a powerful target model on edge servers.<n>This paper is the first to propose a unified framework that jointly optimize user association and resource allocation to support efficient parallel speculative decoding.<n>Results show that our method achieves up to 28.0% and an average of 23.7% reduction in end-to-end latency without compromising inference accuracy.
• Score: 6.130486652666936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing demand for on-device large language model (LLM) inference highlights the need for efficient mobile edge computing (MEC) solutions, especially in resource-constrained settings. Speculative decoding offers a promising solution by partitioning token generation between a lightweight draft model on mobile devices and a powerful target model on edge servers, but suffers from communication overhead and asynchronous delays. This paper is the first to propose a unified framework that jointly optimizes user association and resource allocation (UARA) to support efficient parallel speculative decoding. We solve the UARA problem using a multi-agent deep reinforcement learning algorithm. To evaluate our approach under realistic conditions, we conduct experiments using the Sionna simulator. Results show that our method achieves up to 28.0% and an average of 23.7% reduction in end-to-end latency without compromising inference accuracy, enabling scalable and low-latency LLM services in MEC systems.

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