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This paper explores using collaborative mobile edge computing servers to perform large language model inference while meeting soft deadline constraints. It highlights the importance of completing computations on time to avoid cascading failures due to task dependencies.
This paper explores using collaborative mobile edge computing servers to perform large language model inference while meeting soft deadline constraints. It highlights the importance of completing computations on time to avoid cascading failures due to task dependencies.
AI-assisted summary based on the listed source.
This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In this system, to improve the quality of service, computations are expected to be completed within their deadlines. However, due to dependencies among tasks or...
Meeting soft deadlines in LLM inference is critical to maintaining service quality and preventing failures in dependent tasks. The proposed transformer-enhanced PPO approach aims to optimize task scheduling in MEC environments for timely LLM inference.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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