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cs.DC2026
ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving
Xiangchen Li, Saeid Ghafouri, Jiakun Fan +3
Speculative decoding enables collaborative Large Language Model (LLM) inference across cloud and edge by separating lightweight token drafting from heavyweight verification. While…
cs.DC2026
WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching
Xiangchen Li, Jiakun Fan, Qingyuan Wang +7
As Large Language Models (LLMs) become increasingly accessible to end users, an ever-growing number of inference requests are initiated from edge devices and computed on centralize…
cs.DC2025
Optimizing video analytics inference pipelines: a case study
Saeid Ghafouri, Yuming Ding, Katerine Diaz Chito +3
Cost-effective and scalable video analytics are essential for precision livestock monitoring, where high-resolution footage and near-real-time monitoring needs from commercial farm…