arXiv paper examining distributed real-time inference architectures and their performance tradeoffs. Analyzes cloud vs edge execution strategies for ML models in time-sensitive applications. Contributes to understanding of inference system design decisions.
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Cloud Is Closer Than It Appears: Revisiting the Tradeoffs of Distributed Real-Time Inference
New analysis challenges assumptions that edge-only is optimal for real-time ML inference, showing cloud and edge have more nuanced tradeoffs depending on latency, bandwidth, and cost constraints.
Monday, May 4, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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