The proposed Apparate model uniquely addresses the latency-throughput tension in model serving systems by integrating early-exit strategies, enhancing efficiency without sacrificing accuracy.
Existing model-serving frameworks prioritize throughput under latency constraints, but they often overlook the potential benefits of incorporating early-exit mechanisms for improved system performance.
Apparate presents a novel approach to model architecture by using shallow ramps that enable quicker exit decisions, leading to adaptive responses to varying inference requests.
The study highlights a gap in the literature regarding latency-focused strategies, suggesting a shift towards exploring early-exit designs that optimize both speed and accuracy in inference.
#machine-learning #model-serving #early-exit-mechanisms #latency-throughput-tradeoff #system-architecture
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