STEM与日常科技·英语精读30篇（5）
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Model Distillation in Production: Trade-Offs Between Latency and Accuracy at Scale
生产环境中的模型蒸馏：大规模部署下的延迟与精度权衡
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Large language models now power customer support chatbots, yet their full-size versions often exceed edge-device memory constraints by 300% or more.
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Distillation techniques compress knowledge into smaller student models, but accuracy drops unevenly across domains—legal queries suffer more than restaurant reservations.
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A 40% reduction in inference time may increase error rates for nuanced sentiment detection, especially with non-native speaker phrasing.
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Cloud-based fallback logic adds complexity: when the distilled model fails, routing to a larger one introduces unpredictable latency spikes.
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Teams monitor not just accuracy metrics, but also user session abandonment rates correlated with response delays exceeding 1.8 seconds.
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Ultimately, business impact—not theoretical F1 scores—drives distillation thresholds, especially where conversational continuity affects trust and retention.