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Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching

Ma, Qianli et al.
arXiv (All) · Papers
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artificial-intelligencemachine-learning
Disaggregated serving alleviates memory bottlenecks in Large Language Model (LLM) inference but creates a severe communication bottleneck: transmitting high-dimensional Key-Value (KV) caches often dominates time-to-first-token (TTFT). Moreover, reusing caches across heterogeneous models (e.g., base
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