Semantic Cache Distillation: Efficient State Transfer via Reuse and Selective Patching
Researchers have developed Semantic Cache Distillation (SCD), a new framework designed to reduce the communication bottleneck in disaggregated LLM inference. SCD replaces raw Key-Value (KV) cache transmission with compact semantic codes, improving the time-to-first-token (TTFT) by up to 2.65 times. The method utilizes reuse and selective patching to minimize transfer costs and truncate error propagation, maintaining generation quality close to the oracle. AI
IMPACT Reduces communication overhead in disaggregated LLM inference, potentially speeding up applications that rely on large model serving.