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

Researchers propose Semantic Cache Distillation (SCD), a technical framework that significantly reduces communication overhead in large language model inference by replacing raw Key-Value cache transmission with compact semantic codes. The method achieves up to 2.65x speedup in time-to-first-token while maintaining generation quality within 5% of baseline performance, addressing a critical bottleneck in disaggregated LLM serving architectures.

Semantic Cache Distillation addresses a fundamental infrastructure challenge in modern LLM deployment. As language models have grown exponentially in size, the bottleneck in inference has shifted from computation to communication—specifically, transmitting high-dimensional KV caches between distributed serving components. This creates practical limitations for real-time applications where latency directly impacts user experience.

The research builds on the broader trend of disaggregated inference architectures, which separate memory-intensive operations from computation to optimize hardware utilization. However, this design pattern introduces severe communication costs that can dwarf actual computation time. Previous approaches relied on quantization or selective recomputation, each introducing quality-latency tradeoffs. SCD innovates by introducing semantic compression through two complementary mechanisms: cache reuse via low-rank reconstruction handles the bulk of data transfer, while selective patching at transition layers prevents error accumulation when reusing caches across model variants.

For infrastructure providers and cloud operators deploying LLM services, this work directly impacts operational efficiency and unit economics. Reducing bandwidth requirements enables lower-latency inference at scale, improving competitiveness in production environments. The framework's ability to handle heterogeneous models—base and fine-tuned variants—is particularly valuable for production systems that maintain multiple model versions. The maintained quality levels suggest practical deployment viability rather than theoretical optimization.

The implications extend beyond pure performance metrics. Efficient cache transfer enables more flexible hardware allocation and reduces the computational resources required for inference, potentially lowering operational costs. Future work likely focuses on hardware-specific optimizations and integration with existing serving frameworks like vLLM or Ray Serve.

• →SCD reduces time-to-first-token by up to 2.65x through semantic compression of KV caches instead of raw transmission
• →The framework maintains generation quality within 5% F1 of oracle performance while dramatically reducing bandwidth requirements
• →Two-mechanism approach combines cache reuse via low-rank reconstruction and selective patching to prevent error propagation across layers
• →Outperforms quantization and selective recomputation baselines on quality-latency tradeoff frontier in bandwidth-constrained settings
• →Enables efficient cache reuse across heterogeneous model variants, solving semantic misalignment issues in multi-model serving