arXiv:2606.07684v1 Announce Type: new Abstract: 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 and fine-tuned variants) causes semantic misalignment that accumulates over layers, degrading generation quality. We propose Semantic Cache Distillation (SCD), a loss-constrained framework that replaces raw KV transmission with compact semantic codes.
The continuous growth in size and deployment of Large Language Models is driving urgent research into more efficient inference mechanisms to overcome existing bottlenecks.
Improving LLM inference efficiency directly impacts the scalability, cost, and real-time applicability of AI systems, enabling broader deployment and new use cases.
The proposed Semantic Cache Distillation (SCD) introduces a method to significantly reduce communication overhead and improve cache reuse across different LLM variants, optimizing serving infrastructure.
- · AI service providers
- · Cloud infrastructure providers
- · Generative AI developers
- · Data centers
- · Companies relying on less optimized LLM serving architectures
- · Traditional high-bandwidth memory solution providers
Reduced operational costs and latency for large language model inference.
Acceleration of new AI agent and application development due to more efficient backend infrastructure.
Increased accessibility and democratization of advanced AI capabilities due to lower computational barriers.
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Read at arXiv cs.LG