RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference

Guotao Yang; Mingxi Zhao; Haopeng Li; Zhengchao Wang; Sheng Chen; Yitao Hu; Keqiu Li

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arXiv · 2608.04991

RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference

Guotao Yang·Mingxi Zhao·Haopeng Li·Zhengchao Wang·Sheng Chen·Yitao Hu·Keqiu Li

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Abstract

Large language model (LLM) agents repeatedly process long, privacy-sensitive contexts, while cloud-only deployment exposes user data beyond the trusted endpoint and fully local deployment often requires costly hardware. Split inference offers a middle ground by executing the model head, tail, and tools locally and the middle layers in the cloud, but its local-cloud-local path transfers boundary hidden states at every invocation and creates a critical communication bottleneck. We present \system, a reference-aware codec that retrieves exact-token historical spans for prefill uplinks, reuses the reconstructed uplink state for same-round prefill downlinks, and generates boundary-specific decode references with lightweight causal predictors. RAC applies grouped affine alignment and calibrated residual quantization with optional prefill outliers, while sender-side wire-format reconstruction synchronizes subsequent references and offline calibration accounts for quality and packed representation costs. Across three models and nine evaluated model-link pairs, Raw-to-RAC mean time to first token (TTFT) and time per output token (TPOT) ratios are 1.24-2.72$\times$ and 1.01-2.79$\times$, while the 12 non-perplexity task-score changes range from $-0.40$ to $+2.50$ points.

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BibTeX RIS

Guotao Yang, Mingxi Zhao, Haopeng Li, Zhengchao Wang, Sheng Chen, Yitao Hu, Keqiu Li. 2026-08-05. RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference. https://arxiv.org/abs/2608.04991

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