Closing the Social-Semantic Gap: SPSD for Edge-Based Prompt Compression in Cloud LLM Inference
arXiv:2606.19364v1 Announce Type: new Abstract: The prefill stage of Large Language Model (LLM) inference is a growing contributor to cloud-scale energy cost. Many consumer-support and conversational prompts contain social scaffolding: politeness markers, apologetic preamble, repetition, and rapport-building language that is important for human communication but carries low marginal information for machine reasoning. We call this discrepancy the Social-Semantic Gap. We present SPSD (Sentiment Preserving Semantic Distillation), an edge-based pipeline that compresses user prompts using a 4-bit q
The increasing energy consumption of large language models necessitates immediate innovation in inference efficiency.
Reducing the energy and computational cost of LLM inference is critical for scaling AI globally and mitigating its environmental impact.
This advancement introduces an edge-based method to significantly compress prompts, lowering the operational costs and energy footprint of cloud LLMs.
- · Cloud LLM providers
- · Edge AI hardware manufacturers
- · Consumers of AI services
- · Energy-efficient AI initiatives
- · Inefficient LLM inference architectures
- · Regions with high energy costs for compute
Lower operational costs for LLM inference will accelerate AI adoption and deployment.
Increased accessibility and affordability of advanced AI could lead to broader AI integration across industries, potentially intensifying competition.
Reduced energy demand per LLM query might alleviate some pressure on energy grids, yet overall AI growth could still increase total energy consumption.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG