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VoltanaLLM system cuts LLM inference energy use by 36% while meeting SLOs

By PulseAugur Editorial · [1 sources] ·

A new system called VoltanaLLM has been developed to address the significant energy consumption of Large Language Model (LLM) inference. This system, detailed in a recent arXiv paper, employs adaptive frequency control and state-space routing to reduce energy usage during the prefill and decode phases of LLM serving. By identifying optimal operating points for GPU frequency and intelligently routing requests, VoltanaLLM can achieve substantial energy savings without compromising latency Service Level Objectives (SLOs). AI

IMPACT Potential to significantly reduce the operational costs and environmental impact of deploying LLMs at scale.

RANK_REASON Research paper detailing a new system for LLM serving efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VoltanaLLM system cuts LLM inference energy use by 36% while meeting SLOs

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiahuan Yu, Aryan Taneja, Junfeng Lin, Minjia Zhang ·

    VoltanaLLM: Energy-Efficient and SLO-Aware Disaggregated LLM Serving via Adaptive Frequency Control and State-Space Routing

    arXiv:2509.04827v3 Announce Type: replace-cross Abstract: The energy cost of Large Language Model (LLM) inference is rapidly becoming a barrier to sustainable and scalable deployment. Although modern serving architectures expose distinct prefill and decode behaviors, existing sys…

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