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July 15, 2026ACM SIGOPS Operating Systems Review

Rethinking LLM Deployment for Intent-Based Serving

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Authors

DLDimitrios LiakopoulosPSPrasoon SinhaTHTianrui Hu

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Overview

Randomized trial evaluates a new system to optimize large language model configurations, suggesting significant cost reductions.

Key Points

  • The goal is to optimize the deployment of large language models (LLMs) based on user intents while minimizing operational costs.
  • Introduced MaverIQ, an intent-based LLM inference serving system.
  • Developed lightweight LLM fingerprints and analytical models for latency and memory footprint estimation.
  • Utilized uneven distribution of LLM layers across GPUs to enhance resource efficiency.
  • MaverIQ reduced profiling costs by 7-15× compared to baseline systems.
  • Achieved a 3.8-8.3× reduction in operational costs across various LLMs and workloads.
  • Effectively met user intents during evaluation.

Cite This Study

Liakopoulos et al. (2026) studied this question.

synapsesocial.com/papers/6a57234488b21df8754800aehttps://doi.org/10.1145/3830422.3830428
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1POSTER: LLM-PQ:Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization2024 · 12 citations
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  3. 3LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization2024 · 3 citations
  4. 4ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency2024 · 1 citations
  5. 5BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models2024