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vLLM configurations impact LLM energy, performance, and accuracy

By PulseAugur Editorial · [2 sources] ·

A new research paper investigates the trade-offs between energy consumption, performance, and accuracy when configuring inference engines like vLLM for large language models (LLMs). The study analyzed combinations of attention kernel type, prefix caching, and chunked prefill across five open-weight LLMs and five inference tasks. Results indicate that attention type and prefix caching significantly impact energy and performance, with effects varying by model and workload, while chunked prefill showed limited impact under default configurations. The research also found that inference engine choices can unexpectedly influence model accuracy. AI

IMPACT Configuration tuning of inference engines like vLLM can optimize LLM deployment for energy efficiency and performance without sacrificing accuracy.

RANK_REASON Research paper analyzing configuration trade-offs for LLM inference engines.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

vLLM configurations impact LLM energy, performance, and accuracy

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Research paper analyzing configuration trade-offs for LLM inference engines.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Nada Zine, Tristan Coignion, Vincenzo Stoico, Cl\'ement Quinton, Romain Rouvoy, Patricia Lago ·

    Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations

    arXiv:2607.09172v1 Announce Type: cross Abstract: Large Language Models are reshaping how software is developed and maintained. They are typically deployed in production using inference engines such as vLLM, which can efficiently serve pre-trained, highly configurable models. Whi…

  2. arXiv cs.AI TIER_1 English(EN) · Patricia Lago ·

    Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations

    Large Language Models are reshaping how software is developed and maintained. They are typically deployed in production using inference engines such as vLLM, which can efficiently serve pre-trained, highly configurable models. While prior work has focused on model architectures a…

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