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How Developers Configure LLM Inference: A Large-Scale Study of Real-World Practices

Nada Zine, Matías Martínez, Romain Rouvoy, Clément Quinton · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27945 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

LLMsはますますソフトウェアシステムに統合されており、展開と結論のコンテキストを繰り返すソフトウェアエンジニアリングタスクにします。これらのインフェレンスバックエンドは、モデルロード、生成、サービスおよびリクエスト処理に影響を与える多くのオプションを暴露します。しかし、これらのオプションの開発者が実践で少なくとも1つを使用して明示的に構成するものについてはほとんど知られていません。この論文では、私たちは、Hugging Face Transformers、vLLM、およびOpenAI APIを使用してオープンソースソフトウェアでのLLMの結論構成実践の大規模な実験を紹介しています。私たちは、53898のGitHubリポジトリを分析し、これらのバックエンドの少なくとも

Machine translation (Meta M2M100); the English original follows.

Abstract

LLMs are increasingly integrated into software systems, making deployment and inference configuration a recurring software engineering task. Inference backends expose many options that affect model loading, generation, serving, and request processing. Yet little is known about which of these options developers explicitly configure in practice. In this paper, we present a large-scale empirical study of LLM inference configuration practices in open-source software using Hugging Face Transformers, vLLM, and the OpenAI API. We analyze 5,398 GitHub repositories using at least one of these backends and identify 345,117 explicitly configured option occurrences. Our results show that developers use only a small part of the available configuration space regularly. Although 98.8% of the documented backend-specific options appear at least once, 78.9% are configured in at most 10% of repositories using the corresponding backend. Configurations are also small, with a median of 2 options for Transformers and 4 for vLLM and OpenAI, but the combinations of options vary widely: 56.8-61.6% of multi-option signatures appear in only one repository. We also observe recurring option values as well as substantial variation in how values are chosen. Finally, configuration practices differ across application contexts, with option-adoption differences reaching 30.7 percentage points. These findings characterize the configuration surface developers make explicit rather than the importance or optimality of individual options. They provide empirical evidence for prioritizing future studies of inference configuration and for improving backend documentation and configuration support.

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Topics: Software deployment, Documentation, Inference, Computer science, Software engineering, Software

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