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

Nada Zine, Matias Martinez, Romain Rouvoy, Clément Quinton

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…