Study Reveals vLLM as Most Adopted LLM Serving Framework in Open Source
An empirical investigation published on arXiv (2608.03036) explores the integration of LLM serving frameworks and methodologies within open-source software. The study highlights five specific frameworks: vLLM, SGLang, TensorRT-LLM, LMDeploy, and FlashInfer, assessing their individual and combined usage across various LLM categories. Additionally, it analyzes differences in repositories regarding intent, focus, use cases, and architectural designs. Findings reveal that vLLM stands out as the most prominent framework in practice. This research fills a knowledge gap concerning the practical adoption of these technologies, which is crucial as LLMs become more embedded in AI services and systems, emphasizing the need for efficient serving. Insights into framework usage can assist developers and researchers in tool selection.
Key facts
- The study is published on arXiv with identifier 2608.03036.
- It focuses on LLM serving frameworks in open-source software systems.
- Five frameworks were analyzed: vLLM, SGLang, TensorRT-LLM, LMDeploy, and FlashInfer.
- The study examines individual and combined adoption of these frameworks.
- Adoption is analyzed across different categories of LLMs.
- Repositories are compared based on intent, focus, use case, and architectural design.
- vLLM is identified as the most visible framework.
- The research fills a gap in knowledge about practical adoption of LLM serving techniques.
Entities
Institutions
- arXiv