Large Language Model Integration in Desktop Applications: A Systematic Literature Review

Abstract

The integration of Large Language Models (LLMs) into desktop applications has accelerated sharply over the past several years, yet no prior systematic review has examined this deployment setting as a distinct research domain — separate from web-based or mobile environments, each carrying different architectural constraints, privacy expectations, and user interaction patterns. This study maps, analyzes, and synthesizes the available scientific evidence on LLM architectures adopted in desktop implementations, the technical barriers arising during integration, the measurable effects on User Experience (UX), and the relative effectiveness of competing deployment strategies. A Systematic Literature Review (SLR) was conducted following PRISMA 2020 reporting guidelines, with research questions structured using the PICOC framework. Systematic searches across seven digital databases — IEEE Xplore, ACM Digital Library, Scopus, Web of Science, Google Scholar, arXiv, and Semantic Scholar — covering 2019–2024 yielded 1,200 initial articles, narrowed to 42 final articles through layered selection and quality appraisal using a modified CASP rubric. GPT-4 and the LLaMA family emerged as the dominant architectures, with model selection driven primarily by parameter scale and privacy requirements. Computational constraints, response latency, and data security ranked as the most serious technical barriers, with Retrieval-Augmented Generation (RAG) and model quantization as the most common mitigations. LLM integration was associated with measurable gains in task efficiency and user satisfaction in 73.8% of reviewed studies, though output predictability and user trust remained persistent challenges. A hybrid deployment approach — combining local models with cloud-based API access — emerged as the most common practice among production implementations. Unlike existing reviews addressing LLM capabilities in general software engineering contexts, this study provides the first systematic map of LLM integration specifically within the desktop application domain, offering concrete guidance for developers working at the intersection of artificial intelligence and desktop software engineering.