Addressing the widespread reliance on cloud-based Application Programming Interfaces (APIs) in AI courses at vocational colleges and students’ limited practical experience in localized model deployment, this study systematically reviews the research status and implementation approaches of localized deployment of open-source large language models (LLMs) in vocational AI training programs. Through bibliometric analysis, case study analysis, and theoretical framework construction, we synthesize domestic and international research findings since 2020, focusing on the technological evolution of open-source LLMs, existing challenges in vocational AI education, and critical elements of localized course design. The study reveals a significant structural imbalance in vocational AI education characterized by “technological maturity versus pedagogical lag”: while open-source models like LLaMA3 and Qwen2.5 can efficiently run on consumer-grade GPUs, and toolchains such as Ollama and vLLM have substantially reduced deployment barriers, over 90% of institutions still operate AI courses at the cloud API invocation level. Building on this, we propose a localized course design framework encompassing three dimensions: technology stack selection, curriculum module design, and industry-academia collaboration mechanisms. The Ollama platform integrated with the vLLM toolchain employs Low-Rank Adaptation (LoRA) lightweight fine-tuning technology, representing the most technically viable and pedagogically applicable localized AI training solution for vocational colleges. The school-enterprise “dual-track” curriculum collaboration mechanism forms the institutional foundation for ensuring high-quality implementation of such courses. The proposed “theory-instrument-practice” three-stage framework provides replicable reference models for peer institutions across the province.