In the wave of AI, open-source large language models (LLMs) such as LLaMA, Gemma, and DeepSeek are reshaping the technological landscape, but how to efficiently deploy these models from prototype to production remains a challenge for developers. This presentation will share how to build a scalable and efficient LLMOps pipeline using Kubernetes and open-source tools, covering the entire process from model download to inference optimization. Based on my experience at DaoCloud and open-source projects (such as the Hugging Face model download GUI and Kubernetes configuration tool), I will demonstrate how cloud-native technologies can simplify LLM deployment, including practical cases of automated model distribution, dynamic resource scheduling, and inference acceleration.