This paper presents a comprehensive overview of the LLM supply chain, highlighting its three core elements: 1) model infrastructure, encompassing datasets and toolchain for training, optimization, and deployment; 2) model lifecycle, covering training, testing, releasing, and ongoing maintenance; and 3) downstream application ecosystem, enabling the integration of pre-trained models into a wide range of intelligent applications. The LLM supply chain encompasses the entire lifecycle of pre-trained models, from initial development and training to final deployment and application in various domains. However, this rapidly evolving field faces numerous challenges across these key components, including data privacy and security, model interpretability and fairness, infrastructure scalability, and regulatory compliance. Addressing these challenges is essential for harnessing the full potential of LLMs and ensuring their ethical and responsible use. The paper provides a future research agenda for the LLM supply chain, aiming at driving the continued advancement and responsible deployment of these transformative LLMs. The LLM supply chain is defined as the network of relationships that encompass the development, distribution, and deployment of models, including upstream model development communities, model repositories, distribution platforms, and app markets, as well as data providers, toolchain/model developers, maintainers, and end-users. The supply chain can be further divided into three key components: fundamental infrastructure, model lifecycle, and downstream application ecosystem. The paper discusses the challenges and opportunities in each component, including data cleaning and curation, data poisoning, license management, robust toolchain, inner alignment, helpfulness testing, honesty testing, harmlessness testing, model dependency analysis, risk propagation mitigation, model drift, continual learning, and model compression. The paper also explores the vision for the downstream application ecosystem, including the revolutionary LLM app store and ubiquitous on-device LLMs. The conclusion emphasizes the importance of addressing these challenges and opportunities to ensure the safe, reliable, and equitable deployment of LLMs.This paper presents a comprehensive overview of the LLM supply chain, highlighting its three core elements: 1) model infrastructure, encompassing datasets and toolchain for training, optimization, and deployment; 2) model lifecycle, covering training, testing, releasing, and ongoing maintenance; and 3) downstream application ecosystem, enabling the integration of pre-trained models into a wide range of intelligent applications. The LLM supply chain encompasses the entire lifecycle of pre-trained models, from initial development and training to final deployment and application in various domains. However, this rapidly evolving field faces numerous challenges across these key components, including data privacy and security, model interpretability and fairness, infrastructure scalability, and regulatory compliance. Addressing these challenges is essential for harnessing the full potential of LLMs and ensuring their ethical and responsible use. The paper provides a future research agenda for the LLM supply chain, aiming at driving the continued advancement and responsible deployment of these transformative LLMs. The LLM supply chain is defined as the network of relationships that encompass the development, distribution, and deployment of models, including upstream model development communities, model repositories, distribution platforms, and app markets, as well as data providers, toolchain/model developers, maintainers, and end-users. The supply chain can be further divided into three key components: fundamental infrastructure, model lifecycle, and downstream application ecosystem. The paper discusses the challenges and opportunities in each component, including data cleaning and curation, data poisoning, license management, robust toolchain, inner alignment, helpfulness testing, honesty testing, harmlessness testing, model dependency analysis, risk propagation mitigation, model drift, continual learning, and model compression. The paper also explores the vision for the downstream application ecosystem, including the revolutionary LLM app store and ubiquitous on-device LLMs. The conclusion emphasizes the importance of addressing these challenges and opportunities to ensure the safe, reliable, and equitable deployment of LLMs.