  The rapid advancements in pre-trained Large Language Models (LLMs) and Large
Multimodal Models (LMMs) have ushered in a new era of intelligent applications,
transforming fields ranging from natural language processing to content
generation. The LLM supply chain represents a crucial aspect of the
contemporary artificial intelligence landscape. It encompasses the entire
lifecycle of pre-trained models, from its initial development and training to
its final deployment and application in various domains. This paper presents a
comprehensive overview of the LLM supply chain, highlighting its three core
elements: 1) the model infrastructure, encompassing datasets and toolchain for
training, optimization, and deployment; 2) the model lifecycle, covering
training, testing, releasing, and ongoing maintenance; and 3) the downstream
application ecosystem, enabling the integration of pre-trained models into a
wide range of intelligent applications. 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. This paper provides a future research agenda for the LLM
supply chain, aiming at driving the continued advancement and responsible
deployment of these transformative LLMs.
