Operating large language models introduces infrastructure and data management requirements significantly different from smaller models. Handling training datasets that can reach petabyte scale ($PB$) and coordinating hundreds or thousands of GPUs ($N_{GPU}$) demands specific architectural patterns and operational practices. Simply scaling standard MLOps techniques often proves insufficient or prohibitively expensive.

This chapter focuses on the practical aspects of building and managing the foundation for LLM operations. You will learn to:

• Design scalable compute clusters (GPU/TPU) and address the network considerations essential for efficient distributed processing.
• Implement strategies for storing, accessing, and preprocessing extremely large datasets.
• Develop effective data pipelines tailored for LLM needs, including cleaning and tokenization.
• Apply version control methods appropriate for large-scale data and model artifacts.
• Evaluate the trade-offs between cloud-based and on-premise infrastructure solutions for LLMOps.

We begin by examining the design principles for scalable compute and networking, then move into managing the data itself, from storage and preprocessing to versioning.