Large Language Model (LLM) Pre-training Frameworks

About this course

“This course contains the use of artificial intelligence.”

Building or scaling Large Language Models (LLMs) requires navigating massive compute costs, complex data curation pipelines, and distributed system architectures. A single unoptimized pre-training run or miscalculated scaling law can result in significant resource waste and exponential API cost overruns. Understanding the exact mechanics of model initialization and optimization is critical for maintaining unit economics in enterprise AI initiatives.

This course provides a comprehensive architectural briefing on the pre-training frameworks that power modern foundation models. Moving beyond abstract concepts, the curriculum examines the precise engineering decisions required to train decoder-only transformers at scale. Learners will explore the mechanics of subword tokenization, the mathematical foundations of cross-entropy loss, and the infrastructure demands of three-dimensional parallelism. By analyzing the intersection of parameters, data, and compute, participants will learn how to architect stable, cost-effective training runs.

Frequently Asked Questions

What are LLM scaling laws?

Scaling laws are empirical formulas that predict a language model's final loss based on compute, parameter count, and dataset size. They allow engineering teams to forecast performance and optimize resource allocation before committing to expensive, full-scale training runs.

How does distributed training work for LLMs?

Distributed training divides computational load across multiple accelerators. It utilizes data parallelism to distribute batches, tensor parallelism to split matrix operations, and pipeline parallelism to process distinct transformer layers across a synchronized network of hardware.

What is compute-optimal training?

Compute-optimal training identifies the ideal ratio of model size to training tokens for a fixed compute budget. Current methodologies e

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