The science of scaling language models

Scaling Language Models: A Practical Engineering Guide has landed on GitHub, offering deep coverage of TPU/GPU architecture, distributed training pipelines, parallelization strategies including tensor and pipeline parallelism, and concrete memory and cost modeling for large LLMs. ML engineers and infrastructure teams navigating multi-node training at scale will find actionable frameworks missing from most academic literature. This fills a critical gap left by scattered blog posts and whitepapers, consolidating hardware-aware scaling knowledge into a single structured reference.