Playbook outlines large-scale LLM training practices
Published on April 6, 2026, the material covers data, expert, tensor, pipeline, and context parallelism in GPU clusters, with findings from 4,000 scaling experiments using up to 512 GPUs.
A technical playbook on large-scale LLM training describes five approaches to parallelism: data, expert, tensor, pipeline, and context. It includes empirical examples from 4,000 scaling experiments, using up to 512 GPUs.
The publication relates parallelism choices to memory use, pipeline overhead, and cluster topology. To check its scope and findings, consult the original playbook and verify how its experiments support each comparison; results are not guaranteed to transfer to other configurations. If using AI to study or apply the material, avoid submitting identifiable organizational data and follow internal data protection policies.