Scaling LLM training: parallelism choices
A playbook published on April 6, 2026 describes parallelism strategies for training LLMs on GPU clusters, drawing on 4,000 experiments with up to 512 GPUs.
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What happened and what to do
The playbook, published on April 6, 2026, covers data, expert, tensor, pipeline, and context parallelism for training LLMs on GPU clusters. Its empirical examples span 4,000 scaling experiments with up to 512 GPUs and relate parallelism choices to memory use, pipeline overhead, and cluster topology.
A company training models can apply these criteria to compare configurations and identify bottlenecks before expanding infrastructure. A project could instrument experiments, collect memory-use and runtime metrics, and display findings in a dashboard to inform decisions about parallelism and cluster capacity.
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Wendelmaques can diagnose training infrastructure and workflows, define metrics and project scope, and implement data collection, monitoring, and analysis pipelines, with support for operating the solution.
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