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Training & fine-tuning

Build the plan around accepted progress.

Long-running training connects model fit, collective communication, data delivery and recoverability. Fine-tuning may have a very different memory and duration profile from pretraining.

Define the run

Record the model, precision, sequence length, batch strategy and target quality. Include optimiser and activation memory, not just parameter weights. Different parallelism methods change the communication pattern and where memory resides.

Protect the run

Plan dataset staging, checkpoints and restarts. A short single-node test does not establish multi-node scaling or recovery behaviour. Measure elapsed time to an accepted checkpoint and document what happens if a node or storage path fails.

Choose the operating model

Slurm and Kubernetes address different workflow and operating needs; a managed service can connect them. Ask which team manages identities, scheduling, images and recovery. Compare like-for-like sustained progress, service terms and data movement.

Prepare a comparable test

Keep the workload, software version, data path, service objective and cost boundary attached to the result. Changing these assumptions changes what a comparison means.

Official references: C3, C6. Checked 8 November 2026. Company descriptions remain attributed; announced plans retain their conditions.

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