OpenAFS for Artificial Intelligence Computing - Use Case 1

EntSun -- OpenAFS can play a strategic role in modern AI / LLM infrastructure, especially in large-scale, multi-node or hybrid environments. Sometimes it is best to fence off a system used for training with access to legacy systems.

Use case: When training large language models (LLMs), teams often need to provide multiple compute nodes access to the same massive dataset — text corpora, tokenized datasets, checkpoints, and logs.

OpenAFS advantage:

Provides transparent, consistent access to shared files across many nodes.

Supports caching, reducing I/O load and network strain during distributed training.

Kerberos-based authentication allows secure data access across nodes or even institutions.

Example:

An HPC cluster running a distributed PyTorch training job can mount /afs/data/llm-corpus across 50+ nodes, each caching hot data locally while maintaining consistency for updates.

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Sine Nomine Associates has a long history going back over 25 years to the beginning of OpenAFS. Our team of experts helps many in the financial industry with repeated support services contracts.

Our in-house team includes two of the maintainers for the OpenAFS open-source code. Our unique capabilities have kept OpenAFS up to date with modern tools such as ansible.

Margarete Ziemer, CEO, emphasizes: "We are proud to say we continue to support the open-source community with a sharp focus on the mainframe and OpenAFS areas. We sincerely thank all our past and current customers and our associates. We are always ready to tackle your problems with custom solutions, particularly in the open-source space."

For more information on Sine Nomine Associates and their products, please visit our website.
https://sinenomine.net (https://sinenomine.net/index.php/openafs/)