, tokenization, packing, mixing) that are common in foundation model training.
Zephon data loader provides deterministic training across GPU topology changes
The system guarantees identical batch sequences for stateful pipelines involving tokenization, packing, and mixing, regardless of GPU count or resumption.
Top University
Maximilian Böther · Josh Wills · Ties Robroek · Sonnet Xu · Paul Burstein · Daniel Zayas · +11 more
DatologyAI · ETH Zürich · ITU Copenhagen · Stanford University · Arcee AI
Research Digest··3 min read
The authors present Zephon, a data loader for foundation models that delivers elastic determinism—identical global batches despite changes in GPU topology—for online, stateful data pipelines.
Why this paper
From Stanford University and 5 others
In one line
Zephon keeps online, stateful foundation-model data sequences identical across GPU topology changes, checkpoint resumes, and processing backends without offline materialization.
What we could check
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