Zephon’s TorchTitan integration preserves identical global batches when training resumes on a different GPU count. In our latest blog’s 1B-parameter model test, training resumed from a 16-GPU checkpoint on 8, 32, and 64 GPUs, and the SHA-256 hash of every global batch matched the original run. Zephon tokenizes, packs, shuffles, and mixes data inside the training job, avoiding a preprocessed copy of the corpus. The data, tokenizer, seed, mixture, context length, and global batch size must stay unchanged when resuming. The lane count must divide evenly across data-parallel replicas, and each optimizer step’s batch must split evenly across lanes. Training losses can still differ because floating-point reduction order depends on GPU count. ✍️ @datologyai @josh_wills @MaxiBoether, Ties Robroek (@datologyai) Read more: pytorch.org/blog/bringing-el…

The gains persisted after post-training: our 30B-A3B model scored 46.8% vs. 37.7% for the baseline, averaged across benchmarks. A curated 12B-A1.4B model also beat the 30B-A3B baseline despite using roughly one-fifth of the pretraining compute.

Today, we’re excited to launch the Datology Curation Studio, customizable frontier data curation for every team training its own models. Data quality is the ultimate compute multiplier, and Curation Studio makes it easy for everyone.

At @datologyai we run thousands of data experiments. Two things keep biting us: - Same run on 16 vs 8 GPUs → different evals - Every pipeline change → re-materialize the dataset Introducing Zephon, the data loader we built to process data on the fly, deterministically. 🧵