making data loaders go brr (deterministically) | mts @datologyai | prev. Ph.D. @SystemsGroupETH @anaklimovic

Zurich, Switzerland
Joined October 2012
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. 🧵
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Maximilian Böther retweeted
Awesome to see Zephon, our recently open sourced data loader, integrated with @PyTorch TorchTitan! Check out their blog, and congrats to @MaxiBoether, @josh_wills, and @SGui. We're just getting started with Zephon!
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…
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Very happy that the Zephon team and Pytorch/Torchtitan are working together to make data loading more deterministic. Check out our example integration: github.com/datologyai/torcht… Check out our blog post together with the folks at Meta 👇
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…
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Maximilian Böther retweeted
MosaicML’s Streaming dataloader was one of the most underrated pieces of ML infrastructure ever built imo. It spoiled me. One of my goals when founding Datology was to take all the best things about Streaming and build its torchbearer. Because well-designed dataloaders enable entire domains of data experimentation like real-time curricula, arbitrary mixing schedules, and seamless transitions between pre- and post-training. And even better-designed dataloaders enable productionizing and scaling those experiments. I am so excited that we're finally releasing Zephon. It leveled up Datology and we think it'll level you up, too.
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. 🧵
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A data loader is only as useful as the data it loads. This is why this week is so exciting: we're sharing our Curation Studio and how we use it to generate the best tokens (or raw strings to be tokenized and packed on-the-fly) possible. Check out our release announcement!
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.
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Maximilian Böther retweeted

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[9/] Tweets are way too short to cover the pitfalls around state management in data loading and offline materialization. Hence we wrote a whole blog post around it: datologyai.com/blog/zephon We’re also excited to share our research paper: arxiv.org/abs/2610.03087
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[10/] This is a collaboration with many amazing people at @datologyai: @josh_wills, Ties Robroek, @hurrycane, @arimorcos, and many more, but of course also my PhD supervisor @anaklimovic was a tremendous help over the course of the project. Please reach out if you’re interested!
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Maximilian Böther retweeted
Maybe a bit late, but I wanted to comment on this: I think the AI boom made people forget what a PhD was meant for. Now it's going back to its original purpose: not short-term profit, but long-term research that might not have a practical use for another 10 or 20 years -> Backprop existed decades before the deep learning boom. A PhD isn't for everyone, and that's okay. Industry may suit you better if you want to build something useful now, or academia if you believe in a problem industry doesn't care about yet. The AI boom made a PhD look like preparation for an industry job, but that was never its main purpose.
Students sometimes ask me if it still makes sense, in this accelerating age, to pursue a PhD in AI. Perhaps counterintuitively, I think it's a great time to do so. I wrote up some thoughts on this here: web.mit.edu/phillipi/www/wri…
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Maximilian Böther retweeted
Of course, love me some confirmation bias! DCLM was for text, DCVLM is the same for vision: analyzing VLM data mix across scales. Filtering is no bueno, but the "type" mixing matters, with unfortunately (but imo expectedly) the best small-scale mix != the best mid-scale mix.
Replying to @vishaal_urao
Finding 1: Data filtering barely helps for VLMs! 🤯 Tested 60+ filter configs (CLIP-score, DCLM-fasttext, Nemotron classifiers, UniFilter, perplexity). Best case: +0.8pp over no-filtering. Most filters do nothing or actively hurt! @giffmana @tatsu_hashimoto you might like this!
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DataComp-VLM aka DCVLM is finally out! It was such a pleasure working with this team of cracked ML data folks. Check out what changes in comparison to DCLM as we have surprising findings for our VLM data pool. Models are what they eat, and it continues to show!
🚀New Paper arxiv.org/abs/2606.28551 Everyone obsesses over VLM architectures & training recipes. But what about the data? Presenting the latest work in the DataComp-series: a testbed for VLM data curation with 1,000+ controlled experiments and some surprising lessons 👀 🧵👇
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Maximilian Böther retweeted
There are very few things that have radicalized me as much as founding a UG in Germany
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The misery around recent decision for EU Frontier AI Grand Challenge reveals again strong deficits in EU decision making and its lack of basic competence in ML/AI. Hard to explain otherwise how an entity without a track of record in NeurIPS/ICLR/ICML gets to do "Frontier AI".
So maybe worth a longer note. 1. Domyn is reportedly building a 6k Blackwell cluster in Italy. Meanwhile the call grants about 2.5% of all EuroHPC capacity (yeah it’s bizarre accounting) about 1k h100 cluster for a year, so a tiny fraction of Domyn’s capacity for next year which just… doesn’t make sense. Well capitalized companies hardly ever ask for public compute because it add up frictions, as you cannot use the in house stack (from what I know in France, the allocation of this type went mostly unused). That is if the promised cluster really exists this year and in the promised shape. It has already been postponed multiple times. 2. Domyn (formerly known as iGenius) never pretrained a large model from scratch. The 355b Colossus is a continuous pretrain of Nemotron with not much to show for it (the headline is just an MMLU score). Almost nothing published, hence why hardly anyone in EU AI research knows them. No public deployment story either. Still it does match almost too well the call requirements: an EU company, that "trained or deployed" >100b model, no need for it to be a full pretrain. 3. Financials are fuzzy as well. The 650m headline raise is part debt for cluster buildout, annual revenue seemingly running in the 10-20m range. What we have seems closer to a midsized company creatively rewriting itself to either a lab or an hyperscaler thanks to a sovereign push, and maybe the public compute was actually needed to keep it on. I must say my first reaction when I heard of this call was: who was it made for? At least I have an answer.
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