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Google reproduces AI2's OLMo 3 7B model on TPUs using MaxText
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Google successfully reproduced AI2's OLMo 3 7B model from scratch on Cloud TPUs with MaxText, achieving identical metrics to the original PyTorch reference across full training stages.
The reproduction validates MaxText's reliability for large-scale TPU training and demonstrates faithful framework portability of open frontier models. It confirms that JAX/XLA can match PyTorch performance on TPUs without recipe changes, enabling engineers to adopt MaxText for equivalent model training with comparable efficiency.
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MaxText matched the original PyTorch reference on all held-out evaluations, including top-1 token agreement at 98.75% for 8192-token contexts.
Training resumed exactly after host failures with zero loss deviation, proving checkpoint-and-retry reliability across multi-week runs.
Architecture reshaping to 16×256 head dimensions boosted speed by 12.4% on Ironwood TPUs while preserving identical loss curves.
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The reproduction proves MaxText can faithfully execute PyTorch-based training recipes on TPUs, matching held-out metrics rather than just loss curves. This validates the framework's correctness for engineers porting models to TPUs.
Adopting MaxText requires maintaining identical training scripts and data pipelines, as even minor deviations cause measurable metric drift. The study shows checkpoint resumption works only with exact script replication, demanding strict operational discipline.
Performance gains emerged from specific hardware-aware optimizations like head-dim reshaping to 256×256 on Ironwood, which fully utilized MXU capabilities. Without such co-design, throughput would not match the original 57.4% MFU achieved.
The study's focus on stage-1 pre-training limits direct conclusions about mid-training or long-context adaptation, leaving uncertainty about scalability for full OLMo 3 pipeline stages. Engineers must verify each stage independently.
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