MagicaLabs says $4M pretraining beat all public base models, 50x more compute-efficient than DeepSeek
magicailabs · x · 2026-09-09
MagicaLabs claims its new pretraining recipe, scaled 10x for roughly $4M, exceeds all publicly available base models — a capability that would cost over $100M under DeepSeek V4 Pro's recipe.
- The bet is algorithmic efficiency over chip count: the recipe matches DeepSeek V4 Pro's pretraining with 50x less compute, roughly half the FLOPs of GPT-3, or $0.5M on GB200.
- The gains come from tens of multiplicative changes across architecture, optimizer, training objective, and data; the team found fixing minor bugs is itself a compute multiplier.
- A short math RL run from base was used to sanity-check post-RL performance, and the team says it won't stop scaling there.
- These are self-reported claims with no third-party replication yet.
Related event: Magic Claims to Match DeepSeek V4 Pro Pretraining with 1/50 the Compute(11 posts)→
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