Rumor: US frontier labs burn massive GPUs chasing a final 1-3% model gain
SumitGup · x · 2026-09-05
- An industry account relays an explanation for why US frontier labs throw staggering GPU numbers at training: they reportedly use an inefficient approach that creates multiple pools of candidate models, massively expanding them to explore possibilities before selecting the best.
- Claim: the performance gap between a middling model in those pools and the very best is only about 1-3%, yet labs pour enormous compute into that last margin.
- Context: the previous post noted a model (Astra) trained on 100,000 GPUs. Unconfirmed rumor.
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