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Skepticism over rumors of failed R2 training on unproven hardware

One post argues that the team behind the rumored R2 model would have trained something small first, rather than risk a frontier model on unfamiliar hardware when its advantage was mastery of NVIDIA's stack.

Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)T(
2 Sources, 20d ago, first seen 20d ago

TLDR

A post challenges rumors of a failed attempt to train a frontier model called R2 on unproven hardware. The author questions why a team whose advantage was, in their view, unmatched mastery of NVIDIA's stack would take that approach, arguing that it would have trained a small model first.

Combined views

8.3K

2 Sources, first seen 20d ago

70 likes5 comments7 saves2 reposts

Combined views

8.3K

2 Sources, first seen 20d ago

70 likes5 comments7 saves2 reposts

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Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)@teortaxesTexit's very frustrating how badly models scale. In principle, 250B active is 250B active, Meta did 405B active on 16K H100s 2 years ago. Sparsity adds some MFU penalty, but eh. There is still too much alchemy involved. You'll just waste months of compute if you bumrush this scale.20d
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    2 Sources

    Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)@teortaxesTexit's very frustrating how badly models scale. In principle, 250B active is 250B active, Meta did 405B active on 16K H100s 2 years ago. Sparsity adds some MFU penalty, but eh. There is still too much alchemy involved. You'll just waste months of compute if you bumrush this scale.20d
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