Will Nvidia Vera Rubin actually make LLM pre-training faster? And are 10T+ parameter models next?

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Summary

The article questions whether Nvidia's Vera Rubin will accelerate LLM pre-training and explores if models with over 10 trillion parameters are feasible, considering constraints like data, power, and cost.

Nvidia's Vera Rubin numbers look huge, but most of the headline gains seem tied to low-precision formats and inference. How much of that actually carries over to pre-training? Also curious whether we'll start seeing 10T+ parameter models, or if data, power, and cost are the real limits now, and the focus stays on MoE and better data instead of just going bigger. Anyone with hardware or training experience, I'd love to hear your take.
Original Article

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