Kant: Nvidia GB300 allows models to jump from 1T to 6T parameters
Eiso Kant · The AI Frontier: from open weights to open research — Eiso Kant, Poolside AI · Jul 22, 2026 · at 1:36:46
Eiso Kant, co-founder and CEO of Poolside AI, explains how generational leaps in Nvidia hardware architecture dictate foundation model parameter scaling limits.
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“The difference of a model you could train on hoppers versus GB 300 is the difference in a trillion parameter model and a five or six trillion parameter model.”
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