Editing Openai/69191dd5-6360-800d-b47b-7ebcc8b69dc8 (section)

==== If you imagine a 1 GW AI factory, you’d use these two very differently: ====

===== - Train a small number of frontier 400B-class models. =====
* This is like building one super-refinery: huge, rare, insanely capital intensive.
* Its output (weights / logits) can be: - Served directly for premium workloads. - Used to distill and post-train many smaller models elsewhere.

===== - Take the already-trained 400B teacher (maybe trained on Rubin, maybe externally). =====
* On MI450X, do: - Zebra-Llama-style post-training → 1–8B hybrids tuned for: - Lower KV cache. - Better long-context efficiency. - Specific domains (code, finance, internal docs, etc.). - SFT, RAG-alignment, safety tuning.
* Economically: - For the power and cost of one 400B Rubin pre-train, you can train thousands of hybrids on MI450X. - That lets you match models to workloads and chase tokens-per-dollar instead of “biggest possible model at all times”.