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

==== Comparing “Zebra-Llama on MI450X” vs “standard Llama-3 400B on Rubin VR-200”: ====
* Training scale: - VR-200 + 400B Llama-3 = 15T-token, 10⁷–10⁸ GPU-hour endeavor. - MI450X + Zebra-Llama-style hybrid = 10¹⁰-token, 10³-GPU-hour endeavor. → The hybrid route is ~10⁴× cheaper in GPU-hours per model.
* Hardware fit: - Rubin VR-200 is overkill for hybrids; it shines on frontier pre-training. - MI450X Helios is almost perfect for post-training lots of small/medium models and serving them, especially with huge HBM4 capacity and ROCm-tuned hybrid kernels.
* Economic role: - You justify Rubin VR-200 only if you’re in the business of creating new frontier foundations. - You justify MI450X + Zebra-Llama if you want to exploit those foundations at scale, cheaply, and with workload-specific tuning.

If you want, next step we can numerically budget:
* “How many Zebra-Llama-8B hybrids can I post-train per year on a 1 GW MI450X site?” vs
* “How many Llama-3-400B-class pre-trains can I run per year on a 1 GW Rubin VR-200 site?”

…and turn that into a tokens-per-year / dollars-per-model comparison.