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

===== 1.1 Compute volume =====

Meta reports for Llama-3.1: Hugging Face<ref>{{cite web|title=Hugging Face|url=https://huggingface.co/blog/llama31|publisher=Hugging Face|access-date=2025-11-16}}</ref>
* Total for all 3 models: 39.3M H100-80GB GPU-hours
* Of that, the 405B model used ≈ 30.84M GPU-hours

I’ll use 30.84M GPU-hours as the 400B pre-train cost.

Rubin VR-200 should be faster than H100, but you still need essentially the same total FLOPs. If VR-200 is, say, ~2× faster than H100, you might cut this to ~15M GPU-hours, but I’ll keep the 30.8M number as an H100-equivalent so it’s easy to scale.