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

==== 1. Cost of a 400B Llama-3-style pre-train (H100 → Rubin VR-200) ====

===== 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.

===== 1.2 $/GPU-hour assumptions =====

Public cloud H100 on-demand ballpark (late 2025):
* Thundercompute & similar trackers show H100 at roughly $1.9–$6/GPU-hr depending on provider and commitment. Thunder Compute<ref>{{cite web|title=Thunder Compute|url=https://www.thundercompute.com/blog/nvidia-h100-pricing|publisher=Thunder Compute|access-date=2025-11-16}}</ref>
* Other guides show typical on-demand around $3–$6/GPU-hr. Jarvis Labs Docs<ref>{{cite web|title=Jarvis Labs Docs|url=https://docs.jarvislabs.ai/blog/h100-price|publisher=Jarvis Labs Docs|access-date=2025-11-16}}</ref>

For a big hyperscaler, effective cost per GPU-hr is lower than list price if you own hardware, but you’re then amortizing capex instead. Using cloud-style numbers gives you a clean upper bound.

Let’s bracket it with:
* Low: $3 / GPU-hr (big buyer / efficient own hardware equivalent)
* High: $6 / GPU-hr (retail-ish cloud)

===== 1.3 Direct compute $ for the 400B run =====

GPU cost:
* Low: 30.84M hr×$3/hr≈$92.5M30.84\text{M hr} \times \$3/\text{hr} \approx \$92.5\text{M}30.84M hr×$3/hr≈$92.5M
* High: 30.84M hr×$6/hr≈$185M30.84\text{M hr} \times \$6/\text{hr} \approx \$185\text{M}30.84M hr×$6/hr≈$185M

So call it ~$100–$200M of GPU time to pre-train one Llama-3-class 400B dense model if you priced it at current H100 cloud economics.

===== 1.4 Power cost (just to sanity-check scale) =====

H100-80GB TDP is ~700 W. Google Cloud<ref>{{cite web|title=Google Cloud|url=https://console.cloud.google.com/vertex-ai/publishers/meta/model-garden/llama-3.1-405b-instruct-maas?hl=de|publisher=Google Cloud|access-date=2025-11-16}}</ref>

Energy used:

30.84M hr×0.7 kW=21.6M kWh30.84\text{M hr} \times 0.7\text{ kW} = 21.6\text{M kWh}30.84M hr×0.7 kW=21.6M kWh
Average US all-sector electricity price ≈ 13–14¢/kWh in mid-2020s. AAF<ref>{{cite web|title=AAF|url=https://www.americanactionforum.org/insight/how-much-are-electricity-prices-rising-and-why/|publisher=americanactionforum.org|access-date=2025-11-16}}</ref>

Power cost range:
* At $0.12/kWh → ≈ $2.6M
* At $0.16/kWh → ≈ $3.5M

So energy is a few million, dwarfed by GPU capex/rental, but non-trivial.

: Takeaway: One Llama-3.1-405B-class pre-train is on the order of
: $100–$200M (compute), plus $3–$5M of energy, plus infra/engineering.

Rubin VR-200 just changes the constant factor (fewer GPU-hrs at higher $/GPU-hr), but you’re still very solidly in the nine-figure ballpark for a frontier 400B run.