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

=== Assistant: Cool, let’s put actual dollar signs on this. ===
Cool, let’s put actual dollar signs on this.

I’ll do:
# Cost to train a Llama-3.1-class 405B model (Meta’s run)
# Cost to train an 8B Zebra-Llama-style hybrid
# “Collection of models” equivalence: how many hybrids you get for one 400B run

I’ll be explicit about assumptions so you can swap your own numbers.

==== 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.

==== 2. Cost of a Zebra-Llama-style 8B hybrid on MI450X ====

Now compare that to post-training a hybrid (1–8B) starting from an existing foundation.

===== 2.1 GPU-hours for a hybrid =====

AMD HybridLM / Zebra-Llama prototypes report: ROCm Blog<ref>{{cite web|title=ROCm Blog|url=https://rocm.blogs.amd.com/artificial-intelligence/hybrid-models%2C-mla%2C/README.html|publisher=ROCm Blog|access-date=2025-11-16}}</ref>
* 1B hybrid (HybridLM), 7B tokens, trained in ~17 hr on 8 × MI300 - That’s 136 GPU-hours.
* Other variants at 10B tokens are in the same order of magnitude (a few hundred GPU-hours).

If we scale that to an 8B hybrid:
* 8× parameters ⇒ ~8× compute.
* 7B → 11B tokens ⇒ ~1.6×.
* Rough GPU-hr:

136×8×1.6≈1,700 GPU-hours136 \times 8 \times 1.6 \approx 1{,}700 \text{ GPU-hours}136×8×1.6≈1,700 GPU-hours
So a Zebra-Llama-8B-class post-train is probably in the 1–3k GPU-hr range. I’ll use 1,700 GPU-hr as a mid estimate.

===== 2.2 $/GPU-hr for MI450X =====

We don’t have MI450X cloud prices yet, but we do have MI300X:
* MI300X on-demand ranges from $1.5–$3.3 / GPU-hr depending on provider (Vultr, TensorWave, RunPod, etc.). Thunder Compute<ref>{{cite web|title=Thunder Compute|url=https://www.thundercompute.com/blog/amd-mi300x-pricing|publisher=Thunder Compute|access-date=2025-11-16}}</ref>

MI450X will be newer and denser; to be conservative I’ll assume:
* Effective range: $2.5–$3.0 / GPU-hr (similar to “nice” MI300X or discounted H100).

===== 2.3 Cost per 8B hybrid =====

Using 1,700 GPU-hr:
* Low (2.5 $/hr): 1,700×$2.5≈$4,2501{,}700 \times \$2.5 \approx \$4{,}2501,700×$2.5≈$4,250
* High (3.0 $/hr): 1,700×$3.0≈$5,1001{,}700 \times \$3.0 \approx \$5{,}1001,700×$3.0≈$5,100

So one full 8B Zebra-Llama-style post-train is on the order of $4–5k of GPU time.

Power cost: if MI450X is ~1kW while training, then:

1,700 hr×1 kW=1,700 kWh1{,}700\text{ hr} \times 1\text{ kW} = 1{,}700\text{ kWh}1,700 hr×1 kW=1,700 kWh
At $0.14/kWh → $238. So energy is basically noise compared to GPU cost for small models.

: Takeaway: A full 8B hybrid post-train is in the low thousands of dollars in raw compute, not millions.

==== 3. Cost equivalence: one 400B run vs a collection of hybrids ====

Now the fun part: how many 8B hybrids can you train for the cost of one 400B pre-train?

===== 3.1 Direct ratio =====

Using:
* 400B run cost: $92.5–$185M (GPU only)
* 8B hybrid cost: $4.25–$5.1k

Two useful edges:
# Optimistic for the 400B (cheap H100) vs expensive hybrids - Numerator: $92.5M - Denominator: $5.1k → ≈18,000 8B hybrids\approx 18{,}000\ \text{8B hybrids}≈18,000 8B hybrids
# Pessimistic for the 400B (expensive H100) vs cheap hybrids - Numerator: $185M - Denominator: $4.25k → ≈43,000 8B hybrids\approx 43{,}000\ \text{8B hybrids}≈43,000 8B hybrids

So the cost of ''one'' Llama-3-class 400B pre-train roughly buys you:

: ~18k–40k post-trained 8B Zebra-Llama-style models.

Even if my hybrid GPU-hrs estimate is off by a big factor (say 3×), you’re still in the many thousands of hybrids per 400B run.

===== 3.2 Concrete “collection” examples =====

Let’s say you want a fleet of task-/tenant-specific models:

====== Example A – 1,000-model fleet (per-team/per-customer variants) ======
* 1,000 × 8B hybrids × $5k ≈ $5M in GPU time
* That’s: - ~5% of a cheap 400B run (~$100M) - ~2–3% of an expensive 400B run (~$200M)

So one 400B pre-train funds ~20 such 1,000-model fleets.

====== Example B – 100 “premium” hybrids (extra tokens, extra tuning) ======

Even if you triple the cost per hybrid (heavier SFT, RL, eval):
* Per hybrid: ~$15k
* 100 models: $1.5M

Still <2% of a single 400B foundation training cycle.

===== 3.3 How this maps to an AI factory =====

If you have:
* A Rubin VR-200 “foundation plant”: - You swallow $100–$200M to birth one frontier 400B model.
* A MI450X Helios “post-training refinery”: - You spend single-digit millions per year to continuously spin out: - Per-domain hybrids (code, legal, finance, internal docs) - Per-tenant or per-product instances - Experiments (architecture variants, safety variants, tooling variants)

Economically:
* 400B runs are rare, capital-intensive events.
* Post-training hybrids are cheap, repeatable SKUs you can generate by the hundreds or thousands for the same budget.

==== 4. Summary in one sentence ====

At current-ish GPU economics, one Llama-3-class 400B pre-train (on H100/Rubin-equivalent hardware) costs in the ballpark of $100–200M, while a full Zebra-Llama-style 8B post-train on MI450X is only $4–5k, so a single 400B foundation run is roughly equivalent in raw compute dollars to training on the order of ''tens of thousands'' of 8B hybrid models.

If you want, next step we can build a little table:
* rows = “budget” ($10M, $50M, $100M)
* columns = “# 400B runs”, “# 70B runs”, “# 8B hybrids”

and plug in your own $/GPU-hr & power assumptions to stress-test AI-factory ROI.