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

=== Assistant: Short version: ===
Short version:
* Zebra-Llama on MI450X = “cheap, fast post-training on smaller hybrids”.
* Llama-3 400B on Rubin VR-200 = “insanely expensive frontier pre-training run”.

They live in almost different economic universes.

==== ### ====
* Zebra-Llama / AMD-HybridLM = post-trained hybrid models (1B–8B so far) that: - Start from a pre-trained Transformer (e.g. Llama-style). - Replace many Transformer blocks with MLA + Mamba2 SSM layers. - Use 7–11B tokens of post-training instead of trillions. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/html/2505.17272v1|publisher=arxiv.org|access-date=2025-11-16}}</ref>
* AMD’s HybridLM blog shows e.g. a 1B hybrid trained on 7B tokens in ~17 hours on 8× MI300 → 136 GPU-hours; similar variants with 10B tokens take ~30 hours (~240 GPU-hours). 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>
* Goal: keep accuracy close to the base model while: - Slashing KV-cache by 10–30×. - Improving long-context throughput. - Cutting post-training cost by ~3–4 orders of magnitude vs pre-training.

Now imagine doing that on MI450X instead of MI300:
* Helios racks: up to 72 MI450X per rack, 432 GB HBM4 per GPU, ~1.4 EF FP8 per rack, 31 TB HBM total. Tom's Hardware<ref>{{cite web|title=Tom's Hardware|url=https://www.tomshardware.com/tech-industry/amd-debuts-helios-rack-scale-ai-hardware-platform-at-ocp-global-summit-2025-promises-easier-serviceability-and-50-percent-more-memory-than-nvidias-vera-rubin|publisher=Tom's Hardware|access-date=2025-11-16}}</ref>
* Same post-training recipe basically ports over: 8–16 MI450X nodes chew through 7–11B token runs very quickly, with even better perf/W than MI300.

===== - Meta reports Llama-3.1 405B was trained on 15T+ tokens, using 39.3M H100-80GB GPU-hours (700 W TDP class). Meta AI<ref>{{cite web|title=Meta AI|url=https://ai.meta.com/blog/meta-llama-3-1/|publisher=Meta AI|access-date=2025-11-16}}</ref> =====
* Architecture: vanilla giant decoder-only Transformer (no MoE), extended context, etc. datacamp.com<ref>{{cite web|title=datacamp.com|url=https://www.datacamp.com/blog/llama-3-1-405b-meta-ai|publisher=datacamp.com|access-date=2025-11-16}}</ref>
* Rubin VR-200 is Nvidia’s next-gen data-center training GPU in the Rubin family: - Positioned as the workhorse training GPU alongside Rubin CPX (long-context prefill) and R100 decode parts. Futurum<ref>{{cite web|title=Futurum|url=https://futurumgroup.com/insights/nvidias-new-rubin-cpx-targets-future-of-large-scale-inference/|publisher=futurumgroup.com|access-date=2025-11-16}}</ref> - VR-200-based platforms (e.g. NVL144 / Rubin + Vera racks) are targeted at multi-exaflop FP4/FP8 training/inference with extreme rack-level power (approaching 1 MW racks). NVIDIA Newsroom<ref>{{cite web|title=NVIDIA Newsroom|url=https://nvidianews.nvidia.com/news/nvidia-unveils-rubin-cpx-a-new-class-of-gpu-designed-for-massive-context-inference|publisher=NVIDIA Newsroom|access-date=2025-11-16}}</ref>
* Exact per-GPU TFLOPs for VR-200 aren’t public yet; safe assumption is significantly above Blackwell GB200 and well above H100.

So the “standard Llama-3 400B on Rubin” scenario is:

: 

==== ### ====
* Llama-3.1 405B: ~15T tokens. Meta AI<ref>{{cite web|title=Meta AI|url=https://ai.meta.com/blog/meta-llama-3-1/|publisher=Meta AI|access-date=2025-11-16}}</ref>
* Zebra-Llama / HybridLM: 7–11B tokens for 1–8B hybrids. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/html/2505.17272v1|publisher=arxiv.org|access-date=2025-11-16}}</ref>

Token ratio:

15×101210×109≈1500×\frac{15 \times 10^{12}}{10 \times 10^{9}} \approx 1500\times10×10915×1012≈1500×
So just on tokens, the full 400B pre-train is ~1.5–2.0k× heavier than a single 8B hybrid post-train.

===== Concrete known number: =====
* Llama-3.1 405B: 39.3M H100 GPU-hours. Hugging Face<ref>{{cite web|title=Hugging Face|url=https://huggingface.co/meta-llama/Llama-3.1-405B|publisher=Hugging Face|access-date=2025-11-16}}</ref>

For hybrids, we have actual MI300 data:
* 1B hybrid, 7B tokens: 136 MI300 GPU-hours. 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>

If you roughly scale that up to an 8B hybrid on similar hardware:
* 8× parameters → ~8× compute.
* 7B → 11B tokens → ~1.6×.

So ballpark:

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
Call it ~1–3k GPU-hours for an 8B hybrid.

GPU-hour ratio (H100 vs MI-class is apples/oranges, but order-of-magnitude still works):

39.3 M1.7 k≈23,000×\frac{39.3\text{ M}}{1.7\text{ k}} \approx 23{,}000\times1.7 k39.3 M≈23,000×
So one 8B Zebra-Llama-style hybrid is ~10⁴–10⁵× cheaper in GPU-hours than a 400B pre-train.

Even if Rubin VR-200 is, say, 3–4× faster than H100 for training, you’re still looking at several million VR-200 GPU-hours for the 400B run vs thousands of MI450X GPU-hours for a hybrid.

==== ### ====

From AMD’s OCP Helios disclosure: Tom's Hardware<ref>{{cite web|title=Tom's Hardware|url=https://www.tomshardware.com/tech-industry/amd-debuts-helios-rack-scale-ai-hardware-platform-at-ocp-global-summit-2025-promises-easier-serviceability-and-50-percent-more-memory-than-nvidias-vera-rubin|publisher=Tom's Hardware|access-date=2025-11-16}}</ref>
* Per rack: - 72 × MI450. - ~1.4 EF FP8. - 31 TB HBM4 (432 GB / GPU).
* Rack powers in the 600–900 kW class are implied by that performance + HBM4 + CPUs/NICs (exact TDPs are not yet public, but we know this is “near 1 MW rack” territory for both AMD & Nvidia).

Given how light the hybrid post-training is, a few Helios racks can:
* Train dozens to hundreds of 1–8B hybrids (per week/month).
* Run SFT + distillation on domain-specific data.
* Still have most of their duty cycle available for inference / serving.

===== From Nvidia’s Rubin coverage: =====
* Rubin racks (e.g. Vera Rubin NVL144 CPX) pack 144 main Rubin GPUs + 144 Rubin CPX GPUs + Vera CPUs, hitting 8 EF NVFP4 and 100 TB “fast memory” per rack. NVIDIA Newsroom<ref>{{cite web|title=NVIDIA Newsroom|url=https://nvidianews.nvidia.com/news/nvidia-unveils-rubin-cpx-a-new-class-of-gpu-designed-for-massive-context-inference|publisher=NVIDIA Newsroom|access-date=2025-11-16}}</ref>
* Designed explicitly for million-token contexts, huge PFLOPs, and are expected to land in the ~1 MW per rack regime.

A full 400B pre-train on Rubin VR-200 would:
* Run across thousands to tens of thousands of GPUs.
* Consume many GWh of energy over the run.
* Occupy a non-trivial fraction of a Rubin “AI factory” for weeks.

By contrast, the hybrid MI450X runs are rounding errors in power and time at that scale.

==== This is the core “is it worth it?” question. ====

===== Pros: =====
* Peak capability: 400B dense Transformer gives you: - Better long-tail reasoning. - Higher knowledge capacity. - Better few-shot / zero-shot across obscure edge cases.
* Single foundation: One massive model that you can: - Fine-tune. - Distill into smaller students (including hybrids like Zebra-Llama).
* Rubin stack: VR-200 + CPX + Kyber interconnect are optimized for: - NVFP4 / FP8 training. - Disaggregated prefill/decode. - Huge context inference at massive scale. NVIDIA Newsroom<ref>{{cite web|title=NVIDIA Newsroom|url=https://nvidianews.nvidia.com/news/nvidia-unveils-rubin-cpx-a-new-class-of-gpu-designed-for-massive-context-inference|publisher=NVIDIA Newsroom|access-date=2025-11-16}}</ref>

Cons:
* Capex + opex are astronomical.
* Ultra high power density / cooling requirements (1 MW-class racks).
* You only do this for a handful of foundation models globally.

===== Pros: =====
* Training cost: - 1–2 orders of magnitude fewer tokens. - ~10⁴× less GPU-compute vs 400B pre-train.
* Inference efficiency: - KV-cache shrunk by 10–30× → dramatically better tokens/W and memory/W for long context. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/html/2505.17272v1|publisher=arxiv.org|access-date=2025-11-16}}</ref> - Fits nicely with MI450X’s huge 432 GB HBM4 per GPU; you can hold many concurrent sequences or long contexts per device.
* Deployment flexibility: - Easy to spin up per-customer / per-workload models. - Post-training cycles measured in hours–days, not weeks. - You can saturate Helios racks with hundreds of such jobs over time.

Cons:
* You’re capped at 1–8B (today) – far below 400B.
* Quality is bounded by: - The teacher’s capabilities. - The hybridization & distillation quality.
* For frontier-level reasoning / generality, they complement but don’t entirely replace a 400B class foundation.

==== 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”.

==== 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.