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

==== 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.