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

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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.