Layer 2 · head to head
AMD Helios (Instinct MI455X) vs NVIDIA GB200 NVL72
432 GB per GPU of ROCm against the CUDA rack everyone already runs. The memory argument at rack scale.
| AMD Helios (Instinct MI455X) | NVIDIA GB200 NVL72 | |
|---|---|---|
| Can you get it | Rentable by the hour from multiple clouds; purchasable at scale. | Rentable by the hour from multiple clouds; purchasable at scale. |
| Memory | 31 TB of HBM4 per rack across 72 MI455X GPUs; 432 GB of HBM4 per GPU. | 13.5 TB HBM3E across the rack |
| Bandwidth | 260 TB/s scale-up per rack; 19.6 TB/s per GPU. | 576 TB/s aggregate |
| Compute | 2.9 exaFLOPS FP4 and 1.4 exaFLOPS FP8 per rack, on AMD's own figures. | 1.44 exaFLOPS FP4 inference |
| Power | Not published per rack. | ~120 kW nominal, 130-132 kW observed at full load |
| Interconnect | Scale-up fabric, Helios rack architecture | NVLink 5, 72 GPUs in one coherent domain |
| Software | ROCm | CUDA |
| Workload | both | both |
AMD Helios (Instinct MI455X)
For: Buyers with a working ROCm story who want more memory per GPU than NVIDIA sells, and a second source at rack scale.
The catch: ROCm is still the decision, not the silicon. The memory advantage per GPU is real and large — 432 GB against Blackwell-class parts — but it only pays if your stack runs on ROCm without a rewrite, and for most teams today it does not. Racks are quoted around $5-5.5M, which is one of very few public rack prices anywhere at this layer.
Economics: AMD's leverage here is memory capacity and being a second source, not price per FLOP. The commercial proof is commitment rather than benchmark: Oracle Cloud is the first hyperscaler offering a public MI450-series supercluster at 50,000 GPUs from Q3 2026, and OpenAI has committed to 6 GW of AMD Instinct with the first gigawatt in H2 2026.
NVIDIA GB200 NVL72
For: Frontier training and the largest inference deployments, where a single 72-GPU coherent memory domain is the point.
The catch: Liquid cooling is a mandatory architectural requirement, not a preference. At 120-132 kW a rack it renders most enterprise halls structurally and electrically unable to host it. This is a datacentre decision, not a hardware decision.
Economics: NVIDIA publishes two cents per million tokens and a 15x ROI claim for this configuration. Treat vendor TCO as a ceiling, not a forecast.
Before either — can you power it?
Not published per rack. against ~120 kW nominal, 130-132 kW observed at full load. In 2026 that comparison usually matters more than the FLOPS one: the US interconnection queue exceeds 2,600 GW with waits approaching five years, and roughly 80% of projects withdraw before energising.
Want this whole layer as one table?
The accelerators on these pages as a single CSV — memory, bandwidth, compute, power, interconnect, software stack, availability, who each part is for and the catch — ready to sort, filter or load into a notebook.
Get the silicon table — free for research and evaluation
Four short lines and the download starts at once (CC-BY-NC-4.0, built from these pages the moment you ask). What you type is used only to answer the request and is never sold. Prefer Hugging Face? Request it there.
Using it inside a product, a client report or anything you sell? That needs a commercial licence.