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NVIDIA · Blackwell · Layer 2

NVIDIA GB200 NVL72

Frontier training and the largest inference deployments, where a single 72-GPU coherent memory domain is the point.

rent Rentable by the hour from multiple clouds; purchasable at scale.

Specifications

Memory
13.5 TB HBM3E across the rack
Bandwidth
576 TB/s aggregate
Compute
1.44 exaFLOPS FP4 inference
Power
~120 kW nominal, 130-132 kW observed at full load
Interconnect
NVLink 5, 72 GPUs in one coherent domain
Software
CUDA
Form factor
rack
Workload
both

Verified 2026-09-06. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.

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.

The 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.

Cost per million tokens is the metric that decides most real purchases, and there is no neutral benchmark for it — every published figure comes from a company selling one side of the comparison. Read all of them, ours included, as claims rather than measurements.

The power question underneath this

A rack of frontier accelerators draws 120–200 kW against a 2026 average of about 27 kW, and the US grid interconnection queue exceeds 2,600 GW with waits approaching five years. Whether you can energise this part is now a harder question than whether you can buy it.

Layer 1 — Energy · The interconnection queue · Rack power density · Tokens per watt

Compared against

Related silicon

Sources

Macrostack does not sell silicon and takes no commission at this layer — there is no affiliate programme for compute, which is exactly why nobody publishes a neutral comparison of it.

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