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NVIDIA · Blackwell Ultra · Layer 2

NVIDIA GB300 NVL72

The same buyers as GB200, one generation on, where throughput per megawatt is the binding constraint.

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

Specifications

Memory
Higher HBM3E capacity than GB200; per-rack figure not consistently published
Bandwidth
Not published per rack
Compute
Not published in comparable FP4 terms
Power
135-200 kW per rack
Interconnect
NVLink 5
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

Pushes rack power to 135-200 kW. Very few facilities in the world can energise and cool that today, and the interconnection queue means new ones are a five-year decision.

The economics

NVIDIA claims up to 50x higher throughput per megawatt versus Hopper. Per-megawatt is the right frame in 2026 — see the Energy layer.

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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