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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 2026-09-09. 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

Where you actually rent this

Choosing the part is the easy half. The same accelerator rents for wildly different money depending only on who you rent it from — the cheapest and dearest published on-demand price for an H100 sit 5.6× apart — and the provider, not the chip, is what decides whether you can leave later.

Layer 2.5 — Compute providers · The GPU cost calculator

Compared against

Related silicon

Sources

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