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Inside the AI data centre · Compute hall

The Compute Rack

This section explores NVIDIA Vera Rubin NVL72, the newest AI rack platform NVIDIA provides: a whole rack of Rubin GPUs and Vera CPUs, liquid-cooled and joined so tightly that it works as one machine.

Every part, chip to rack →

Annotated illustration of an AI compute rack. From top to bottom: top-of-rack network switches, a scale-up switch tray, a bank of compute trays holding accelerators and host processors, BlueField DPUs, NVMe storage, and power distribution units at the base. Side panels show a cooling and power section view, a rear cable-management view, and a legend for electricity, cooling, scale-up and network flows.

Illustrative model — author-supplied, not official vendor material. The drawing is a generic GPU rack, not the Vera Rubin NVL72 layout. NVIDIA's published rack holds 18 compute trays (72 Rubin GPUs, 36 Vera CPUs) and 9 NVLink switch trays, and weighs ~4,000 lb (≈1,800 kg). Its in-rack switching is those NVLink switch trays; the scale-out leaf switches the drawing puts at the top typically sit in separate network racks. Open the ⓘ on the compute rack for the sourced specification.

Select a part to zoom in on it · 6 components · 10 diagram panels

Build sequence

How the rack is put together

Why is an AI rack delivered and accepted as one engineered system, not a cabinet to fill?

  1. In the rack

Sources · 0/8 verified8 unsourced
  • The significant change across these generations is not accelerator performance but the unit of deployment moving from chip to server to rack to pod, which changes procurement, logistics, commissioning and failure-domain size together.

    Industry reference patternReview required

    No source attached. Working explanation, awaiting technical review.

  • Integrating and testing racks at a staging facility before shipping reduces on-site commissioning time and the number of faults discovered in the data hall.

    Industry reference patternReview required

    No source attached. Working explanation, awaiting technical review.

  • Vera Rubin is a coordinated platform spanning CPU, GPU, scale-up interconnect, scale-out networking, infrastructure processing, rack design, cooling, power and system software — not a single GPU product.

    Vendor implementationReview required

    No source attached. Working explanation, awaiting technical review.

  • Rack-scale AI systems commonly distribute power through shelves feeding a common busbar rather than individual per-server power supplies, which changes the rack-level failure domain.

    Industry reference patternReview required

    No source attached. Working explanation, awaiting technical review.

  • NVLink provides the high-bandwidth, low-latency interconnect within the scale-up domain, and NVLink switches extend that domain beyond a single tray.

    Vendor implementationReview required

    No source attached. Working explanation, awaiting technical review.

  • Liquid-cooled racks require leak detection, isolation valves and defined maintenance procedures, because a coolant leak is both an availability and an electrical-safety event.

    Industry reference patternReview required

    No source attached. Working explanation, awaiting technical review.

  • In a rail-optimised design each accelerator connects to a specific fabric plane, so collectives among the same-numbered accelerators across nodes stay within one rail and do not contend with the others.

    Industry reference patternReview required

    No source attached. Working explanation, awaiting technical review.

  • Out-of-band management must be reachable independently of the data fabric, or it is unavailable during precisely the incidents it exists to support.

    Industry reference patternReview required

    No source attached. Working explanation, awaiting technical review.

How everything connects

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Physical
Projection
Compute rack
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A
Status
Conceptual educational model
Claims
14/152 verified

Note · This interactive model is a simplified educational representation. Exact component placement, capacity, power, cooling and network architecture vary by platform generation, deployment design and vendor configuration. Scale marker shows 2m — Reference value.