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A 20,000-GPU AI data center needs much more than enough servers: it needs utility and backup power sized to the chosen GPU platform, a cooling plant that can remove the resulting heat, separate networks for GPU communication and operations, and storage matched to the workload. There is no reliable universal megawatt figure for that GPU count. The estimate changes with the hardware, rack design, ancillary IT loads, facility efficiency, redundancy, and operating reserve.
How much power would 20,000 GPUs require?
Start by defining what the power figure includes. Compute-rack TDP covers the reference compute racks in a cited design; total IT load also includes equipment such as networking and storage; facility input includes the electrical and cooling infrastructure needed to run that IT load. Those are different quantities. Redundancy and reserve capacity should be identified separately, not concealed in an unexplained multiplier.
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Two NVIDIA reference architectures illustrate how much the answer depends on the platform. They are examples, not interchangeable assumptions or a site-specific estimate:
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| Reference | Published figures | What they do—and do not—show |
|---|---|---|
| NVIDIA GB200 DGX SuperPOD (2025) | One scalable unit comprises eight DGX GB200 rack systems and has 1.2 MW TDP. The cited architecture can scale beyond 128 racks and 9,216 GPUs. | A GB200-specific reference unit, not a prediction that a 20,000-GPU site will use this exact system or unit mix. |
| NVIDIA GB300 SuperPOD (2026) | The cited design places four DGX B300 systems per rack. One scalable unit lists 576 GPUs across 18 compute racks, or 32 GPUs per rack; the reference rack is approximately 56 kW. | A GB300-specific reference. A straight-line extrapolation from these figures gives about 625 compute racks and 35 MW of compute-rack TDP for 20,000 GPUs. This is derived arithmetic, not an NVIDIA-published 20,000-GPU design or a facility power estimate. |
The GB300 extrapolation excludes network and storage racks, facility overhead, distribution losses, reserve capacity, and redundancy. NVIDIA also notes that rack layouts may need adjustment to local power and cooling capability. The GB200 and GB300 figures describe different configurations; combining them into a single assumed rack density would be misleading.
What a site estimate must add
A real capacity plan needs the selected server and GPU power profiles, the number and configuration of racks, network and storage loads, electrical distribution, backup systems, operating reserve, and cooling plant. Utility capacity and interconnection depend on the location and utility territory; the cited architecture references do not establish a connection size or service timeline for a hypothetical site.
How must the facility cool the GPUs?
Nearly all electricity used by IT equipment ultimately becomes heat that the facility must remove. At high compute-rack densities, direct liquid cooling is a central option: it transfers heat from components through cold plates and a technology cooling loop. That is only part of the cooling system. The facility must also move heat out of that loop and reject it outdoors, while cooling equipment that remains air-cooled.
NVIDIA’s GB200 reference describes a hybrid design combining direct liquid and air cooling. Its DSX facilities reference describes a broader plant involving coolant distribution units (CDUs), facility-water distribution, dry coolers for heat rejection, central utility buildings, and computer-room air handlers (CRAHs) for remaining air-cooled equipment. The reference parameters below belong to that design; they are not universal requirements:
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| DSX reference parameter | Published value or description | How to interpret it |
|---|---|---|
| Liquid-cooling design point | 45°C | A vendor reference design point, not a required operating temperature for every facility. |
| TCS design flow | At least 1.5 LPM/kW | A DSX reference value for the technology cooling system, not a universal specification. |
| CDU redundancy | N+1 | The redundancy described in the DSX reference; the appropriate resilience design depends on the project. |
| Cabinet TDP range cited | 198 kW to 330 kW | The range of cabinet TDP values cited in the DSX reference, not a general figure for all GPU racks. |
Cooling design must account for both the rack-side loop that captures component heat and the facility-side systems that carry and reject it. The choice of heat-rejection equipment depends on climate, water strategy, temperatures, plant design, and local constraints. The cited references do not support a universal claim about water use or energy savings for one approach.
What networking does a 20,000-GPU cluster need?
“The network” is not one connection. A large GPU installation has several network roles, each with different traffic and operational needs. NVIDIA’s reference architecture uses NVLink for the local GPU-to-GPU domain in a rack; its NCP design separates cluster, tenant-access, and management networks.
- In-rack scale-up: NVLink connects GPUs within the local rack domain in the cited NVIDIA architecture.
- Scale-out cluster fabric: This carries GPU communication between racks. NVIDIA’s NCP reference allows Ethernet or InfiniBand.
- Tenant access and front end: This connects users and the cluster to other data-center services. Storage is a significant consumer in the cited design.
- Secure management: A separate out-of-band network supports configuration and management.
No fabric is automatically best for every 20,000-GPU deployment. A proposal should be evaluated against its supported topology, bandwidth, latency, congestion behavior under the intended collective-communication workload, operations expertise, and integration with the selected GPU platform. NVIDIA’s GB200 reference combines InfiniBand and Ethernet, while the NCP reference describes separate network roles; neither establishes a universal choice for every project.
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What storage and data movement should the design include?
Storage requirements depend on whether the cluster will train models, serve inference, or run other workloads. A design may combine remote block storage, high-speed file systems, object storage, and local NVMe for temporary data such as logs or image caches. The required bandwidth per GPU varies with workload, model, and performance goals; the cited reference does not provide a single bandwidth figure that can be applied to every cluster.
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- Model PWS-1K11P-1R is a 1010W DC power supply module designed to deliver consistent regulated direct current output for industrial and data center electronic equipment, with a rated continuous power output of 1010 watts for stable operational performance.
- This redundant power unit supports compatible integration into GPU server chassis and data center infrastructure, providing reliable backup power distribution to prevent unexpected downtime during critical workload operations.
- Constructed with heat-resistant industrial-grade components, the module features a streamlined thermal management design to maintain safe operating temperatures even during extended high-load use in enclosed server racks.
- The unit is engineered to meet standard industrial DC power supply specifications, with precise voltage regulation to protect connected electronic hardware from fluctuations and extend overall equipment service life.
- Designed for use in industrial and scientific electronic setups, including rack-mounted server systems and data center power distribution arrays, this module supports seamless hot-swapping for simplified maintenance and upgrades.
How should the project be divided into repeatable units?
Repeatable building blocks can help with layout and phased construction, but a vendor’s “scalable unit” is an architecture-specific term, not a complete site plan. NVIDIA’s GB200 reference defines a unit as eight rack systems; the GB300 reference lists 18 compute racks per unit. Those definitions should not be treated as equivalent.
NVIDIA’s DSX facilities reference uses a different facility-level definition: a scalable unit consists of a compute hot-aisle containment area plus a support hot-aisle containment area. It describes 18 such units per data hall, or 24 in the MaxLPS design. These are DSX reference layouts, not a general prescription for the number of units a data hall must contain.
What availability and site constraints belong in the design?
NVIDIA recommends that a data center based on its GB200 reference generally meet Uptime Institute Tier 3 or an equivalent TIA-942-B Rated 3 or EN 50600 Availability Class 3 design, including concurrent maintainability and no single point of failure. That is vendor guidance for the cited architecture, not an independently established requirement for every facility.
Power, cooling, and networking plans also need to fit the actual site. Utility service and interconnection, available space, climate, water strategy, local constraints, and the expansion plan all affect what can be built. Without a location and utility territory, a specific grid connection or service schedule cannot be established.
How can you compare two data-center proposals?
Compare like with like: the same GPU generation, workload assumptions, and definition of power are essential before rack or facility figures can be meaningfully compared. Use these questions to evaluate a proposal:
Quick Recap
- Which GPU and server generation is specified, how many GPUs are in each node, and what workload power profile is assumed?
- What are the rack power density and rack count? How are power distribution, redundancy, and reserve capacity represented?
- What liquid-cooling temperatures and design flows are specified? Which equipment remains air-cooled, and what heat-rejection approach, CDU capacity, and CDU redundancy are planned?
- How are in-rack scale-up, cross-rack cluster traffic, tenant access, and management handled? What topology, port speeds, cabling, and operating model are proposed?
- Which storage types are included, and what workload-specific bandwidth and latency needs are they designed to meet?
- What availability target and maintainability approach are specified, and how do site, utility, climate, water, and phased expansion constraints affect the design?
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