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NVIDIA Vera Rubin NVL72 is a rack-scale AI and high-performance-computing system built as one 72-GPU NVLink domain. It is not a conventional server with 72 interchangeable graphics cards. NVIDIA’s DGX configuration combines 18 liquid-cooled compute trays, 72 Rubin GPUs, 36 Vera CPUs, nine NVLink 6 switch trays, management networking, shared-storage connectivity, power shelves and rack-level cooling.
The design is intended to keep GPUs working together on very large training and inference jobs, while also supporting scientific simulation and data analysis. Understanding the rack’s topology matters because NVLink handles scale-up inside the rack; Ethernet or InfiniBand fabrics handle management, storage access and communication between racks.
What “NVL72” means
“NVL72” describes a 72-GPU NVLink domain. The number identifies the GPUs participating in a tightly connected scale-up system, not the number of cards that can simply be installed in one ordinary chassis. In NVIDIA’s documented DGX arrangement, those GPUs are distributed across 18 one-rack-unit (1RU) compute trays.
Each compute tray contains two Vera CPUs and four Rubin GPUs. Across the 18 trays, that totals 36 CPUs and 72 GPUs. Nine additional 1RU trays provide the NVLink switching needed to connect every GPU in the single rack-level L1 domain. Management switches, power shelves, a bus bar, liquid-cooling manifolds and cable infrastructure complete the rack.
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This architecture lets software treat the GPUs as a coordinated pool for parallel workloads. It also means deployment resembles installing a small data-center system rather than buying a standalone accelerator server: the rack needs high-capacity power, liquid-cooling plumbing, structured networking and operational monitoring.
Inside an NVL72 rack
Compute trays: 72 Rubin GPUs and 36 Vera CPUs
Rubin GPUs are the accelerators for AI and other highly parallel calculations. Vera CPUs provide host processing, orchestration and general-purpose work for the GPU subsystems. The 18-tray layout distributes the hardware physically while presenting it as one NVLink-connected GPU domain.
| Rack element | NVIDIA’s documented quantity | Role |
|---|---|---|
| Rubin GPUs | 72 | AI training, inference and parallel scientific workloads |
| Vera CPUs | 36 | Host and general-purpose processing; two per compute tray |
| Compute trays | 18, each 1RU | Houses two Vera CPUs and four Rubin GPUs |
| NVLink 6 switch trays | 9, each 1RU | Provides the rack’s GPU scale-up fabric |
| Top-of-rack Ethernet switches | 2 | Management connectivity and controller access |
| Power shelves | 4 per rack | Converts AC input to 54 V DC for the rack bus bar |
The table describes the physical configuration in NVIDIA’s DGX guide. Other system manufacturers may package Vera Rubin technology differently, so the exact chassis, cabling and service procedures should be verified for the product being purchased.
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NVLink 6 and NVSwitch
Every one of the nine NVLink switch trays contains four NVSwitch ASICs. NVIDIA specifies 72 NVLink 6 ports per ASIC. These switches form the high-bandwidth scale-up fabric that connects the GPUs throughout the rack, rather than limiting communication to GPUs sharing one compute tray.
NVLink’s purpose is different from ordinary Ethernet or InfiniBand networking. It allows GPU-to-GPU communication and unified-memory access within the NVL72 domain. The external networks remain necessary for management, storage and scale-out to additional racks.
Operating the fabric requires more than plugging in cables. NVIDIA identifies GPU drivers, CUDA and NCCL software, and a fabric-management service—referred to as Fabric Manager or NVLSM in its documentation—as parts of setup, monitoring and troubleshooting. Version compatibility among those components is an operational requirement.
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ConnectX-9 SuperNICs and BlueField-4 DPUs
NVIDIA’s Vera Rubin platform descriptions also name ConnectX-9 SuperNICs and BlueField-4 DPUs. These devices support data movement, networking and infrastructure processing. They should not be confused with NVSwitch: SuperNICs and DPUs serve the network and data-processing planes, while NVLink and NVSwitch provide the tightly coupled GPU fabric inside the rack.
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The DGX guide separates management, compute and storage networking. Top-of-rack Ethernet switches connect baseboard-management controllers and other management endpoints. A compute network carries application traffic, and a storage network connects the rack to shared data infrastructure.
For multi-rack deployments, NVIDIA describes both InfiniBand and Ethernet as scale-out choices. The appropriate fabric depends on the cluster design, software stack, distance between racks and required communication pattern. A rack can therefore have a very fast internal NVLink domain while still relying on a separate Ethernet or InfiniBand fabric outside that domain.
Local and shared storage
Each compute tray uses local E1.S NVMe devices for boot and caching. The main dataset source is shared parallel storage reached over converged Ethernet by default; InfiniBand is also possible. A local cache can reduce repeated reads from shared storage when it is configured and sized for the workload.
Local NVMe is not a replacement for a shared data system in a large training cluster. Dataset capacity, checkpoint retention, metadata performance and the number of simultaneous jobs still have to be planned at the storage layer.
How data moves through the system
- Job control enters through management services. Administrators use the management network to provision nodes, monitor controllers and handle firmware or hardware events.
- Datasets arrive from shared storage. The storage network reads training data and checkpoints; local E1.S NVMe can cache frequently reused content.
- CPUs prepare and schedule work. Vera CPUs handle host-side processing, input pipelines and coordination for the accelerator workload.
- GPUs exchange tensors over NVLink. Collective operations such as those used by distributed training can remain inside the 72-GPU domain, using NVSwitch to reach GPUs in other compute trays.
- External fabrics handle scale-out. When a job spans multiple NVL72 racks, Ethernet or InfiniBand carries inter-rack traffic and connects the cluster to storage and services.
This separation is central to the design. An NVL72 rack is not a self-contained replacement for a data center’s storage, scheduling, authentication and external network systems.
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Cooling and power requirements
Liquid cooling
The compute trays are liquid cooled. Rack manifolds circulate coolant through cold plates attached to CPUs, GPUs and other supported networking or storage components. The rear of the rack provides access to cooling manifolds, cable cartridges and the power bus bar.
Liquid cooling changes site requirements compared with air-cooled servers. A facility must provide the approved coolant distribution, leak detection and service clearances, and technicians must follow the manufacturer’s procedures when disconnecting a tray. The rack should be treated as an integrated cooling system, not as a collection of air-cooled add-in cards.
Power shelves and the bus bar
NVIDIA’s guide specifies four power shelves per rack, each rated at 110 kW. They convert incoming AC to 54 V DC, which is distributed through the rack bus bar. The four nameplate ratings represent up to 440 kW of shelf capacity; actual consumption varies with workload, power limits, utilization, cooling overhead and the rest of the facility.
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What NVIDIA says Vera Rubin NVL72 is used for
Large AI training
NVIDIA positions NVL72 for training large models, including mixture-of-experts (MoE) systems. MoE training can involve many expert networks and substantial all-to-all communication, making fast GPU-to-GPU links valuable. The rack’s single NVLink domain is intended to reduce the need to treat each compute tray as an isolated island.
Reasoning-heavy and agentic inference
The platform is also aimed at interactive inference workloads in which models generate longer responses, use tools or agents, and maintain large context windows. Those jobs can require sustained throughput and fast movement of model state, not just a short burst from one accelerator.
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Scientific and high-performance computing
NVIDIA describes Vera Rubin for climate modeling, computational-fluid dynamics, quantum chemistry, energy exploration, simulation and data-intensive research. The stated approach combines high-precision simulation, AI models and analysis of large scientific datasets.
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Multi-rack AI factories and HPC clusters
One rack is the scale-up building block. Larger installations connect multiple racks with InfiniBand or Ethernet, add shared storage and run a cluster scheduler. In that arrangement, NVLink handles the most tightly coupled communication within each rack while the external fabric carries traffic between racks.
Published specifications and how to read them
The following figures are specifications published by NVIDIA for the Vera Rubin NVL72 product. They use different numerical-precision formats and workload categories, so they are not interchangeable measures of one universal speed.
| Metric | NVIDIA-published figure | Interpretation |
|---|---|---|
| GPUs | 72 Rubin GPUs | Accelerator count in the NVL72 rack |
| CPUs | 36 Vera CPUs | Two CPUs in each of 18 compute trays |
| NVFP4 inference | 3,600 PFLOPS | Published inference throughput at NVFP4 precision |
| NVFP4 training | 2,520 PFLOPS | Published training throughput at NVFP4 precision |
| FP8/FP6 training | 1,260 PFLOPS | Published training figure for the listed formats |
| FP16/BF16 | 288 PFLOPS | Published throughput at these precisions |
| TF32 | 144 PFLOPS | Published throughput at TF32 precision |
PFLOPS figures are peak or specified product metrics, not a guarantee for every model. Real results depend on model architecture, sequence length, batch size, sparsity, software kernels, communication, data loading and the precision used by the application.
How to interpret NVIDIA’s performance comparisons
NVIDIA’s product material claims that Vera Rubin NVL72 can train a specified MoE model with one-fourth as many GPUs as a GB200 NVL72 system. It also claims up to 10 times more tokens per megawatt and one-tenth the cost per million tokens for specified comparisons.
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These are vendor claims tied to named models, workloads and assumptions. They are not independent third-party test results and should not be generalized to every model, data center or electricity price. “Tokens per megawatt” depends on the serving configuration, utilization, precision, cooling and power-measurement boundary. “Cost per million tokens” additionally depends on hardware amortization, software, staffing and the time period used for the calculation.
For a procurement comparison, request the complete test definition: model and parameterization, context length, batch or concurrency, precision, target quality, software versions, power boundary, rack count and whether storage and networking are included. Comparing a TF32 number from one system with an NVFP4 inference number from another produces a misleading result.
Deployment checklist
- Facility power: Confirm AC service and distribution for four 110 kW-rated shelves, including redundancy and safety margins.
- Liquid-cooling plant: Provide compatible coolant distribution, manifolds, leak monitoring and maintenance access.
- Rack space and weight: Reserve the required rack footprint, service clearances and floor loading for a fully populated system.
- Network design: Plan separate management, compute and storage paths, then choose Ethernet or InfiniBand for scale-out.
- Storage throughput: Size shared parallel storage and caching for dataset reads, checkpoints and concurrent jobs.
- Software integration: Validate GPU drivers, CUDA, NCCL and Fabric Manager or NVLSM versions as one tested stack.
- Operations: Prepare monitoring, firmware-management, scheduler, authentication and incident-response procedures before production use.
Common misconceptions and failure modes
“It is a 72-GPU server.”
It is more accurate to call NVL72 a rack-scale system or 72-GPU NVLink domain. The GPUs live in 18 compute trays and depend on nine switch trays, rack power and liquid cooling.
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“NVLink replaces the data-center network.”
NVLink connects GPUs inside the rack. Management traffic, shared storage and communication with other racks use Ethernet or InfiniBand networks.
“The PFLOPS number predicts my application.”
The published numbers are precision- and workload-specific. A model that uses a different precision, memory pattern or communication pattern may achieve substantially different throughput.
“Local NVMe is the cluster’s main storage.”
Local E1.S devices provide boot storage and caching. NVIDIA’s design uses shared parallel storage as the primary dataset source for large jobs.
“A 110 kW shelf means the rack always consumes 110 kW.”
110 kW is the rating of each power shelf. Workload utilization, power controls and facility overhead determine actual draw.
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NVL72 makes sense when a team needs a tightly coupled GPU domain for very large AI training, high-concurrency or long-context inference, or GPU-accelerated scientific workloads, and has the facility to support liquid cooling and substantial power distribution. It is less appropriate for a small development group, occasional single-GPU experiments or a site that cannot provide rack-scale electrical and cooling infrastructure.
Evaluate it as a complete platform: GPU scale-up, CPU capacity, external networking, storage, software compatibility, power, cooling and operations all affect the outcome. The rack’s value comes from the interaction of those pieces rather than from the accelerator count alone.
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