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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Vera Rubin NVL72 is NVIDIA’s newer 72-GPU rack platform, with higher published peak compute and vendor-claimed inference-efficiency gains over GB200 NVL72. Those figures are workload-specific projections, not guarantees of application throughput or a rack’s electrical draw. GB200 documentation provides a power figure for a particular DGX GB rack configuration; NVIDIA’s reviewed Vera Rubin material does not provide a directly comparable finalized rack input-power specification. For procurement, compare the exact system configurations against your workload and facility limits—not tokens per megawatt against rack kilowatts.
How the two racks differ
Both systems center on a 72-GPU rack-scale NVLink domain, but they combine different GPU, CPU, and interconnect generations. NVIDIA describes Vera Rubin NVL72 as a third-generation MGX NVL72 rack design; GB200 NVL72 pairs Blackwell GPUs with Grace CPUs in a liquid-cooled rack. The table summarizes NVIDIA’s published platform specifications, which should not be read as application benchmarks.
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| Specification | Vera Rubin NVL72 | GB200 NVL72 |
|---|---|---|
| GPUs and CPUs | 72 Rubin GPUs and 36 Vera CPUs. NVIDIA Vera Rubin NVL72 | 72 Blackwell GPUs and 36 Grace CPUs. NVIDIA GB200 NVL72 |
| NVLink | Sixth-generation NVLink switching. The cited rack specification page does not state an aggregate rack NVLink bandwidth figure. NVIDIA Vera Rubin NVL72 | Fifth-generation NVLink; NVIDIA states 130 TB/s of rack NVLink communications. NVIDIA GB200 NVL72 |
| Published NVFP4 peak compute | 3,600 PFLOPS inference and 2,520 PFLOPS training. NVIDIA labels the DGX Vera Rubin specifications preliminary and subject to change. NVIDIA Vera Rubin NVL72 NVIDIA DGX Vera Rubin NVL72 | 1,440 PFLOPS inference and 720 PFLOPS training. NVIDIA GB200 NVL72 |
| Memory figures published | NVIDIA’s Vera Rubin rack page lists 20.7 TB HBM4 and 1,400 TB/s GPU memory bandwidth. Its separate DGX page gives a preliminary “up to 1,580 TB/s” figure; these are page-specific published values, not one settled figure. NVIDIA Vera Rubin NVL72 NVIDIA DGX Vera Rubin NVL72 | The cited GB200 product page does not state a directly comparable aggregate GPU-memory-bandwidth value. |
| Scale-out networking named | ConnectX-9 SuperNICs, BlueField-4 DPUs, and Quantum-X800 InfiniBand and Spectrum-X Ethernet options. NVIDIA Vera Rubin NVL72 | The cited GB200 product page describes the rack’s NVLink domain; confirm the external network configuration for the exact system being quoted. NVIDIA GB200 NVL72 |
Peak PFLOPS figures describe vendor-listed compute capability at the stated precision. They do not tell a buyer how many tokens per second a particular model will serve: that also depends on model architecture, memory behavior, parallelism, software, batch size, latency target, and achieved utilization.
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What NVIDIA’s performance advantage claims mean
Inference efficiency and token cost
NVIDIA claims Vera Rubin NVL72 can deliver up to 10 times more tokens per megawatt than GB200 NVL72 for highly interactive deep-reasoning agentic AI. The product page’s stated comparison uses Kimi-K2 Thinking with 32K input and 8K output sequence lengths. NVIDIA also claims one-tenth the cost per million tokens for Kimi-K2-Thinking under the same 32K-input/8K-output scenario. These are NVIDIA’s workload-specific claims, and the company marks its LLM inference performance as subject to change. NVIDIA Vera Rubin NVL72
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
NVIDIA’s FY2026 sustainability report describes the per-megawatt comparisons as DLSim analytical projections using common modeling assumptions, which may differ from measured silicon results and other deployments. It discusses a Kimi-K2 Thinking comparison at NVFP4 with 32K input/8K output, and a separate comparison using a 2-trillion-parameter GPT MoE model with a 400K context. Those scenarios are not interchangeable: performance depends on the model and serving conditions being evaluated. NVIDIA Sustainability Report Fiscal Year 2026
Training claims
NVIDIA says Vera Rubin can train mixture-of-experts models with one-fourth as many GPUs as GB200 NVL72 in a projected scenario: a 10-trillion-parameter MoE model trained on 100 trillion tokens within one month. The company marks that projection as subject to change; it is not a general claim that every training run will need 75% fewer GPUs or finish in the same time. NVIDIA Vera Rubin NVL72
How to interpret these numbers
- Keep modeled and measured results separate. NVIDIA’s efficiency comparisons are analytical projections, not a universal measured-silicon result. Ask vendors for benchmark methodology and results on your target models and software stack.
- Match the service conditions. Input and output context lengths, concurrency, latency objectives, precision, and utilization all affect whether a published inference comparison is relevant to your deployment.
- Check what “cost” includes. A cost-per-million-tokens estimate is meaningful for procurement only when the hardware, utilization, software, power, and operating-cost assumptions match the buyer’s accounting boundary.
Power and cooling: what can and cannot be compared
NVIDIA’s tokens-per-megawatt figure is an efficiency ratio. It is not a Vera Rubin rack’s input-power rating, and it cannot be directly compared with a rack-kilowatt number to infer facility draw. The reviewed official Vera Rubin sources do not establish a finalized rack input-power and facility-interface specification directly comparable with GB200. NVIDIA Vera Rubin NVL72 NVIDIA DGX Vera Rubin NVL72
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NVIDIA’s DGX GB Rack Scale Systems User Guide gives approximately 120 kW rack power consumption for the documented DGX GB rack system. The guide describes power shelves receiving AC power from a remote panel and distributing DC through a bus bar. It also describes liquid cooling to compute trays through manifolds and cold plates, while networking and storage devices are air-cooled. This is deployment detail for that documented DGX implementation, not a guaranteed rating for every OEM’s GB200 NVL72 configuration. NVIDIA DGX GB Rack Scale Systems User Guide: Hardware
What to request for Vera Rubin
NVIDIA describes Vera Rubin as a modular MGX rack design and presents DGX Vera Rubin with Mission Control capabilities for configuration, facility integration, cluster and workload management, and cooling and power events. Those capabilities do not replace final engineering data for the system a buyer will receive. NVIDIA Vera Rubin NVL72 NVIDIA DGX Vera Rubin NVL72
- Maximum and typical rack input power, including power distribution and redundancy assumptions.
- Coolant supply and return conditions, flow requirements, heat rejection, and any air-cooling needs.
- Network uplinks, cable routing, rack dimensions, floor loading, and service clearances.
- Operating limits and service procedures for the exact OEM configuration.
How to choose for an actual workload and facility
Use a like-for-like evaluation rather than choosing from a peak-FLOPS or tokens-per-megawatt headline alone. Ask the system supplier to document the configuration and assumptions behind each answer.
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- Blackwell Architecture
- 24GB GDDR7 with PCIe 5.0 & Ray Tracing
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- Define the workload. Specify training or inference, model architecture and size, precision, context length, concurrency, latency target, and expected traffic pattern.
- Set a measurable service target. Compare sustained tokens per second, latency, and utilization on the serving or training stack you plan to run. Keep vendor projections, preview specifications, and measured results distinct.
- Check memory and communication fit. Map HBM capacity and bandwidth, CPU memory, NVLink generation and bandwidth, and scale-out networking to the model’s parallelism strategy and data movement needs.
- Validate facility capacity. Confirm rack input power, redundancy, cooling supply and return, heat rejection, network connections, floor loading, and service access against the exact quoted system.
- Verify delivery and operating support. Obtain supplier confirmation of production availability, delivery timing, software qualification, management tooling, and service coverage. NVIDIA’s platform descriptions do not substitute for a binding delivery commitment or configuration-specific support terms.
Deployment readiness is more than rack specifications
At this scale, installation and operation are part of the platform decision. NVIDIA’s Mission Control customer enablement material recommends deployment planning and describes software and firmware package context for GB200. For either platform, buyers should establish who owns configuration, cluster management, workload operations, and the response to power or cooling events before accepting a delivery schedule. NVIDIA Mission Control Customer Enablement Resources
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsIn practice, the decisive comparison is the one suppliers can support with configuration-specific evidence: benchmark conditions that represent the intended workload, a final facilities package, and a credible plan for delivery and ongoing service. Until comparable final power and interface data are available for the selected systems, treat Vera Rubin’s efficiency claims as performance projections—not as proof that its rack will draw less power than a GB200 rack.
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