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NVIDIA positions Vera Rubin NVL72 as a major step up in inference efficiency and peak compute over GB200 NVL72, while both are 72-GPU rack-scale NVLink systems. But NVIDIA’s headline performance comparisons are workload-specific modeled claims, not guarantees of application throughput, and the official sources reviewed do not establish directly comparable finalized rack input-power figures. Buyers should compare the exact workload and system configuration—and obtain facility specifications from the selected supplier—before treating either rack as a fit.
How the two racks differ
The systems share a rack-scale approach, but use different GPU and CPU generations, NVLink generations, and supporting network components. The specifications below are NVIDIA’s published system-level figures; they are not a promise of performance for a particular application.
| System | Compute and rack architecture | Published peak compute | Memory and interconnect | Rack power evidence |
|---|---|---|---|---|
| Vera Rubin NVL72 | 72 Rubin GPUs and 36 Vera CPUs; sixth-generation NVLink switching; ConnectX-9 SuperNICs and BlueField-4 DPUs. NVIDIA describes the rack as using its third-generation MGX NVL72 design. NVIDIA Vera Rubin NVL72 | 3,600 PFLOPS NVFP4 inference and 2,520 PFLOPS NVFP4 training. These are vendor specifications, not application-throughput guarantees. NVIDIA Vera Rubin NVL72 | 20.7 TB HBM4 and 1,400 TB/s GPU memory bandwidth in the Vera Rubin system-page rack specification. A separate preliminary DGX page gives “up to 1,580 TB/s,” so the two pages should not be treated as one settled bandwidth figure. NVIDIA Vera Rubin NVL72; NVIDIA DGX Vera Rubin NVL72 | A finalized, directly comparable rack input-power figure is not established in the official Vera Rubin sources reviewed. The DGX page marks its specifications preliminary and subject to change. NVIDIA DGX Vera Rubin NVL72 |
| GB200 NVL72 | 72 Blackwell GPUs and 36 Grace CPUs in a liquid-cooled rack-scale system, with a 72-GPU NVLink domain and fifth-generation NVLink. NVIDIA GB200 NVL72 | 1,440 PFLOPS NVFP4 inference and 720 PFLOPS NVFP4 training. These are vendor specifications, not application-throughput guarantees. NVIDIA GB200 NVL72 | NVIDIA reports 130 TB/s of rack NVLink communications. The cited GB200 product page does not state a directly comparable HBM capacity or GPU-memory-bandwidth figure. NVIDIA GB200 NVL72 | NVIDIA documents approximately 120 kW for its DGX GB rack system; this is scoped to that documented configuration, not every GB200 NVL72 OEM rack. NVIDIA DGX GB Rack Scale Systems User Guide: Hardware |
Rubin’s listed compute figures are preliminary and subject to change on NVIDIA’s Vera Rubin and DGX pages. Peak PFLOPS help describe vendor-rated capability at a stated precision; they do not tell a buyer how much usable throughput a specific model, software stack, or facility will deliver.
What NVIDIA’s performance claims do—and do not—show
NVIDIA says Vera Rubin NVL72 can deliver up to 10 times more tokens per megawatt than GB200 NVL72. The product page ties that comparison to Kimi-K2 Thinking with 32K input and 8K output sequence lengths, and labels LLM inference performance subject to change. NVIDIA’s FY2026 sustainability report describes its performance-per-megawatt comparisons as DLSim analytical projections using common modeling assumptions; projected results can differ from measured silicon and other deployments. These are vendor-modeled scenario results, not an independently verified outcome for every serving workload. NVIDIA Vera Rubin NVL72; NVIDIA Sustainability Report Fiscal Year 2026
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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
The same product page claims one-tenth the cost per million tokens for Kimi-K2-Thinking under the stated 32K input and 8K output scenario. That is a vendor comparison for the specified workload, not a general inference-cost forecast: realized cost also depends on utilization, software, service-level targets, power and cooling overhead, and the buyer’s own infrastructure and operating costs. NVIDIA Vera Rubin NVL72
For training, NVIDIA projects that Vera Rubin can train MoE models with one-fourth the GPUs compared with GB200 NVL72 in a scenario involving a 10-trillion-parameter MoE model trained on 100 trillion tokens within one month. NVIDIA marks the projection subject to change. The sustainability report also describes a separate modeled comparison using a 2-trillion-parameter GPT MoE model and a 400K context, reinforcing that the outcome depends on the scenario rather than establishing a universal training ratio. NVIDIA Vera Rubin NVL72; NVIDIA Sustainability Report Fiscal Year 2026
No independently published third-party benchmark specific to this Vera Rubin-versus-GB200 comparison is established by the cited sources. Treat the published ratios as useful vendor claims with disclosed workload and modeling context, then require measured or clearly modeled like-for-like results for the intended deployment.
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Power, cooling, and facility readiness
Tokens per megawatt is an efficiency ratio; it is not a rack’s electrical draw. The available official sources do not establish finalized, directly comparable rack input-power and facility-interface specifications for Vera Rubin and GB200. In particular, comparing the DGX GB rack’s documented power figure with Rubin’s tokens-per-megawatt claim would mix an absolute system-power value with an efficiency metric.
For the DGX GB rack system specifically, NVIDIA describes power shelves that receive AC power from a remote panel and distribute DC through a bus bar. Compute trays are liquid-cooled through manifolds and cold plates, while networking and storage devices are air-cooled. This is useful design context for that DGX implementation, but should not be generalized to all GB200 NVL72 configurations. NVIDIA DGX GB Rack Scale Systems User Guide: Hardware
NVIDIA describes Vera Rubin as a modular MGX rack design intended for enterprise deployment. The DGX Vera Rubin page also describes Mission Control for configuration, facility integration, cluster and workload management, and cooling and power events. Those capabilities do not replace engineering data for the actual system being procured. NVIDIA Vera Rubin NVL72; NVIDIA DGX Vera Rubin NVL72
Rank #3
- Professional GPU with Blackwell Architecture in Compact Small Form Factor (SFF)
- Blackwell Architecture
- 24GB GDDR7 with PCIe 5.0 & Ray Tracing
- AI Workstation
What to obtain before reserving capacity
- Final rack input power, including the supplier’s stated operating conditions and power redundancy configuration.
- Coolant supply and return requirements, permitted inlet temperatures, flow requirements, heat-rejection assumptions, and any facility-side cooling equipment needs.
- Rack weight, floor-loading requirements, service clearances, network uplink details, and installation and maintenance procedures.
- Confirmation that all figures apply to the exact OEM or DGX configuration under consideration, rather than to a related reference design.
How to make a like-for-like buying comparison
Build the comparison around the intended service and the complete system, not the GPU headline alone. Ask each supplier to provide the same information for the same model, workload, software stack, facility assumptions, and operating target.
- Define the workload and service target. Specify whether the rack will train models, serve batch inference, or power interactive reasoning. For inference, record model architecture, precision, context length, concurrency, latency target, and expected request mix; for training, include the model, training data volume, parallelism strategy, and completion target. NVIDIA’s published comparisons are tied to defined scenarios, not workload-agnostic results. NVIDIA Vera Rubin NVL72; NVIDIA Sustainability Report Fiscal Year 2026
- Request comparable throughput and cost evidence. Compare tokens per second and tokens per joule or megawatt at the target workload and utilization. Ask whether each result is measured or modeled, what serving stack and precision were used, and whether cost per million tokens includes the relevant power, cooling, hardware, and operating costs. Keep preview specifications and projections separate from measured system results.
- Match memory and communication to the model’s execution plan. Evaluate HBM capacity and bandwidth, CPU memory, NVLink generation and bandwidth, and scale-out networking against the model’s parallelism and communication needs. A higher headline compute rating is not decisive if memory capacity, communication, or the chosen parallelism strategy limits useful throughput.
- Validate the facility envelope for the purchased configuration. Confirm electrical supply and redundancy, liquid-cooling conditions, heat rejection, network connections, and installation and service requirements with the system vendor and facility engineering team. Do not infer Rubin rack power from its efficiency claim or transfer DGX specifications to another OEM.
- Verify delivery and operational support. Confirm production availability, allocation, delivery timing, software qualification, management tooling, and service coverage in the supplier’s commitment for the specific system. NVIDIA describes Rubin production ramp and DGX support, but those statements alone do not establish a buyer’s delivery date or support terms. NVIDIA Vera Rubin NVL72; NVIDIA DGX Vera Rubin NVL72
Which system is the better fit?
Vera Rubin is the more compelling candidate when its newer architecture and NVIDIA’s modeled efficiency and compute gains match the workload—and when the supplier can substantiate performance, facility requirements, availability, and support for the configuration on offer. GB200 remains a concrete option where its system-specific documentation and a buyer’s existing power, cooling, software, or procurement plans align with the intended deployment. Neither choice should be made from peak PFLOPS or tokens-per-megawatt alone; the deciding evidence is a like-for-like workload result paired with validated facility and delivery specifications.
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