Instant access to NVIDIA GB200 NVL72 GPUs—ideal for training trillion-parameter language models and complex data analytics—available globally with hourly pricing and rapid deployment through RunPod. Experience unparalleled computational power with the integration of 72 Blackwell B200 GPUs and 36 Grace CPUs, delivering exascale performance without the capital outlay. Rent on RunPod for flexible scaling and cutting-edge AI development without the need for infrastructure management.

Why Choose NVIDIA GB200 NVL72

The GB200 NVL72 represents a quantum leap for organizations tackling exascale workloads, large language models, and generative AI applications. It's designed to deliver unprecedented computational power and efficiency, making it ideal for demanding AI tasks.

Benefits

• Unmatched Computational Power
  With 72 Blackwell B200 GPUs and 36 Grace CPUs, the GB200 NVL72 delivers up to 1,440 PFLOPS of FP4 performance, enabling it to handle the most demanding AI and machine learning tasks with ease, outperforming other leading GPUs in GPU comparison.
• Massive Memory Capacity
  The system offers up to 13.5 TB of HBM3e memory and 576 TB/s of aggregate bandwidth, allowing it to work with trillion-parameter models without frequent data shuffling.
• Efficient Communication
  Fifth-generation NVLink provides 130 TB/s of bandwidth within the rack, eliminating communication bottlenecks and ensuring seamless GPU-to-GPU interaction for distributed training.
• Scalability and Flexibility
  Cloud rental options, including both NVIDIA and AMD GPU options, enable organizations to scale resources as needed, from a few GPUs to entire racks, without the capital expenditure or complex infrastructure management. Services like RunPod cloud services provide these flexible solutions.
• Seamless Integration with AI Frameworks
  Compatible with popular frameworks like PyTorch and TensorFlow, the system integrates easily into existing AI workflows, enhancing productivity and reducing deployment time.

Specifications

FeatureValueGPUs72 Blackwell B200CPUs36 Grace (Arm Neoverse V2)Total CPU CoresUp to 2,952CoolingLiquid-cooledFast MemoryUp to 13.5 TB HBM3eTotal NVLink Bandwidth130 TB/sPower Consumption per GPUUp to 1,200WAggregate Tensor Core PerformanceFP4: 1,440 PFLOPS, FP8/FP6: 720 PFLOPS, INT8: 720 POPS, FP16/BF16: 360 PFLOPS, TF32: 180 PFLOPS, FP64: 3,240 TFLOPS (GPU FLOPS metrics)Individual GPU Peak Tensor PerformanceFP4: 20 PFLOPS, FP8/FP6: 10 PFLOPS, INT8: 10 POPS, FP16/BF16: 5 PFLOPS, TF32: 2.5 PFLOPSTransformer EngineSecond-generation, supports FP8 and FP4 precisionGPU MemoryEach B200 GPU features up to 192 GB HBM3eSystem-wide Memory AggregateUp to 13.5 TB HBM3eTotal Memory Bandwidth576 TB/sIndividual B200 GPU Bandwidth8 TB/sNVLink Generation5th generationCPU ModelGrace CPU SuperchipIntegrationCPUs directly connected to GPUs for unified memory accessIntended UseOptimized for data movement, preprocessing, and scalable AI workloadsPower and EfficiencyLiquid-cooled, maximum energy efficiency

FAQ

What are the pricing models for renting GB200 NVL72 GPUs?

GPU pricing details for GB200 NVL72 GPUs vary across cloud providers, but expect per-GPU hourly rates between $4-$7 based on current market trends for premium AI accelerators. This represents significant value when compared to the estimated $3 million purchase price for a complete system. Most cloud providers offer both on-demand and commitment-based options:

• On-demand: Pay only for what you use, billed by the hour or minute
• Reserved capacity: Commit to longer terms (1-3 months) for 20-40% discounts
• Volume discounts: Available for companies renting multiple nodes

For perspective, even at $7 per GPU hour, you could run all 72 GPUs continuously for over 5 years before reaching the purchase price—without factoring in maintenance, power, or cooling costs.

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How does the setup process work on cloud platforms?

Setting up a GB200 NVL72 instance typically follows these steps:

1. Select your desired configuration (GPUs, memory, storage)
2. Choose from pre-configured container images optimized for common AI frameworks
3. Configure networking, storage volumes, and access controls
4. Launch your instance and connect via SSH or web terminal

Most cloud providers offer one-click deployment of popular AI development environments like JupyterLab, PyTorch, and TensorFlow. Your environment can be running within minutes, compared to the months it would take to procure and install physical hardware.

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How do rental costs compare to purchasing the hardware?

The rental vs. purchase calculation depends on your usage patterns:

• For intermittent or experimental workloads, renting offers clear advantages:
• No upfront capital expenditure
• No facility requirements (power, cooling, space)
• No maintenance or support contracts
• Pay only for actual usage

A research team running intensive training jobs 8 hours daily would spend approximately $12,000-$21,000 per month for all 72 GPUs—significant but far below the amortized cost of ownership, especially considering the NVIDIA GPU pricing. For continuous, production workloads running 24/7/365, purchase might eventually become economical after 2-3 years, but only for organizations with existing data center infrastructure and specialized cooling capabilities.

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What types of AI workloads are best suited for the GB200 NVL72?

As one of the best GPUs for AI, the GB200 NVL72 excels at the most demanding AI tasks:

• Training foundation models with trillions of parameters
• Fine-tuning large language models on specialized datasets
• Running inference for complex multimodal AI systems
• Processing massive scientific datasets and simulations

Its exceptional memory capacity (13.5 TB HBM3e) and interconnect bandwidth (130 TB/s) make it ideal for workloads that previously required multiple nodes or were simply impossible due to memory constraints. According to NVIDIA's technical documentation, the architecture delivers particular performance advantages for transformer models that power today's most advanced AI systems.

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How do cloud providers ensure data security when using rented GPUs?

Security measures for GB200 NVL72 instances typically include:

• Hardware isolation through specialized virtualization
• Encrypted data transfer using TLS/SSL protocols
• End-to-end encryption for data at rest
• Role-based access controls
• Secure boot and attestation

Most providers offer compliance measures with major standards like SOC 2, HIPAA, and GDPR for regulated workloads. For highly sensitive applications, ask about dedicated instances that guarantee physical isolation from other customers. Many platforms also provide seamless integration with private cloud environments through secure VPN connections and offer serverless GPU endpoints, allowing hybrid workflows that keep sensitive data on-premises while leveraging cloud GPU resources for computation.

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