NVIDIA GB200 NVL72 vs. NVIDIA H200: When to choose which

GPU cloud services typically offer high-performance computing capabilities with specialized infrastructure for AI and machine learning workloads. Users can expect access to clusters of GPUs connected through high-bandwidth networks, allowing for distributed processing and faster model training. These services generally include pre-configured environments optimized for common AI frameworks, reducing setup time and complexity.

The infrastructure usually scales based on demand, from single GPU instances to multi-GPU clusters, with features like low-latency networking and high-speed interconnects. Security measures, compliance certifications, and technical support are standard offerings. Pricing models tend to be usage-based, with costs varying depending on GPU type, usage duration, and resource allocation.

About the NVIDIA GB200 NVL72

The NVIDIA GB200 NVL72 is a rack-scale supercomputer that combines 36 CPUs and 72 GPUs into one liquid-cooled system. It handles massive AI workloads and runs the largest language models with trillions of parameters. The system delivers 30x faster performance than previous generations, making previously impossible AI models practical to deploy.

This system targets organizations building next-generation AI systems. Large tech companies training foundation models, research institutions working on cutting-edge AI, and cloud providers serving enterprise customers are the primary users. If you're training models that barely fit in memory or serving AI applications to millions of users, this system makes those challenges manageable.

The GB200 NVL72 is designed for scale where energy efficiency matters. Organizations running thousands of these systems benefit from its optimized power consumption and cooling design.

About the NVIDIA H200

The NVIDIA H200 delivers 141GB of HBM3e memory and substantially higher bandwidth than its predecessor. It offers nearly double the memory capacity and 1.4x the bandwidth of the H100. This makes it valuable for memory-intensive tasks that would otherwise hit RAM bottlenecks.

Researchers working on large language models and AI systems are the main users, along with organizations running high-performance computing workloads requiring massive amounts of fast memory. The extra memory capacity enables training enormous neural networks and running inference on models that won't fit on less capable hardware.

While availability remains limited, users find it particularly useful for pushing AI research boundaries and deploying cutting-edge models in production.

Comparison

• NVIDIA GB200 NVL72 offers exceptional performance with up to 1,440 PFLOPs and massive 13.5 TB memory capacity, making it ideal for trillion-parameter model inference and large-scale AI training. However, it comes with significantly higher costs ($60,000-$70,000), substantial power requirements, and complexity due to its rack-scale liquid-cooled design with 36 CPUs and 72 GPUs.
• NVIDIA H200 provides a more accessible entry point at $30,000-$40,000 with 141 GB HBM3e memory and strong performance for large AI model training and inference. The main drawbacks include limited availability and significantly lower memory capacity compared to rack-scale solutions, though it offers better cost efficiency for smaller-scale deployments.

The NVIDIA GB200 NVL72 suits large enterprises, cloud service providers, and research institutions handling trillion-parameter models. Organizations with substantial budgets, dedicated data center infrastructure, and technical expertise to manage complex liquid-cooled rack systems will benefit most.

The NVIDIA H200 works better for mid-sized companies, academic researchers, and development teams working on large AI models with constrained budgets and infrastructure. It offers a practical balance of performance and cost for organizations needing significant AI capabilities without rack-scale complexity and expense.

Frequently asked questions

What is the price difference between the NVIDIA H200 and GB200 NVL72?

The NVIDIA H200 costs approximately $30,000–$40,000, while the GB200 NVL72 costs around $60,000–$70,000. The GB200 NVL72 costs more due to its rack-scale design with 36 Grace CPUs and 72 Blackwell GPUs.

How much memory does each system offer?

The NVIDIA H200 provides 141 GB of HBM3e memory, while the GB200 NVL72 offers up to 13.5 TB of HBM3e memory. That's nearly 100 times more memory capacity.

What are the best use cases for each system?

The H200 works best for training and inference of large AI models and HPC workloads requiring high memory bandwidth. The GB200 NVL72 handles real-time trillion-parameter LLM inference, massive-scale AI training, and energy-efficient HPC applications.

What are the main tradeoffs for the GB200 NVL72?

The GB200 NVL72 offers 30x faster real-time LLM inference performance but requires high acquisition costs and significant power. It's suitable primarily for large-scale data centers due to its liquid-cooled rack-scale design.

How do the rental costs compare between these systems?

The H200 rents for approximately $3.83–$10 per hour, while GB200 NVL72 rental pricing is available on request only. This reflects its premium enterprise positioning and custom deployment requirements.

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