NVIDIA GB200 NVL72 vs. NVIDIA H100: When to choose which
Comprehensive guide to GPU cloud services featuring NVIDIA GB200 NVL72 and H100 comparison. Explore pricing, performance specs, memory capacity, and use cases for high-performance AI and machine learning workloads.
GPU cloud services deliver high-performance computing capabilities with specialized infrastructure for AI and machine learning workloads. Users gain access to GPU clusters connected through high-bandwidth networks, enabling distributed processing and faster model training. These services include pre-configured environments optimized for common AI frameworks, reducing setup time and complexity.
The infrastructure scales on demand, from single GPU instances to multi-GPU clusters, featuring low-latency networking and high-speed interconnects. Security measures, compliance certifications, and technical support come standard. Pricing models are usage-based, with costs varying by GPU type, usage duration, and resource allocation.
About the NVIDIA GB200 NVL72
The NVIDIA GB200 NVL72 is a room-sized computer designed for the biggest AI problems. It combines 36 CPUs and 72 of NVIDIA's newest GPUs into one massive liquid-cooled system with over 13 terabytes of memory. It delivers inference that's 30 times faster than previous systems, running enormous language models in real-time.
This system targets organizations running trillion-parameter models that serve millions of users simultaneously. Large tech companies, cloud providers, and research institutions with significant budgets and power infrastructure are the primary buyers. They use it for training next-generation AI models and serving them at massive scale, where fast response times create competitive advantage or enable scientific breakthroughs.
About the NVIDIA H100
The NVIDIA H100 represents the current pinnacle of AI computing hardware. It delivers around 2,000 TFLOPs of performance with 80GB of ultra-fast HBM3 memory. The system offers cutting-edge performance specifically optimized for transformer models, the architecture behind ChatGPT and similar AI systems. However, it has massive power consumption and may be excessive for smaller projects.
Organizations training the largest AI models and running complex high-performance computing workloads are the primary users. This includes major AI research labs, tech companies building foundation models, and scientists working on computationally intensive problems like climate modeling or drug discovery. The H100's extreme capabilities make it necessary only for those pushing the absolute boundaries of current AI capabilities.
Comparison
- NVIDIA GB200 NVL72 offers exceptional performance for massive-scale AI applications with up to 13.5 TB memory and 1,440 PFLOPs processing power, making it ideal for trillion-parameter model inference. However, it comes with extremely high costs ($60,000-$70,000) and significant power requirements, limiting accessibility to large enterprises and data centers.
- NVIDIA H100 provides strong performance at ~2,000 TFLOPs with 80 GB memory, offering good value for training large AI models and HPC workloads. While still expensive at ~$30,000, it's more accessible than the GB200 but may be excessive for smaller-scale applications.
Feature
NVIDIA GB200 NVL72
NVIDIA H100
Price Range
Very High
High
Memory Capacity
Massive ✅
Large ✅
Performance Power
Extreme ✅
High ✅
Energy Efficiency
Optimized ✅
Standard ❌
Accessibility
Limited ❌
Better ✅
Setup Complexity
Complex ❌
Moderate ✅
The NVIDIA GB200 NVL72 suits hyperscale cloud providers, large enterprises, and research institutions working with trillion-parameter models or requiring massive parallel processing capabilities. Its rack-scale design and exceptional performance justify the premium cost for organizations handling the most demanding AI workloads.
The NVIDIA H100 serves a broader market including mid-to-large enterprises, research labs, and specialized AI companies that need high-performance computing without the extreme scale of the GB200. Its lower entry cost and more manageable infrastructure requirements make it accessible to organizations focused on training large models or complex HPC tasks without requiring trillion-parameter capabilities.
Frequently asked questions
Q. What is the price difference between the NVIDIA GB200 NVL72 and H100?
A. The NVIDIA GB200 NVL72 costs approximately $60,000–$70,000, while the NVIDIA H100 costs around $30,000. The GB200 NVL72 is roughly 2-2.3 times more expensive than the H100.
Q. How much memory does each system offer?
A. The NVIDIA GB200 NVL72 offers up to 13.5 TB of HBM3e memory, while the NVIDIA H100 has 80 GB of HBM3 memory. The GB200 NVL72 provides significantly more memory capacity.
Q. What are the best use cases for each system?
A. The NVIDIA GB200 NVL72 is best for real-time trillion-parameter LLM inference, massive-scale AI training, and energy-efficient HPC. The NVIDIA H100 is optimized for training large AI models and HPC workloads.
Q. What are the main tradeoffs for these systems?
A. Both systems have high acquisition costs and power consumption. The GB200 NVL72 requires large-scale data center infrastructure due to its rack-scale liquid-cooled design. The H100 may be overkill for smaller tasks despite its cutting-edge performance.
Q. How much does it cost to rent these systems per hour?
A. The NVIDIA H100 costs approximately $3–$10 per hour to rent. For the NVIDIA GB200 NVL72, rental pricing is available on request only, indicating it's likely significantly more expensive due to its enterprise-scale nature.
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