NVIDIA B200 vs. NVIDIA H200: When to choose which

GPU cloud services deliver high-performance computing with specialized infrastructure for AI and machine learning workloads. Users access GPU clusters connected through high-bandwidth networks for 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. Features include low-latency networking and high-speed interconnects. Security measures, compliance certifications, and technical support are standard. Pricing models are usage-based, with costs varying by GPU type, usage duration, and resource allocation.

About the NVIDIA B200

The NVIDIA B200 delivers cutting-edge AI acceleration with 192GB of HBM3e memory. Users report dramatic performance gains: up to 15 times faster inference and 3 times faster training compared to the H100. The advanced memory system processes data more efficiently, though it requires robust cooling due to high power consumption.

This chip serves demanding users: researchers training large AI models, companies running massive inference workloads, and high-performance computing teams. It's built for organizations where compute time directly impacts business outcomes. The difference between 15 minutes and 4 hours matters enough to justify premium hardware costs.

The B200 is your choice when the H100 isn't fast enough and maximum performance is essential.

About the NVIDIA H200

The NVIDIA H200 provides 141GB of high-bandwidth memory for demanding AI workloads. It delivers nearly double the memory of the H100 and moves data 40% faster. This makes it valuable for training or running large language models and AI systems requiring massive amounts of accessible data.

Researchers working on cutting-edge AI models and organizations running high-performance computing tasks benefit most from the H200. If you're training next-generation language models, running complex simulations, or doing inference on memory-intensive models, this hardware delivers the capacity you need.

The main challenge is availability. Limited supply means many organizations that would benefit are still waiting for access.

Comparison

The NVIDIA B200 offers superior performance with up to 15x inference and 3x training improvements over the H100, plus 192GB HBM3e memory. However, it requires custom pricing discussions and robust cooling solutions due to higher power consumption.

The NVIDIA H200 provides substantial memory improvements with 141GB HBM3e and 1.4x bandwidth increase over H100, with transparent pricing around $30,000-$40,000. The main limitations include lower memory capacity than B200 and potential availability constraints.

The NVIDIA H200 suits organizations with established budgets who need immediate deployment of memory-intensive AI workloads. Its balanced performance improvements and transparent pricing make it accessible for enterprises transitioning from H100 systems.

The NVIDIA B200 targets cutting-edge research institutions and large-scale AI companies that prioritize maximum performance over cost considerations. Organizations with infrastructure to handle higher power requirements and willingness to discuss custom pricing will benefit most from its superior computational capabilities.

FAQ

Q. What is the pricing difference between NVIDIA B200 and H200?

A. The NVIDIA H200 has a retail price of approximately $30,000-$40,000, while the B200's pricing is available on request. For rental costs, the B200 starts at $2.40/hour while the H200 ranges from $3.83-$10/hour.

Q. How much memory do these GPUs have?

A. The NVIDIA B200 comes with 192 GB of HBM3e memory, while the H200 has 141 GB of HBM3e memory, giving the B200 significantly more memory capacity.

Q. What are the main use cases for these GPUs?

A. Both GPUs are designed for high-performance AI training and inference, as well as HPC (High-Performance Computing) workloads. The H200 is specifically optimized for large AI models requiring high memory bandwidth, while the B200 handles general high-performance AI and HPC tasks.

Q. What performance advantages does the B200 offer over the H100?

A. The NVIDIA B200 delivers up to 15x better inference performance and 3x better training performance compared to the H100. It also features an advanced memory architecture that enhances data processing efficiency.

Q. What are the main drawbacks or considerations for these GPUs?

A. The B200 has higher power consumption requiring robust cooling solutions. The H200 may have limited availability despite offering nearly double the memory capacity and 1.4x the bandwidth of the H100.

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