NVIDIA B200 vs. NVIDIA H100: When to choose which

GPU cloud services deliver high-performance computing capabilities with specialized infrastructure for AI and machine learning workloads. Users get access to GPU clusters connected through high-bandwidth networks. This enables distributed processing and faster model training.

These services include pre-configured environments optimized for common AI frameworks. This reduces setup time and complexity. The infrastructure scales based 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 offerings. Pricing models are usage-based, with costs varying by GPU type, usage duration, and resource allocation.

About the NVIDIA B200

The NVIDIA B200 represents the latest generation of AI accelerators. It packs 192GB of advanced memory into a chip that runs inference 15 times faster than the previous H100 generation. Training speeds are 3 times quicker than the H100.

Users report that the memory architecture makes everything more responsive when working with large models. The main limitation is heat generation. The B200 requires serious cooling solutions and cannot be dropped into existing setups without infrastructure changes.

Research labs working on frontier AI models are natural customers for the B200. Companies building the next wave of AI applications also benefit from its capabilities. If you are training massive models or serving AI applications to millions of users with low latency requirements, the B200 delivers the performance you need.

Cloud providers are purchasing these in bulk because customers demand more compute for ambitious projects. The B200 is overkill for standard AI work. When you are pushing AI boundaries, it provides experimentation room that older hardware cannot match.

About the NVIDIA H100

The NVIDIA H100 represents current peak GPU performance for AI work. It features 80GB of fast memory and roughly 2,000 TFLOPs of computing power. The H100 is built specifically for massive neural networks, especially transformer models.

The H100 excels at the mathematical operations that matter most for training large AI systems. It uses significant power and may exceed requirements for smaller projects. Performance is exceptional for enterprise-scale AI workloads.

Research labs, tech companies building large language models, and high-performance computing organizations are typical users. The H100 is the preferred choice for training models with billions of parameters. It handles simulations that would take excessive time on other hardware.

Universities conducting cutting-edge AI research rely on the H100. Companies developing language model competitors use it for training. Scientific organizations processing massive datasets benefit from its capabilities. For smaller models or basic machine learning, the H100 exceeds most requirements.

Comparison

The NVIDIA B200 offers exceptional performance improvements with up to 15x inference and 3x training performance over the H100. It features 192GB of advanced HBM3e memory. However, pricing is available on request only and power consumption is higher, requiring robust cooling solutions.

The NVIDIA H100 provides proven cutting-edge performance with approximately 2,000 FP16 TFLOPs. It has established market availability at approximately $30,000. Limitations include lower memory capacity at 80GB HBM3 and significantly reduced performance compared to the B200 architecture.

The NVIDIA H100 suits organizations that need proven, immediately available hardware with transparent pricing. It works well for companies with existing infrastructure that can accommodate its power requirements. Organizations wanting predictable costs for large-scale AI training projects benefit from the H100.

The NVIDIA B200 is ideal for cutting-edge research institutions and large enterprises that prioritize maximum performance over cost considerations. It works for organizations that can handle custom pricing negotiations. Superior memory capacity and performance gains make it worth considering for organizations pushing AI model development boundaries and requiring the most advanced capabilities available.

FAQ

Q. What is the pricing difference between NVIDIA B200 and H100 GPUs?

A. The NVIDIA H100 has a retail price of approximately $30,000, while the B200's pricing is available on request. For rentals, the B200 starts at $2.40/hour compared to the H100's approximately $3 to $10/hour range.

Q. How much memory do these GPUs offer and what type?

A. The NVIDIA B200 comes with 192GB of HBM3e memory, while the H100 has 80GB of HBM3 memory. The B200 offers significantly more memory capacity with newer HBM3e technology.

Q. What performance advantage does the B200 have over the H100?

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

Q. What are the best use cases for each GPU?

A. Both GPUs excel at high-performance AI training and inference as well as HPC (High-Performance Computing) tasks. The H100 is optimized for training large AI models and transformer models. The B200 is designed for the most demanding AI workloads.

Q. What are the main drawbacks of these high-end GPUs?

A. Both GPUs have high power consumption requiring robust cooling solutions. Premium pricing may exceed requirements for smaller tasks. The H100 has high price and power consumption. The B200's higher performance comes with increased power requirements.

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