NVIDIA H100 vs H200 Key Differences Specifications and Pricing

The NVIDIA H200 GPU is an upgraded version of the H100, both built on the Hopper architecture, designed for AI and high-performance computing (HPC) workloads. The H200 offers nearly double the memory capacity (141 GB HBM3e vs. 80 GB HBM3), around 43% higher memory bandwidth (4.8 TB/s vs. 3.35 TB/s), and improved cloud computing efficiency, but comes at a notably higher price. While the H100 remains an excellent choice for many AI workloads, the H200’s enhanced specs provide substantial performance improvements for large-scale AI models and enterprise applications. Cyfuture Cloud offers flexible cloud GPU hosting solutions for both GPUs, enabling businesses to access cutting-edge AI hardware without heavy upfront investments.

Overview of NVIDIA H100 and H200 GPUs

The NVIDIA H100 was a breakthrough in AI acceleration, featuring enhanced computational power with the Hopper GPU architecture. It introduced improvements in streaming multiprocessors (SM) and supported new data formats like FP8 for faster AI calculations

The H200 builds on the foundation of the H100 with significant memory and bandwidth upgrades. It uses advanced HBM3e memory technology, supports larger multi-instance GPU configurations, and is tailored for more demanding AI workloads such as large language models (LLMs) and high-throughput inference tasks. These improvements translate to better efficiency and performance for enterprise-grade AI and HPC applications.

Key Spec Differences Between H100 and H200

The standout differences include the GPU memory nearly doubling from 80 GB to 141 GB and the memory bandwidth increasing by ~43%, allowing the H200 to efficiently handle larger AI models and datasets in one GPU without bottlenecks. The H200 also supports larger multi-instance GPU (MIG) sizes, further improving workload flexibility.

Performance Comparisons and Use Cases

The H200 delivers up to 45% better performance on certain AI benchmarks such as Llama 2-70B inference tasks when power consumption is set optimally.

Both GPUs maintain similar compute throughput in terms of FP32 and tensor operations, but the H200’s larger memory and bandwidth improve overall training and inference speed, especially for large-scale models.

The H100 offers strong value and performance for mid-sized AI projects or workloads that fit within its 80 GB memory limit.

The H200’s efficiency gains make it ideal for enterprises pushing the limits of AI research, generative AI, and HPC workloads requiring extensive parallel compute and data throughput.

Pricing Comparison: On-Premise vs. Cloud Hosting

The H200 is 25-50% more expensive than the H100 both for outright hardware purchase and cloud GPU rental.

The price premium stems from the H200’s memory size, advanced HBM3e memory, and higher memory bandwidth.

Purchasing physical GPUs involves high upfront costs; cloud GPU hosting offers a flexible and scalable alternative.

Cyfuture Cloud provides competitive pricing for both H100 and H200 GPUs with scalable plans, eliminating the need for large capital expenditure and allowing businesses to access the latest GPUs as needed.

Cloud options reduce operational risks and allow for better budget control, especially important for startups and enterprises scaling AI workloads.

Why Choose Cyfuture Cloud for GPU Hosting?

Harness NVIDIA’s H100 and H200 Power with Cyfuture Cloud

Flexible GPU Hosting Plans: Adjust GPU allocations and usage time based on workload demands and budget.

Cost-Effective Access: Avoid upfront hardware investment and maintenance costs.

Enterprise-Grade Infrastructure: High availability, security, and optimized performance for AI and HPC workloads.

Multi-GPU Support: Access advanced NVIDIA GPUs like H100 and H200 for large-scale AI model training and inference.

Dedicated Support: Expert assistance for cloud integration and GPU optimization.

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Frequently Asked Questions

What are the main advantages of the H200 over the H100?

The H200 offers nearly double the memory (141 GB vs. 80 GB) and significantly higher bandwidth (4.8 TB/s vs. 3.35 TB/s), improving performance on large AI models and HPC applications.

Is the higher price of the H200 justified?

For memory-intensive AI workloads and enterprise cloud deployments, the H200’s performance gains and efficiency justify the 25-50% higher cost. For smaller workloads, H100 remains a cost-effective option.

Can Cyfuture Cloud provide both H100 and H200 GPUs?

Yes, Cyfuture Cloud offers flexible cloud GPU hosting

 for both H100 and H200, tailored to enterprise AI needs.

Conclusion

Both the NVIDIA H100 and H200 GPUs represent the cutting edge of AI acceleration technology with the Hopper architecture. While the H100 remains exceptionally capable, the H200’s enhanced memory, bandwidth, and efficiency establish it as the superior choice for demanding AI, HPC, and cloud workloads. Given the significant price difference, the choice depends on specific workload requirements and budget constraints.