NVIDIA H100 vs H200-Key Differences, Specs, and Pricing

The NVIDIA H200 is a significant upgrade over the H100, offering nearly double the memory capacity (141 GB vs. 80 GB), higher memory bandwidth (4.8 TB/s vs. 3.35 TB/s), and improved efficiency for AI and high-performance computing workloads. While both GPUs share the Hopper architecture and many core compute specs, the H200 delivers up to 45% better performance in AI model processing at a higher power ceiling and cost. Pricing differences reflect these upgrades, with the H200 costing approximately 25-50% more than the H100, especially in cloud environments like Cyfuture Cloud, which provides flexible GPU hosting solutions tailored for enterprise AI needs.

Overview of NVIDIA H100 and H200 GPUs

NVIDIA’s H100 and H200 GPUs both utilize the Hopper architecture and serve as cutting-edge accelerators for AI training, inference, and high-performance computing (HPC). The H100 revolutionized AI workloads at launch by offering enhanced floating-point performance and introducing FP8 data types. The H200 advances these capabilities, focusing on larger models and more efficient cloud deployment. Its increased memory capacity and bandwidth make it well-suited for massive AI models like GPT-4 and other large language models (LLMs).

Architecture and Specifications Comparison

Beyond raw specs, the H200 uses next-generation HBM3e memory, enabling greater efficiency in bandwidth-heavy tasks.

Performance Improvements in H200 over H100

The H200 offers:

Nearly double the memory capacity (141 GB vs. 80 GB), enabling processing of larger datasets and models without splitting across multiple GPUs.

A 43-45% higher memory bandwidth, accelerating training and inference speeds, especially for large AI and HPC workloads.

Larger MIG slices (up to 18 GB per instance), which can be more efficient for multi-instance GPU virtualization.

Similar peak computational throughput but increased efficiency per watt, important for power-conscious data centers.

Real-world benchmarks reveal up to 45% improvement on large language model inference tasks when configured at the same power limits.

Pricing Differences and Value Consideration

The H200 commands a premium due to its advanced capabilities, with prices approximately 25-50% higher than the H100 both for outright purchase and cloud usage. For enterprises, this cost difference is weighed against:

Reduced model training and inference times.

Potential operational savings via more efficient workloads.

The ability to run larger models on a single GPU.

Purchasing GPUs outright vs. cloud hosting via providers like Cyfuture Cloud can alter cost calculations. Cyfuture Cloud offers flexible, enterprise-grade GPU hosting that enables leveraging H100 or H200 GPUs without upfront hardware investments, with competitive pricing tailored to client needs.

Use Cases and Cloud Hosting with Cyfuture Cloud

Both GPUs target AI research labs, large enterprises, and HPC centers, supporting:

AI model training and fine-tuning, including LLMs.

Deep learning inference at scale.

Data analytics, scientific simulation, and HPC workloads.

Cyfuture Cloud’s GPU hosting plans provide access to these GPUs on flexible terms, ideal for:

Businesses aiming to avoid high upfront capital expenses.

Teams requiring scalable AI infrastructure for variable workloads.

Organizations seeking performance enhancements by moving to next-gen GPUs like the H200.

Cyfuture Cloud supports seamless access to both NVIDIA H100 and H200 GPUs, offering optimized environments for cloud AI workloads.

Frequently Asked Questions (FAQs)

What are the main technical differences between NVIDIA H100 and H200?

The H200 offers 141 GB HBM3e memory, 4.8 TB/s bandwidth, and improved multi-instance GPU slices, compared to the H100’s 80 GB HBM3 memory and 3.35 TB/s bandwidth. Both share the same Hopper architecture and similar peak FLOPS specs.

Is the performance gain worth the higher price of the H200?

If workloads require processing large models or datasets, the H200’s higher memory and bandwidth justify the up to 50% price premium. For smaller workloads, the H100 remains a cost-effective choice.

Should I buy GPUs or use cloud hosting?

Cloud hosting via providers like Cyfuture Cloud reduces upfront costs and offers flexible scaling. It is suited for startups, research labs, or enterprises adopting AI workloads without hardware management overhead.

Conclusion

The NVIDIA H200 is a substantial evolution of the H100, especially suited for large-scale AI and HPC workloads needing enhanced memory capacity and bandwidth. While it comes at a higher price point, the efficiency and performance gains can deliver considerable value for demanding applications. Businesses can access these GPUs via flexible cloud hosting options such as Cyfuture Cloud, balancing cost and performance without heavy upfront investments.