NVIDIA H100 vs. H200: What is the Difference and Which Should You Buy?

The race for superior GPU performance continues as NVIDIA introduces its H200, the successor to the already impressive H100. Both of these GPUs represent the cutting edge of high-performance computing, AI, and deep learning. But as enterprises, researchers, and data centers evaluate their next upgrade, the key question arises: Which is better, the NVIDIA H100 or the NVIDIA H200? Let’s dive into a detailed comparison of these two GPUs to find out.

NVIDIA H100 Overview

The NVIDIA H100, part of the Hopper architecture, was designed to accelerate workloads across a variety of industries. Launched in 2022, the H100 quickly became the go-to GPU for AI training, large-scale simulations, and complex data analytics. It features:

• CUDA Cores: 16,896
• Tensor Cores: 528
• Memory: 80 GB HBM2e
• Memory Bandwidth: 3.2 TB/s
• Processing Power: Up to 60 TeraFLOPS (FP64), 1,000 TeraFLOPS (Tensor Float 32)

The H100 was a significant leap forward from its predecessor, the NVIDIA A100, delivering up to 3x the performance for AI workloads. Its versatility and power have made it a staple in data centers worldwide.

NVIDIA H200 Overview

NVIDIA H200, expected to be released in August 2024, is the latest evolution in the Hopper series. It’s designed to push the boundaries even further, catering to the growing demand for real-time AI inference, complex simulations, and other high-compute tasks. While detailed specifications are still emerging, early benchmarks and leaks provide us with a glimpse of its capabilities:

• CUDA Cores: Estimated around 20,000+
• Tensor Cores: Enhanced and more numerous than the H100
• Memory: 141 GB HBM3
• Memory Bandwidth: 4.8 TB/s
• Processing Power: Expected up to 80 TeraFLOPS (FP64), 1,500 TeraFLOPS (Tensor Float 32)

The H200 is anticipated to offer significant improvements in both raw computing power and memory bandwidth, making it a formidable choice for next-generation AI and HPC applications.

Performance Comparison: H100 vs. H200

1. Processing Power

The H200’s architecture is designed to deliver approximately a 30% increase in processing power compared to the H100. This improvement is crucial for applications requiring massive parallel processing, such as large language models (LLMs), deep learning frameworks, and advanced simulations. The H200’s increased CUDA core count and refined Tensor cores further enhance its ability to handle complex computations faster and more efficiently.

2. Memory and Bandwidth

One of the standout features of the H200 is its upgraded memory system. With 96 GB of HBM3 memory, the NVIDIA H200 not only offers more capacity but also significantly higher bandwidth. The 4.5 TB/s bandwidth provides faster data access, which is essential for high-performance tasks where large datasets are processed in real-time. This improvement reduces bottlenecks and accelerates workflows, particularly in data-heavy applications.

3. AI and Deep Learning

For AI and deep learning tasks, the H200’s enhanced Tensor cores and higher processing power make it the superior choice. The H200 is designed to accelerate both training and inference, with a particular focus on optimizing large AI models that require intensive computational resources. The H100 is no slouch in this area, but the H200’s architecture is purpose-built to handle the demands of next-generation AI workloads more efficiently.

4. Energy Efficiency

While performance is crucial, energy efficiency is also a significant consideration for data centers and enterprises. The H200 is expected to deliver better performance-per-watt compared to the H100, thanks to advancements in NVIDIA’s architecture and manufacturing processes. This efficiency not only reduces operational costs but also aligns with the growing emphasis on sustainability in IT infrastructure.

Real-World Applications

The choice between the H100 and H200 largely depends on your specific use case. If you’re running current-generation AI models, simulations, or high-performance computing tasks, the H100 is more than capable. However, if you’re looking to future-proof your infrastructure, handle next-gen AI models, or push the envelope in real-time data processing, the H200 offers a significant upgrade.

GPU Cost Difference vs. Performance

When considering whether to invest in the NVIDIA H100 or the H200, one of the most crucial factors for many organizations is the cost-performance ratio. While the H200 undoubtedly offers superior performance, this comes with a price premium that may influence your decision depending on your specific needs and budget constraints.

1. Initial Cost

The H200, being the newer and more advanced GPU, is expected to carry a significantly higher price tag compared to the H100. Early estimates suggest that the H200 could be priced 30-50% higher than the H100, reflecting its cutting-edge technology, increased memory, and enhanced processing power. The NVIDIA H100, on the other hand, has become more affordable as it has matured in the market, making it a more budget-friendly option for organizations that do not require the latest advancements.

• NVIDIA H100: The list MSRP for the NVIDIA H100 varies depending on the specific model and configuration, but it typically ranges between $25,000 to $30,000.
• NVIDIA H200: Since the H200 is a newer release, specific pricing details might vary or may not be widely available. However, it is anticipated to be in a similar or slightly higher range than the H100, potentially $30,000 to $40,000 depending on the configuration.

2. Cost-Performance Ratio

While the H200 offers up to 30% more processing power and significantly higher memory bandwidth, the cost-performance ratio may vary depending on the workload. For tasks that can fully utilize the H200’s capabilities, such as next-generation AI models or complex simulations, the performance gains could justify the higher initial investment. However, for less demanding applications, the H100 might offer a better cost-performance ratio, delivering sufficient power at a lower cost.

3. Operational Costs

Another aspect to consider is the operational cost, which includes power consumption and cooling requirements. The H200 is expected to be more energy-efficient on a per-watt basis, but its increased power draw could lead to higher electricity costs if not managed properly. Data centers may need to invest in upgraded cooling systems to accommodate the H200’s higher thermal output, adding to the overall cost. The H100, while still powerful, may be easier to integrate into existing infrastructure without the need for significant additional investment in power and cooling.

4. Total Cost of Ownership (TCO)

When evaluating the total cost of ownership (TCO), it’s essential to consider not just the upfront hardware cost but also the long-term operational expenses. The H200’s higher efficiency could lead to lower energy costs over time, partially offsetting its higher purchase price. However, the NVIDIA H100 may still present a lower TCO in scenarios where its performance is sufficient for the required tasks and where energy costs are less of a concern.

5. Future-Proofing

Investing in the H200 may provide better future-proofing, allowing organizations to handle more advanced workloads as they emerge. This could lead to cost savings in the long run, as upgrading from the H100 to the H200 at a later date might incur additional expenses beyond the initial hardware costs, such as downtime, integration, and configuration efforts. Therefore, the higher initial cost of the H200 could be seen as an investment in future capabilities and scalability.

Conclusion: Balancing Cost and Performance

The choice between the H100 and H200 ultimately depends on how you value the balance between cost and performance. If your workloads demand the absolute best in processing power, memory bandwidth, and efficiency, and you are prepared to invest in the latest technology, the H200 is the optimal choice despite its higher cost. However, if you are looking for a more cost-effective solution that still delivers exceptional performance for current applications, the H100 might offer the best value.

In summary, the H200’s premium cost is justified by its advanced features and future-proofing potential, but the H100 remains a formidable contender for those who prioritize a strong cost-performance ratio and are managing within tighter budget constraints.

About TRG Datacenters

TRG Datacenters is designed to support the most demanding high-performance computing environments, including deployments with NVIDIA H100 and H200 GPUs. Our facilities are equipped with high-density racks, advanced liquid cooling systems, and ultra-low latency data center interconnects to ensure your GPU clusters operate at peak performance and efficiency.

Whether you're running AI, deep learning, or complex simulations, our fully managed colocation services make remote deployments seamless. Simply ship us your NVIDIA GPU clusters, and we'll handle the rest, providing you with a reliable, high-performance infrastructure to drive your business forward.