NVIDIA’s Hopper architecture has redefined AI and HPC capabilities. The NVIDIA H100 set high standards, and the recently launched H200 takes it further with major upgrades in memory, bandwidth, and efficiency. The following comparison tables highlight the differences to help you decide which GPU is best suited for your advanced AI and data-heavy workloads.
GPU Overview and Architecture
Feature | NVIDIA H100 | NVIDIA H200 |
Architecture | Hopper | Enhanced Hopper |
Release Date | 2022 | 2024 |
CUDA Cores | 16,896 | Estimated 20,000+ |
Tensor Cores | 528 | Enhanced Tensor Cores |
Memory | 80GB HBM3 | 141GB HBM3e |
Memory Bandwidth | 3.35 TB/s | 4.8 TB/s |
Processing Power | Up to 67 TFLOPS (FP32) | Up to 80 TFLOPS (FP32) |
Description | Designed for high-demand applications like large-scale simulations, complex data analytics, and AI model training | Increased memory and bandwidth make it suitable for real-time AI inference, large language models, and HPC tasks |
Key Specification Comparison: NVIDIA H100 vs. H200
Feature | NVIDIA H100 | NVIDIA H200 |
CUDA Cores | 16,896 | Estimated 20,000+ |
Tensor Cores | 528 | Enhanced |
Memory Type | HBM3 | HBM3e |
VRAM Capacity | 80GB | 141GB |
Memory Bandwidth | 3.35 TB/s | 4.8 TB/s |
Power Draw (TDP) | Up to 700W | Up to 1,000W |
PCIe Support | PCIe 5.0 | PCIe 5.0 |
NVLink Bandwidth | 900 GB/s | 900 GB/s |
MIG Instances | 7 at 10GB each | 7 at 16.5GB each |
Looking for NVIDIA H100 and H200 GPUs?
Performance Metrics: Memory, Bandwidth, and Inference Speed
Memory and Bandwidth: The H200 nearly doubles the H100’s memory, which is essential for managing larger AI models and datasets. The 141GB HBM3e memory with 4.8 TB/s bandwidth provides a 1.4x speed boost for data transfers, reducing latency and bottlenecks in high-throughput applications. For memory-intensive tasks like AI inference and HPC simulations, this memory upgrade significantly impacts performance.
Inference Performance: The H200 excels in natural language processing and LLMs. In recent MLPerf benchmarks, the H200 achieved up to 42% more tokens per second than the H100 in offline scenarios, proving its efficiency for generative AI tasks.
Real-World Applications
The choice between NVIDIA H100 and H200 depends on your workload needs:
AI and Machine Learning: The H200 is ideal for next-gen AI models, with faster inference times and superior training capabilities for tasks like LLMs and NLP.
HPC and Scientific Research: For simulations, data analysis, and complex computations, the H200’s improved bandwidth and memory allow faster processing of large datasets.
Data Centers: The H100 remains a solid option for current workloads, but the H200 is designed to future-proof infrastructure, supporting emerging applications with advanced performance metrics.
Energy Efficiency and Operational Costs
While both GPUs focus on energy efficiency, the H200 demonstrates better performance-per-watt, particularly for demanding LLM tasks. It consumes up to 50% less energy per inference workload compared to the H100, reducing operational costs over time. However, the H200’s higher power draw (up to 1,000W) may require additional cooling, impacting initial setup costs.
Cost Considerations and Total Cost of Ownership (TCO)
Initial Cost:
NVIDIA H100: Approx. $25,000 - $30,000
NVIDIA H200: Estimated $30,000 - $40,000
The H200 is available at a premium price due to its advanced features and increased capabilities. However, organizations aiming to maximize efficiency and future-proof their infrastructure may find the H200’s higher price justified.
Operational Costs:
The H200 is more energy-efficient for AI tasks, reducing long-term operational expenses. Its 50% reduction in TCO for LLM tasks makes it an attractive investment for businesses focused on minimizing total ownership costs while maintaining high performance.
Conclusion: Choosing Between NVIDIA H100 and H200
The NVIDIA H100 and H200 GPUs each offer unique advantages for specific needs:
Choose the H100: if you require a powerful, reliable GPU for existing AI and HPC applications at a more accessible price.
Choose the H200 for advanced AI training, real-time data processing, and future-proofing your infrastructure, especially if handling complex models like LLMs is a priority.
Both GPUs mark NVIDIA’s commitment to pushing AI and HPC capabilities forward, and either choice ensures exceptional performance for your data center.
Get your NVIDIA H100 and H200 GPUs!
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