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NVIDIA H100 vs. NVIDIA H200 Comparison: Which GPU Fits Your AI and Data Center Needs?

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. Please be aware that some figures are still estimation.


GPU Overview and Architecture: NVIDIA H100 vs. NVIDIA H200

Feature

NVIDIA H100

NVIDIA H200

Architecture

Hopper

Enhanced Hopper

Release Date

2022

2024

CUDA Cores

16,896

Estimated 20,000+

Tensor Cores

528

Improved 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: NVIDIA H100 vs. NVIDIA H200


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.

Comparison between NVIDIA H100 and H200 GPUs for advanced AI, deep learning, and high-performance computing (HPC) tasks. Highlights improvements in memory, bandwidth, and processing power, tailored for AI model training, inference, and data-heavy workloads. Ideal for IT professionals seeking insights on GPU performance and server optimization with server-parts.eu.

Real-World Applications: NVIDIA H100 vs. NVIDIA H200


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: NVIDIA H100 vs. NVIDIA H200


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): NVIDIA H100 vs. NVIDIA H200


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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