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

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Nov 3, 2024
3 min read

Updated: Oct 4

NVIDIA’s Hopper architecture has reshaped how enterprises approach AI and high-performance computing (HPC). The NVIDIA H100 set the standard for large-scale AI training and data-heavy workloads, while the NVIDIA H200 extends these capabilities with major improvements in memory, bandwidth, and energy efficiency.

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The following sections and tables compare both GPUs to help you determine which one fits your performance needs and infrastructure plans.

NVIDIA H100 vs H200 GPU comparison for AI, HPC, and data center workloads showing detailed specifications, performance benchmarks, CUDA cores, Tensor Cores, HBM3 and HBM3e memory bandwidth, TFLOPS processing power, LLM and machine learning inference efficiency, and total cost of ownership advantages of the H200 over the H100 based on Hopper and Enhanced Hopper architecture. server-parts.eu. refurbished. used.

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	Enhanced Tensor Cores
Memory	80GB HBM3	141GB HBM3e
Memory Bandwidth	3.35 TB/s	4.8 TB/s
Processing Power (FP32)	Up to 67 TFLOPS	Up to 80 TFLOPS
Description	Built for large-scale simulations, analytics, and AI training	Designed for real-time inference, large language models (LLMs), and demanding HPC workloads

Key Specification Comparison: NVIDIA H100 vs. NVIDIA 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 × 10GB	7 × 16.5GB

Performance Metrics: Memory, Bandwidth, and Inference Speed - NVIDIA H100 vs. NVIDIA H200

Memory and Bandwidth:

The NVIDIA H200 nearly doubles the available memory compared to the NVIDIA H100. Its 141GB of HBM3e memory and 4.8 TB/s bandwidth deliver up to 1.4× faster data movement, reducing latency and improving throughput in data-intensive workloads. For tasks like AI inference and HPC simulations, this boost allows smoother handling of larger models and datasets.

Inference Performance:

In MLPerf benchmark tests, the H200 achieved up to 42% more tokens per second than the NVIDIA H100 in offline inference scenarios. This shows clear gains in LLM performance and real-time response speed for generative AI applications.

Real-World Applications: Which GPU Fits Your Workload? - NVIDIA H100 vs. NVIDIA H200

AI and Machine Learning:

The NVIDIA H200 is ideal for advanced AI training and inference, especially for large-scale language models and next-generation NLP tasks.

HPC and Scientific Research:

With higher memory bandwidth and capacity, the NVIDIA H200 handles complex simulations and massive datasets more efficiently than the NVIDIA H100.

Data Centers:

The NVIDIA H100 remains an excellent choice for established workloads. However, the NVISIA H200 is better suited for organizations planning future AI infrastructure upgrades or needing to support more memory-demanding models.

Energy Efficiency and Operational Costs - NVIDIA H100 vs. NVIDIA H200

Both GPUs are built for performance efficiency, but the H200 offers better performance-per-watt for demanding inference tasks. It can deliver up to 50% lower energy consumption per inference compared to the NVIDIA H100, reducing long-term operational expenses.However, due to its higher 1,000W power draw, it may require stronger cooling systems, which can increase initial setup costs.

Cost and Total Cost of Ownership (TCO) - NVIDIA H100 vs. NVIDIA H200

Initial Cost:

NVIDIA H100: Around $20,000–$25,000
NVIDIA H200: Estimated $25,000–$30,000

The H200’s higher price reflects its increased capabilities and efficiency. For companies focused on long-term scalability and performance, the investment can be justified.

Operational Cost:

Thanks to its efficiency in LLM and inference tasks, the NVIDIA H200 can reduce total cost of ownership (TCO) by up to 50% compared to the NVIDIA H100, making it appealing for large-scale deployments where power and time savings accumulate quickly.

Final Thoughts: NVIDIA H100 or NVIDIA H200?

Both GPUs are powerful choices for AI and HPC applications, but their use cases differ slightly.

Choose the NVIDIA H100 if you need a proven, high-performance GPU at a lower cost for current AI or HPC workloads.
Choose the NVIDIA H200 if your focus is on advanced AI training, large-scale inference, or preparing for future AI demands with higher efficiency and memory capacity.

NVIDIA H100 & H200 GPUs: Save Up To 80%