NVIDIA H200 vs H100: Which GPU Is Right for Your Dedicated Server?

The rise of GPU-powered dedicated servers has redefined the landscape of advanced computing, powering everything from AI model training to real-time inference at unprecedented speeds. NVIDIA has emerged as the industry benchmark in this space, offering GPUs that combine high-speed data handling with exceptional processing power. 

In this comprehensive blog post, we will compare NVIDIA H200 vs H100 side by side with their features, architecture, speed, memory configuration, and much more. Keep reading!

A] Understanding the NVIDIA H100 GPU

Built on Hopper architecture, the NVIDIA H100 GPU has become a significant processor for handling massive data loads without any performance drops. It offers a massive data transfer speed which is over 3.8TB per second. It made the monumental leap over traditional GPUs by offering impressive processing power for large-scale AI model training and inference. It is capable of computing hundreds and thousands of calculations simultaneously, all thanks to its parallel computing method. The integration of second-generation MIG technology creates several partitions within a single GPU, offering separate computing resources for each partitioned GPU instance. It is ideal for AI researchers, model training, and deep learning deployment. 

B] Exploring the NVIDIA H200 GPU

Built on evolved Hopper architecture with HBM3e memory, the NVIDIA H200 GPU is the upgraded version of the NVIDIA H100 GPU. Like NVIDIA H100, it is suitable for generative AI, LLMs (large language models), and memory-intensive tasks. In addition, it offers an impressive ground for inference-heavy environments and real-time AI services with 4.8TB per second memory bandwidth. With faster memory fuels, it provides a future-ready option for demanding AI applications and HPC (high-performance computing) workloads. 

Now, let’s compare NVIDIA H100 and H200 while focusing on clarity, objectivity, and the value that both future-ready GPUs offer.

C] Key Performance Attributes: H100 and H200 in Focus

1. Architecture Foundation

When comparing NVIDIA H200 vs H100, H100 is based on the Hopper architecture and second-generation MIG technology. It has specialised hardware units, known as Tensor cores, with a fourth-generation transformer engine to accelerate matrix operations for deep AI and other AI workloads. 

NVIDIA H200 GPU, on the other hand, is based on the evolved Hopper architecture. Like H100, it has fourth-generation Tensor cores with a transformer engine for performance-intensive AI workloads. It has integrated MIG technology, allowing a single GPU to partition into multiple GPU instances to parallelly divide the loads and accelerate the performance.

2. Memory Configuration

NVIDIA H100 is backed by the HBM3 memory type with 80GB memory capacity. It has 3.35 TB per second memory bandwidth, offering impressive system performance and eliminating performance bottlenecks. H100 is combined with the L2 caching, allowing swift performance of the memory-intensive AI tasks. 

NVIDIA H200, on the other hand, is backed by the improved HBM3e memory type and enhanced 141 GB memory capacity. As the H200 is the upgraded version of the H100, it offers high bandwidth and capacity compared to the previous HBM3 memory type that is used in H100 GPUs. The enhanced memory and capacity provide a tailored performance ground for large AI models handling tasks. 

3. Speed

The higher memory bandwidth, faster processing unit, and partitioning of the GPU through MIG technology make the NVIDIA H100 one of the ultra-fast graphics processing units in this ever-changing computing environment. The H100’s transformer engine delivers 9x faster performance when it comes to AI model training and 30x faster inference compared to its counterparts, such as the A100 GPU.

NVIDIA H200 takes a significant leap when it comes to speed, delivering unmatched processing power for larger and extensive HPC workloads, such as inference-heavy environments and real-time AI services. It has 141GB of HBM3e memory with 4.8TB per second bandwidth, which offers 1.4x increased speed compared to its predecessor.

4. AI & ML Workload Suitability

NVIDIA H100 is specially designed to efficiently handle AI and ML (machine learning) workloads without any performance bottlenecks. As it is built on Hooper technology, it offers a highly efficient ground for performance-intensive AI workloads, such as deep learning, data analysis, and large-scale simulations. 

NVIDIA H200, on the other hand, offers a large memory capacity and higher bandwidth, providing a solid ground for extensive AI and ML workloads. It is designed to prioritise inference performance and next-gen model throughput, allowing H200 to be the top choice for large AI and ML workloads.

5. Data Center Fit

NVIDIA H100 is a proven GPU for handling resource-intensive tasks in data centers to accelerate AI and deep learning data handling and processing. Absolute Cloud, a renowned provider of cloud computing solutions, utilises the NVIDIA H100 GPU in its data center to provide a massive performance leap over CPU-based processing units. 

NVIDIA H200 is designed to power modern data centers like Absolute Cloud offers to its tenants. It has the power to efficiently handle high I/O needs without performance hindrance. At Absolute Cloud, we provide both GPU options for our clients based on their performance requirements and HPC needs.

6. Scalability & Power Efficiency

H100 is designed with scalability and power efficiency in mind. It features NVLink, multi-instance GPU, and enhanced memory architecture to provide efficient scaling for complex AI models and data processing tasks. Besides, it optimises power usage while in operation, making it a well-suited GPU for data centers and HPC applications. 

When comparing the H200 vs H100 GPU, H200 offers enhanced scalability and power efficiency. It has improved the compute-to-power ratio for intensive use cases. Absolute Cloud offers highly scalable and power-efficient NVIDIA H100 and H200 for diverse industrial and business needs.

7. Industry Use Alignment

When exploring Nvidia H100 and H200 difference, H100 is exclusively suitable for research labs, enterprise AI teams, and organisations that have HPC workloads. In addition, it is highly suitable for large-scale operations and data centers. 

H200 is best suited for generative AI platforms, LLM, interference-as-a-service providers, deep learning related tasks, and scientific calculations. In addition, it is effectively suited for vision AI and excels in areas like LLM training and inference. 

D] When to Choose H100 or H200 For Dedicated Server: Matching GPU to Your Use Case

Selecting between the NVIDIA H100 and H200 GPUs often comes down to a business’s core computing objectives. Technical factors, such as required memory bandwidth, high inference loads, and reliance on parallel computing, significantly influence the decision. Absolute Cloud offers end-to-end consulting to help businesses select configurations that precisely match their workload needs, ensuring high performance, energy efficiency, and future-proof scalability.

E] Why to Choose Absolute Cloud’s GPU-Powered Dedicated Servers?

Absolute Cloud offers fully customisable dedicated servers built around the powerful NVIDIA H100 and H200 GPUs. Designed for AI and high-performance computing, our data centres are optimised to support parallel processing, large-scale model training, and intensive inference tasks. With flexible GPU provisioning, businesses can scale their computing resources as needed. Trusted by AI research teams, technology startups, and enterprise-scale applications, our infrastructure supports everything from training foundational models to deploying production-grade inference systems with our state-of-the-art NVIDIA H100 and H200 GPUs.

Conclusion

Absolute Cloud proudly supports both NVIDIA H100 and H200 GPUs, giving businesses the flexibility to choose hardware that matches their performance goals. Whether the requirement is high-throughput training or memory-intensive inference, the objective is to help organisations align their GPU choice with the specific demands of their workloads and long-term infrastructure plans. Choose our modern H100 and H200-enabled dedicated servers from Absolute Cloud, a leading provider of GPU dedicated servers in India. Contact us today!

Also Read: Transforming AI Model Training? How is Cloud GPU Infrastructure