What is the NVIDIA H200

The effort to develop more and more powerful GPUs remains ever constant with the growth of the AI sector, as does the effort to match user needs in the cloud. More and more research projects in this space are being released every day. Furthermore, the hardware needs to be more and more optimized for new technological advancements.

NVIDIA Hopper’s release, the second most recent generation of microarchitecture released for their GPU products, aimed at solving such problems of scale before they were created. With new technologies like the Transformer Engine, advancements to interconnectivity for distributed setups, and advanced confidential computing, Hopper truly took AI development on the cloud a step further with the release of the NVIDIA H100.

Following up on this machine came the NVIDIA H200, which was recently released on DigitalOcean’s GPU Droplets. In this article, we will take a deeper look at what makes the NVIDIA H200 so powerful & consider where the GPU should be used.

Machine Overview: NVIDIA H200

The NVIDIA H200 is an incredibly powerful GPU. At the time of release, it was the most powerful consumer GPU on the market, and it remains a titan for AI development. The H200 features all of the upgrades that made the NVIDIA H100 so much more powerful than its predecessor, the NVIDIA A100, and more. Notably, the H200 features a slightly updated and augmented version of the Hopper microarchitecture, nearly doubles the memory capacity of HBM3E, and offers 1.4 times the memory bandwidth of the H100.

Let’s look at the features of the H200 more closely.

Features of the NVIDIA H200

• HBM3E memory technology: developed by Micron, this is the highest bandwidth memory available on the cloud, enabling 4.8 terabytes per second (TB/s) memory bandwidth on the NVIDIA H200
• 141 gigabytes of memory capacity: offering close to double the NVIDIA H100s memory, this GPU capacity enables the largest Deep Learning models to run training or inference on single or distributed setups
• Fourth-Generation Tensor Cores with the Transformer Engine: the H200 uses the same fourth-gen Tensor Core technology as the H100. This is facilitated by the Transformer Engine, “a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper, Ada, and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference” (Source)
• Second-Generation Secure MIG: the H200 can be split into seven secure and concurrent GPU instances with multi-tenant, multi-user configurations in virtual environments with 16.5GB of memory each.
• Fourth-Generation NVLink: Fourth-generation NVLink effectively scales multi-instance GPU IO interactions up to 900 gigabytes per second (GB/s) bidirectional per GPU, which is estimated to be over 7X the bandwidth of PCIe Gen5. (Source)
• Third-Generation NVSwitch: Third-generation NVIDIA NVSwitch supports Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) in-network computing, and provides a 2X increase in all-reduce throughput within eight H100 (or H200) GPU servers compared to the previous-generation A100 Tensor Core GPU systems (Source)

All together, these features create one of the most powerful tools for AI development available.

NVIDIA H200 vs NVIDIA H100

The NVIDIA H200 should be compared especially often with its smaller predecessor, the NVIDIA H100, wherever possible.

As we can see from the table above, there is a huge overlap in the capabilities of these two machines from the same microarchitecture generation. This makes logical sense, given that the core technologies making up the NVIDIA H100 and H200 are the same. The key differences lie of course with the GPU Memory and GPU Memory bandwidth, which we touched on earlier. Other notable differences include their max thermal design power and the size of the Multi-Instance GPUs: The H200 can handle higher wattage and better dissipating heat than the H100, and it can host significantly larger MIGs.

When to use the NVIDIA H200

Given the advantages of the increased memory capacity and throughput, the NVIDIA H200 is a significantly more powerful machine than the H200. Accordingly, the pricing for these machines on the cloud will reflect that. Thus, it is important to consider when and where we might want to use an NVIDIA H200.

First, the NVIDIA H200 should always be considered first where efficiency is concerned. The massive increase to throughput alone guarantees that you will run AI training or inference with significantly improved performance speed over the NVIDIA H100. The other improvements to the Hopper microarchitecture also help to accelerate GPU processes, especially Large Language Models (LLMs).

The second thing to always consider is cost. The NVIDIA H200 is more powerful than the NVIDIA H100, and it is correspondingly more expensive to run and therefore access on the cloud. When cost is a concern, we have to consider if the smaller NVIDIA H100 can handle the task. If it can, then it may be worthwhile using the NVIDIA H100 instead.

If the NVIDIA H100’s memory is too small to handle a task, such as trying to host a state of the art LLM, then the NVIDIA H200 may be a superior option. This brings us to the third consideration which is computational expense. We must always consider if the GPU will be able to handle the task we are giving it within the constraints of its hardware. Since the NVIDIA H200 has significantly larger memory, 141 GB to the NVIDIA H100’s 80 GB, it is always the better option when taking on the largest models. For example, an 8xH100 setup could not run DeepSeek-R1 while a 8xH200 setup could.

In summary, these three considerations give us a framework with which we can select the best GPU for our use case. While both machines have their strengths and weaknesses, we almost always recommend the NVIDIA H200 for AI inference and training tasks because of its higher performance capabilities. This is unless cost is the most important consideration, in which case the H100 may be a suitable alternative.

Closing Thoughts

The NVIDIA H200 is an incredibly potent GPU for AI training and inference, and is a notable upgrade to the NVIDIA H100. We recommend using it for all Deep Learning related tasks, and its evident that it is already playing a major role in the ongoing AI revolution.

Try the NVIDIA H200 on DigitalOcean’s Bare Metal GPU’s today! Fill out the form here for more information.