What is the NVIDIA H200

The effort to create much and much powerful GPUs remains ever changeless pinch the maturation of the AI sector, arsenic does the effort to lucifer personification needs successful the cloud. More and much investigation projects successful this abstraction are being released each day. Furthermore, the hardware needs to beryllium much and much optimized for caller technological advancements.

NVIDIA Hopper’s release, the 2nd astir caller procreation of microarchitecture released for their GPU products, aimed astatine solving specified problems of standard earlier they were created. With caller technologies for illustration the Transformer Engine, advancements to interconnectivity for distributed setups, and precocious confidential computing, Hopper genuinely took AI improvement connected the unreality a measurement further pinch the merchandise of the NVIDIA H100.

Following up connected this instrumentality came the NVIDIA H200, which was precocious released connected DigitalOcean’s GPU Droplets. In this article, we will return a deeper look astatine what makes the NVIDIA H200 truthful powerful & see wherever the GPU should beryllium used.

Machine Overview: NVIDIA H200

The NVIDIA H200 is an incredibly powerful GPU. At the clip of release, it was the astir powerful user GPU connected the market, and it remains a titan for AI development. The H200 features each of the upgrades that made the NVIDIA H100 truthful overmuch much powerful than its predecessor, the NVIDIA A100, and more. Notably, the H200 features a somewhat updated and augmented type of the Hopper microarchitecture, astir doubles the representation capacity of HBM3E, and offers 1.4 times the representation bandwidth of the H100.

Let’s look astatine the features of the H200 much closely.

Features of the NVIDIA H200

• HBM3E representation technology: developed by Micron, this is the highest bandwidth representation disposable connected the cloud, enabling 4.8 terabytes per 2nd (TB/s) representation bandwidth connected the NVIDIA H200
• 141 gigabytes of representation capacity: offering adjacent to double the NVIDIA H100s memory, this GPU capacity enables the largest Deep Learning models to tally training aliases conclusion connected azygous aliases distributed setups
• Fourth-Generation Tensor Cores pinch the Transformer Engine: the H200 uses the aforesaid fourth-gen Tensor Core exertion arsenic the H100. This is facilitated by the Transformer Engine, “a room for accelerating Transformer models connected NVIDIA GPUs, including utilizing 8-bit floating constituent (FP8) precision connected Hopper, Ada, and Blackwell GPUs, to supply amended capacity pinch little representation utilization successful some training and inference” (Source)
• Second-Generation Secure MIG: the H200 tin beryllium divided into 7 unafraid and concurrent GPU instances pinch multi-tenant, multi-user configurations successful virtual environments pinch 16.5GB of representation each.
• Fourth-Generation NVLink: Fourth-generation NVLink efficaciously scales multi-instance GPU IO interactions up to 900 gigabytes per 2nd (GB/s) bidirectional per GPU, which is estimated to beryllium complete 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 summation successful all-reduce throughput wrong 8 H100 (or H200) GPU servers compared to the previous-generation A100 Tensor Core GPU systems (Source)

All together, these features create 1 of the astir powerful devices for AI development available.

NVIDIA H200 vs NVIDIA H100

The NVIDIA H200 should beryllium compared particularly often pinch its smaller predecessor, the NVIDIA H100, wherever possible.

As we tin spot from the array above, location is simply a immense overlap successful the capabilities of these 2 machines from the aforesaid microarchitecture generation. This makes logical sense, fixed that the halfway technologies making up the NVIDIA H100 and H200 are the same. The cardinal differences dishonesty of people pinch the GPU Memory and GPU Memory bandwidth, which we touched connected earlier. Other notable differences see their max thermal creation powerfulness and the size of the Multi-Instance GPUs: The H200 tin grip higher wattage and amended dissipating power than the H100, and it tin big importantly larger MIGs.

When to usage the NVIDIA H200

Given the advantages of the accrued representation capacity and throughput, the NVIDIA H200 is simply a importantly much powerful instrumentality than the H200. Accordingly, the pricing for these machines connected the unreality will bespeak that. Thus, it is important to see erstwhile and wherever we mightiness want to usage an NVIDIA H200.

First, the NVIDIA H200 should ever beryllium considered first wherever ratio is concerned. The monolithic summation to throughput unsocial guarantees that you will tally AI training aliases conclusion pinch importantly improved capacity velocity complete the NVIDIA H100. The different improvements to the Hopper microarchitecture besides thief to accelerate GPU processes, particularly Large Language Models (LLMs).

The 2nd point to ever see is cost. The NVIDIA H200 is much powerful than the NVIDIA H100, and it is correspondingly much costly to tally and truthful entree connected the cloud. When costs is simply a concern, we person to see if the smaller NVIDIA H100 tin grip the task. If it can, past it whitethorn beryllium worthwhile utilizing the NVIDIA H100 instead.

If the NVIDIA H100’s representation is excessively mini to grip a task, specified arsenic trying to big a authorities of the creation LLM, past the NVIDIA H200 whitethorn beryllium a superior option. This brings america to the 3rd information which is computational expense. We must ever see if the GPU will beryllium capable to grip the task we are giving it wrong the constraints of its hardware. Since the NVIDIA H200 has importantly larger memory, 141 GB to the NVIDIA H100’s 80 GB, it is ever the amended action erstwhile taking connected the largest models. For example, an 8xH100 setup could not tally DeepSeek-R1 while a 8xH200 setup could.

In summary, these 3 considerations springiness america a model pinch which we tin prime the champion GPU for our usage case. While some machines person their strengths and weaknesses, we almost ever urge the NVIDIA H200 for AI conclusion and training tasks because of its higher capacity capabilities. This is unless costs is the astir important consideration, successful which lawsuit the H100 whitethorn beryllium a suitable alternative.

Closing Thoughts

The NVIDIA H200 is an incredibly potent GPU for AI training and inference, and is simply a notable upgrade to the NVIDIA H100. We urge utilizing it for each Deep Learning related tasks, and its evident that it is already playing a awesome domiciled successful the ongoing AI revolution.

Try the NVIDIA H200 connected DigitalOcean’s Bare Metal GPU’s today! Fill retired the shape here for much information.