Setting Up an NVIDIA H100 Server : Components, Scaling & PCIe Gen5 Importance

The NVIDIA H100 GPU is a high-performance chip based on the Hopper architecture. It is designed to deliver outstanding performance, security, and scalability for every HPC (high-performance computing) and AI workload. This CPU is a significant component in servers, which plays a major role in inference, deep learning training & HPC tasks. NVIDIA offers various server configurations, including the DGX H100 and HGX H100, which utilize multiple H100 GPUs for demanding applications. Building an AI server using NVIDIA H100 GPUs requires careful planning from hardware selection to software optimization. This article elaborates on setting up the NVIDIA H100 server, its working, and its applications.

What is an NVIDIA H100 Server?

An NVIDIA H100 server system is a powerful computing platform designed to be used in HPC or high-performance computing and highly demanding AI workloads. This server system uses a high-end, specialized GPU like the NVIDIA H100 Tensor Core GPU with the Hopper architecture. It creates a powerful platform mainly for challenging computational tasks, delivers improvements in the major performance of different tasks like large language models, deep learning training, AI training & inference, computationally intensive tasks, scientific computing, and many more.

A server platform merges several H100 GPUs, including high-speed interconnects, to form a powerful computing node. In addition, this server system supports NVMe drives for improved performance. Many other server manufacturers provide H100-based servers like Silicon Mechanics, Thinkmate, etc.

How Does the NVIDIA H100 Server Work?

The NVIDIA H100 GPU in AI servers plays a significant role by increasing the speed of computation for both AI model training & inference. This GPU has advanced architecture with fourth-generation Tensor Cores and Transformer Engines, which accelerate the process of training complex AI models in real-time to run those models.

In addition, this GPU includes high-speed interconnects like PCIe Gen5 and NVLink, which allow quick training & inference for other AI tasks and large language models. So this results in quick development cycles, enhanced performance, and is capability to handle more complicated AI applications.

Components

The required hardware components to build an NVIDIA H100-based AI server include: the NVIDIA H100 GPU, CPU, RAM, Storage, Motherboard & PCIe lanes, PSU, and cooling system, which are explained below.

NVIDIA H100 GPUs

The NVIDIA H100 GPU provides up to 30x higher performance as compared to its predecessor to make it perfect for AI training & inference. Select a suitable number of GPUs depending on the requirements of your workload.

CPU

A high-performance CPU for an AI server is required to control data pre-processing & overall system coordination. AMD EPYC and Intel Xeon processors are commonly used in AI servers.

RAM

AI training models need huge amounts of memory, like 256GB RAM is suggested, but large datasets-based workloads may require 512GB or more.

Storage

Choose high-speed NVMe SSDs with a minimum of 4TB storage. Consider adding an external storage or cloud storage system solution if it works with huge datasets.

Motherboard & PCIe Lanes

Make sure that the motherboard supports several PCIe Gen 5 slots to contain the H100 GPUs & allow for the best bandwidth.

PSU or Power Supply Unit

Each H100 GPU has around 350W of power draw. If it uses several GPUs, then the suggested power is a 2000W+ PSU.

Cooling System

Usually, H100 GPUs produce significant heat, so a liquid cooling & high-performance fan combination is necessary to maintain the best performance.

Setting up an NVIDIA H100 Server

NVIDIA H100 GPU installation in a server needs careful attention for compatibility of hardware, careful handling, and power requirements to ensure sure best performance and long life. The step-by-step, detailed guide will help you through the procedure to ensure a successful NVIDIA H100 GPU installation on your server. So it allows high-performance computing for machine learning, AI & other demanding workloads.

Server Compatibility Verification

Before installing the GPU within a server, verify that your server meets the requirements of the GPU. The H100 GPU is well-matched with PCIe 5.0 and 4.0 slots. Make sure the motherboard of your server supports a minimum of PCIe 4.0 x16 for complete performance.

The GPU is available in various form factors like SXM and PCIe. For PCIe versions, check that your server chassis has enough clearance for the GPU’s dimensions. The H100 PCIe version needs enough power delivery, like a minimum of 350W for each GPU. So your PSU must have the required PCIe power connectors like 8-pin/12VHPWR.

Set up the Server

The following steps must be followed to set up your server, mainly for the installation of the GPU.

• First, the server must be installed, and all power cables. Utilize an anti-static wrist strap to avoid ESD (electrostatic discharge) damage to the components or the GPU.
• After that, identify an obtainable PCIe x16 slot to meet the requirements of the GPU.
• Separate any brackets or blanking plates covering the PCIe slot if required.

Install the GPU

• Insert the NVIDIA H100 GPU carefully into the server:
• Position the H100 GPU by connecting its connector to the PCIe slot.
• After that, lightly push the GPU into the slot until it connects into place. Utilize the screw or retention bracket to save the GPU to the frame.
• Connect the necessary PCIe power cables to the GPU from the PSU. Make sure that all connections are compact.

Configure the Server System

• Configure the server to identify the H100 GPU once the physical installation is done.
• Turn ON the server by reconnecting the power supply.
• Download & install the newest NVIDIA data center drivers from the official website of NVIDIA.
• Utilize the Nvidia-semi command in the Linux terminal or Windows (Device Manager) to verify whether the GPU is recognized or not.
• Make sure firmware updates for the NVIDIA H100 GPU to check compatibility & performance optimizations.

How to Scale an NVIDIA H100 Server?

It is significant to scale the NVIDIA H100 server very efficiently by optimizing the densities of GPUs for complete usage of resources & superior output while running ML and AI loads. So the steps involved in scaling a server effectively are discussed below.

• First, you should know what your ML or AI tasks need. Consider things like memory needs, parallelism & computation intensity. So this informs your approach to expanding GPUs.
• The H100 GPU’s MIG feature is used to separate every GPU into up to seven separate partitions.
• These instances can be customized to meet varied tasks or user requirements, thereby increasing the overall usage efficiency of every individual GPU involved.
• High-density graphics card design-based areas can generate extreme heat. Innovative cooling techniques must be used to keep temperatures stable to secure sustained performance & reliability.
• As a fraction of designing your infrastructure, incorporate scalability features. Select a server architecture that allows for the simple integration of extra GPUs, otherwise other hardware components.
• By doing this, you can save some time and money due to computational expansion in future scaling.

PCIe Gen5 Importance to Maximize the Performance

PCIe Gen5 enhances the performance of the NVIDIA H100 GPU server by allowing this important element in the following ways.

• Quick communication in an SoC and among two chips mounted on an MCM (multichip module). PCIe Gen 5.0 increases the transfer rate of PCIe Gen 4 by enhancing communication among H100 GPUs & other system parts. It is essential for AI/ML applications that need high-speed data transfer & processing.
• Less latency will decrease the time spent delivering data & receiving responses from remote-access clients. Thus, it increases the overall data-intensive tasks like real-time AI applications that run on H100 GPU servers.
• The bandwidth increase will allow a number of channels for data flow and faster connections. It is perfect for activities with large amounts of data, such as training complex AI models. By using PCIe Gen5, you can make sure your server infrastructure is prepared for future advancements in GPU technology. It protects your investment and allows easy transitions to future-generation GPUs.

By understanding & applying the above principles, any organization can significantly improve the efficiency and performance of their NVIDIA H100 GPU server, therefore driving forward AI innovation at a speed-up pace.

How to Choose the Right Form Factor for NVIDIA H100 GPU Server?

Choosing the correct form factor is significant for the NVIDIA H100 GPU server to improve the performance of your HPC and AI workloads. So the following factors must be considered while selecting the form factor.

Space Limits

Rack servers are designed for simple scalability, which can fit into consistent data center configurations, making them more appropriate for organizations with space limits.

Cooling Capacity

For example, the thermal design of a server is significant, mainly for high-end NVIDIA H100 GPUs; blade servers may provide optimized cooling solutions within dense configurations.

Expansion Requirements

Consider whether the NIVIDIA server allows you in the future to include additional GPUs or hardware. For instance, generally, tower servers provide more space for physical expansion because of their larger frame.

Custom Configuration Options through Online System Configurations

These configurations include different custom configuration options, mainly for NVIDIA H100 servers that meet specific performance and workload requirements. So the major configurable parameters are discussed below.

Selection of CPU

The CPU is selected based on whether the workload is extra CPU-intensive or needs higher parallel processing capabilities to balance between clock speed & core count.

Configuration of Memory

Strike a balance between speed and capacity by correcting the type and amount of RAM based on your particular computational necessities.

Storage Options

The trade-off between speed, cost, and storage capacity should be considered when choosing hybrid, HDD, and SSD configurations.

Networking Hardware

Bandwidth requirements and latency sensitivity must be considered when deciding NIC (network interface card) options.

Power Supply Units

Use very energy-efficient types of PSUs since the power consumption of the Nvidia H100 server is quite high.

Cooling Solutions

Choose suitable cooling solutions depending on the deployment surroundings to maintain the best thermal performance levels.

 Thereby the  above-mentioned parameters must be taken into account to arrange Nvidia’s H100 servers properly. In this way, businesses can change their systems to attain the necessary trade-off between efficiency, cost-effectiveness, and performance.

Thus, this is an overview of the H100 GPU server system that includes a minimum of one or above H100 GPUs, frequently next to other components like CPUs and high-speed interconnects. It is a main component within servers that is used for inference, HPC tasks, and deep learning training. Thus, it makes a great platform for challenging computational tasks. Here is a question for you: What is NVIDIA GB200?