NVIDIA HGX: The Ultimate Reference Platform for AI and HPC

What Is NVIDIA HGX? More Than Just the Sum of Its Parts

The NVIDIA HGX platform is a highly integrated platform for demanding multi-GPU, AI, and HPC systems. It combines multiple NVIDIA GPUs with high-speed interconnects such as NVIDIA NVLink™ and NVSwitch™, enabling significantly more powerful GPU-to-GPU communication than traditional multi-GPU architectures that primarily rely on PCIe.

In conventional server architectures, the PCIe bus (Peripheral Component Interconnect Express) serves as the primary interface for high-performance components. However, in compute-intensive AI and HPC applications that rely on parallel processing across multiple GPUs, the shared bandwidth of this bus becomes a limiting factor. Extensive data transfers between GPUs (peer-to-peer communication) lead to high latency and bus saturation. As a result, the powerful compute units of the GPUs cannot be fully utilized because they have to wait for data – reducing the overall efficiency of the system.

This is exactly where the NVIDIA HGX platform comes into play. It is not a finished product, but rather the standardized reference architecture that serves as the foundation for the most powerful NVIDIA GPU servers for AI and HPC. In addition to HGX-based systems, Happyware offers individually configured NVIDIA servers for AI, HPC, rendering, and other GPU-accelerated workloads.

Technically speaking, NVIDIA HGX is a highly integrated server platform or baseboard and reference design that connects multiple NVIDIA GPUs via high-speed interconnects. Current HGX platforms such as HGX Rubin NVL8, HGX B300, and HGX B200 integrate eight NVIDIA SXM GPUs.

The goal of this architecture is to provide high-bandwidth communication between the GPUs so that multi-GPU workloads can be efficiently distributed across multiple accelerators.

HGX in a nutshell:

• Problem: The PCIe bus limits communication between multiple GPUs.
• Solution: HGX creates a direct, ultra-fast connection between all GPUs, allowing them to work as a single unit.
• Result: Maximum overall performance for AI and HPC.

The Key Technologies for Maximum NVIDIA HGX Performance

The outstanding performance of an HGX system is based on the perfect interaction of three key technologies developed by NVIDIA to eliminate communication bottlenecks.

1. NVIDIA GPUs: Current HGX platforms are based on NVIDIA Rubin, Blackwell Ultra, and Blackwell. HGX Rubin NVL8 integrates eight Rubin GPUs with HBM4, while HGX B300 combines eight Blackwell Ultra GPUs and HGX B200 combines eight Blackwell GPUs. H100 and H200 based on the Hopper architecture continue to be used in existing HGX systems.
2. NVIDIA NVLink™: This is a proprietary high-speed interconnect that establishes a direct point-to-point connection between GPUs. Unlike the PCIe bus, whose shared bandwidth becomes a bottleneck in parallel workloads, NVLink provides a dedicated and significantly higher data transfer rate. This dedicated connection is crucial for minimizing latency and maximizing data exchange for GPU-to-GPU communication.
3. NVIDIA NVSwitch™: NVSwitch enables high-bandwidth communication between GPUs within the HGX platform. The available interconnect bandwidth depends on the respective HGX generation. HGX B200 and HGX B300 use fifth-generation NVLink with up to 14.4 TB/s of total NVLink bandwidth. HGX Rubin NVL8 uses sixth-generation NVLink and achieves up to 28.8 TB/s of total NVLink switch bandwidth.

This trio transforms a collection of individual GPUs into a coherent, highly efficient supercomputer ready for the most demanding workloads.

The Key Difference: NVIDIA HGX vs. DGX

One of the most common questions we receive during consulting concerns the difference between HGX and DGX. Understanding this distinction is crucial to your purchasing decision.

• NVIDIA HGX is the standardized reference architecture. As a system integrator, HAPPYWARE uses this architecture as the foundation for configuring your customized HGX-based system using components from leading manufacturers such as Supermicro, GIGABYTE, Dell, and others. HGX therefore provides maximum flexibility when configuring the CPU (including AMD EPYC, for example), memory, storage, and networking components.
• NVIDIA DGX is NVIDIA's finished product. It is a turnkey, fully integrated hardware and software system supplied directly by NVIDIA. Rack-based NVIDIA DGX systems use tightly integrated NVIDIA GPU platforms and are offered as complete NVIDIA hardware and software systems. For businesses that prefer a fully integrated NVIDIA hardware and software platform, NVIDIA DGX offers a turnkey alternative to individually configured HGX systems.

NVIDIA DGX Alternatives: Customized Performance for Your Budget

In addition to turnkey NVIDIA DGX systems, HAPPYWARE offers a broad portfolio of alternative AI server solutions tailored to different budgets and performance requirements. Based on our experience with customized AI infrastructure projects, comparably designed GPU systems can, depending on configuration and requirements, offer cost advantages of approximately 30–50% compared with NVIDIA DGX systems. At the same time, they provide greater flexibility in the selection of server platform, GPUs, storage, and networking. With customized configuration and support services for every brand, we help you select the AI server solution that precisely matches your use case.

• Supermicro AI Servers: Supermicro offers current GPU server platforms for NVIDIA HGX B200 and HGX B300, as well as systems based on the Hopper generation. The platforms are designed for demanding AI training, inference, and HPC workloads and, depending on the system, enable flexible integration of networking, storage, and other components. These systems offer an excellent price-performance ratio, particularly for large AI training projects.
• Gigabyte AI Servers: GIGABYTE offers powerful GPU servers for current NVIDIA HGX platforms. One example is the GIGABYTE G4L4-SD1-LAX5 with NVIDIA HGX B200 and eight NVIDIA Blackwell GPUs. Due to their flexibility, these systems are particularly suitable for research institutions and universities.
• Dell AI Servers: With its PowerEdge XE series, Dell offers GPU-accelerated server systems for demanding AI and HPC workloads. Dell systems are regarded as highly reliable AI server alternatives, particularly in established enterprise environments.

The Evolution of Performance: From A100 to Rubin

The HGX platform continues to evolve with each GPU generation to meet the exponentially growing demands of AI.

• HGX A100: Ampere-based HGX generation that played a major role in establishing highly integrated multi-GPU systems for AI and HPC.
• HGX H100/H200: Hopper-based generation featuring Transformer Engine as well as HBM3 or HBM3e for demanding AI training, inference, and HPC workloads.
• HGX B200: Current Blackwell-based HGX platform with eight Blackwell SXM GPUs, a total of 1.4 TB of GPU memory, and fifth-generation NVLink.
• HGX B300: Blackwell Ultra platform with eight Blackwell Ultra SXM GPUs, a total of 2.1 TB of GPU memory, and fifth-generation NVLink.
• HGX Rubin NVL8: Current Rubin-based HGX generation with eight NVIDIA Rubin GPUs, sixth-generation NVLink, and up to 28.8 TB/s of NVLink switch bandwidth. The platform is designed particularly for large-scale agentic AI, reasoning, inference, training, and HPC workloads.

At HAPPYWARE, we ensure that you always have access to the latest NVIDIA technologies for future-proof investments.

Use Cases and Benefits: Who Should Invest in NVIDIA HGX Servers?

Purchasing an HGX-based system is a strategic decision for businesses and research institutions operating at the forefront of technological development.

Key benefits at a glance:

• Maximum Performance: Eliminate GPU communication bottlenecks and reduce training times from months to days.
• Linear Scalability: Start with an 8-GPU system and scale by combining multiple systems through ultra-fast networking (e.g. NVIDIA Quantum InfiniBand) into massive clusters.
• Higher Efficiency: A single optimized HGX system can perform the work of many smaller servers, reducing total cost of ownership (TCO) in terms of space, power, and administration in the data center.
• Future-Proofing: You invest in a proven industry standard supported by a vast software ecosystem (e.g. NVIDIA AI Enterprise, NVIDIA NGC).

Typical users and workloads:

• Most Demanding AI Workloads: Training, fine-tuning, and inference of LLMs, image and video models (Generative AI).
• High-Performance Computing (HPC): Complex simulations in science, molecular dynamics, weather forecasting, and finance.
• Research & Development: Researchers and scientists at universities and private laboratories who require full access to dedicated computing resources for their datasets.
• Industry 4.0 & Automotive: Development of autonomous driving systems, digital twins, and complex optimization models.

Our expert recommendation for NVIDIA HGX clusters:

• A single NVIDIA HGX server functions as a logical “super GPU” thanks to NVLink™ and NVSwitch™, offering aggregated GPU memory capacity and massively increased interconnect bandwidth. However, for exascale AI/ML/DL workloads – such as Large Language Models (LLMs) with hundreds of billions of parameters – a single node is not sufficient.
• In these cases, multiple HGX cluster nodes are deployed in rack or multi-rack architectures. High-performance network fabrics are used for communication between systems, such as NVIDIA Quantum-2 InfiniBand with up to 400 Gb/s or current Spectrum-X Ethernet infrastructures with up to 800 Gb/s. The actual achievable scaling depends, among other factors, on the workload, parallelization strategy, network architecture, and number of systems.
• For optimal efficiency, cooling, and cost savings we recommend deploying NVIDIA HGX clusters in Liquid Cooling Data Center or with Direct Liquid Cooling (DLC). This is particularly critical for high-density systems with B200, B300, and Rubin, as well as other platforms with high power density, where efficient thermal management and high energy efficiency translate directly into sustained performance and lower Total Cost of Ownership (TCO).

Practical Check: What You Need to Consider Before Deploying a Server with NVIDIA HGX

A current NVIDIA HGX system with eight high-end GPUs places high demands on power supply, cooling, and rack infrastructure. HGX B200, HGX B300, and Rubin-based systems in particular reach high power densities that must be taken into account during data center planning. Our experts at HAPPYWARE provide comprehensive advice on these critical aspects:

• Power Supply: Current NVIDIA HGX systems with eight high-end GPUs can, depending on GPU generation and server configuration, reach power consumption well into the double-digit kilowatt range. HGX B200, HGX B300, and Rubin-based systems in particular place high demands on power delivery. This requires careful planning of power distribution units (PDUs), circuit protection, and the available power per rack.
• Cooling: The extreme power density generates enormous amounts of heat. While high-performance air-cooled solutions exist, Direct Liquid Cooling (DLC) or liquid cooling is increasingly becoming the standard for HGX systems in order to cool the systems as efficiently as possible and ensure maximum performance at higher data center densities.
• Space and Weight: NVIDIA HGX servers are large and heavy systems due to their high GPU density and the required power and cooling infrastructure. The required rack height varies depending on the HGX generation, OEM platform, and cooling concept and can span several rack units. Dimensions, weight, and installation depth must therefore be taken into account when planning racks and calculating load capacity.

Your Customized NVIDIA HGX Solution – Configured by HAPPYWARE

The NVIDIA HGX platform provides the technological foundation. At HAPPYWARE, we transform this foundation into a customized, production-ready, and reliable solution precisely tailored to your requirements.

Since 1999, we have supported businesses throughout Europe with individually configured server and storage systems. Benefit from our ISO 9001-certified quality and deep technical expertise.

Our services for you:

• Expert Consulting: We analyze your workload and recommend the optimal configuration – from CPU and memory to networking.
• Custom Manufacturing: Every GPU server with NVIDIA HGX is built in-house according to your requirements and subjected to rigorous testing.
• Infrastructure Planning: We support you in planning the required power and cooling capacities.
• Comprehensive Support: With Europe-wide on-site service and warranty extensions of up to 6 years, we protect your investment over the long term.

Take the next step toward your AI future. Speak with our HPC and AI specialists today to configure your customized NVIDIA HGX solution.

Frequently Asked Questions (FAQ) About NVIDIA HGX

What is the main advantage of NVLink and NVSwitch compared with PCIe?
The main advantage is bandwidth and direct communication. NVLink provides a dedicated, extremely fast connection between GPUs. NVSwitch enables all GPUs to communicate with one another simultaneously, avoiding the communication congestion that occurs when PCIe lanes are shared. The result is accelerated application performance.

Which current NVIDIA HGX generations does HAPPYWARE offer?
HAPPYWARE offers HGX-based server systems from various generations and manufacturers. Depending on availability and project requirements, these include systems with NVIDIA H100/H200, HGX B200, and HGX B300, as well as new Rubin-based platforms. Our experts advise you on selecting the right generation for training, inference, HPC, and other GPU-accelerated workloads.

Can I get an HGX system with AMD EPYC™ CPUs?
Yes, this is one of the major advantages of the flexible HGX platform. We can configure your system with either the latest Intel® Xeon® processors or powerful AMD EPYC™ CPUs, depending on which architecture is best suited to your specific use case.

Is an HGX system better for Deep Learning than a standard GPU server?
For training large Deep Learning models, particularly Large Language Models, an HGX system is significantly superior. The high interconnect bandwidth is crucial when the model and datasets need to be distributed across multiple GPUs, which is the case with almost all modern, complex models.