What Is NVIDIA HGX?

The NVIDIA HGX A100 is a computing platform, featuring the new generation of A100 80GB GPUs. A single HGX A100 now offers up to 1.3 terabytes (TB) of GPU memory. It is designed to accelerate artificial intelligence (AI) and high-performance computing (HPC) workloads.

The NVIDIA HGX platform is a modular and scalable system that integrates the latest GPU acceleration technologies. This system provides high performance, efficiency, and flexibility to power the most demanding AI and HPC applications. It leverages the power of state of the art GPUs, providing the computational resources to handle large-scale data processing tasks.

What Is NVIDIA DGX?

NVIDIA DGX is a series of powerful AI supercomputers designed for enterprise-level applications. DGX systems provide a massive amount of computing power—between 1-5 PetaFLOPS—in one device. It is an end-to-end, fully-integrated, ready-to-use system that combines NVIDIA's most advanced GPU technology, comprehensive software, and state-of-the-art hardware.

The NVIDIA DGX system is built to deliver massive, highly scalable AI performance. With its built-in AI software stack and powerful computational capabilities, the NVIDIA DGX system simplifies the process of building, training, and deploying AI models at virtually any scale.

This is part of a series of articles about NVIDIA A100.

In this article:

• NVIDIA HGX vs. DGX: 5 Key Differences
• ~1. Hardware Configuration
• ~2. Software Stack and Integration
• ~3. Customizability
• ~4. Target Users and Applications
• ~5. Cost
• NVIDIA HGX Use Cases
• ~Cloud Data Centers
• ~High-Performance Computing (HPC)
• ~AI Research and Development at Scale
• ~Customizable Infrastructure
• NVIDIA DGX Use Cases
• ~AI and Deep Learning Development
• ~Edge Computing
• ~Healthcare and Medical Research
• ~Content Creation and Media
• How to Choose Between NVIDIA HGX and DGX?
• NVIDIA HGX vs DGX with Run:ai

NVIDIA HGX vs. DGX: 5 Key Differences

1. Hardware Configuration

When it comes to hardware configuration, both NVIDIA HGX and DGX use NVIDIA's advanced GPU technology. However, they differ significantly in their design and hardware setup. NVIDIA HGX is a scalable and modular platform that integrates multiple NVIDIA GPUs, allowing users to customize the system based on their computational needs.

On the other hand, NVIDIA DGX comes as a fully-integrated system with fixed hardware configuration. It combines multiple NVIDIA GPUs, high-speed NVLink interconnects, and a high-performance CPU in a single system, delivering exceptional computational power for demanding AI and deep learning applications.

2. Software Stack and Integration

In terms of software, both NVIDIA HGX and DGX come with a comprehensive AI software stack. However, the DGX’s software stack is more user-friendly and well-integrated, making it a perfect fit for enterprises. It comes with a fully-optimized software suite that includes everything from operating system and drivers to libraries and frameworks, all designed to facilitate AI and deep learning development.

NVIDIA HGX, while offering a similar software stack, is more suited to researchers and developers who desire more control over their software environment. It is designed to be flexible and customizable, allowing users to tailor the software stack to their specific needs.

3. Customizability

NVIDIA HGX is designed to be highly customizable, offering a modular architecture that allows users to add or remove GPUs based on their computational needs. This flexibility makes the HGX ideal for a wide range of high-performance computing tasks, from cloud data centers to AI research and development at scale.

On the contrary, NVIDIA DGX, while delivering exceptional performance, is not as customizable as the HGX. Its hardware configuration is fixed, and it's designed to deliver maximum performance out of the box. However, this is compensated by its superior performance, ease of use, and the ability to easily scale DGX systems by combining them into SuperPODs.

Learn more in our detailed guide to NVIDIA SuperPOD (coming soon)

4. Target Users and Applications

NVIDIA HGX and DGX are designed for different target users and applications. The HGX is primarily aimed at researchers and developers who require a flexible and scalable platform for their high-performance computing needs. It is suitable for applications such as cloud data centers, high-performance computing, AI research and development at scale, and customizable infrastructure.

On the other hand, NVIDIA DGX is designed for enterprises that require a powerful, ready-to-use AI solution. It's ideal for applications like AI and deep learning development, edge computing, healthcare and medical research, and content creation and media.

5. Cost

The HGX, due to its modular nature, has a flexible pricing model that depends on the configuration chosen by the user. It's also widely available to researchers and developers through NVIDIA's extensive network of partners.

On the contrary, NVIDIA DGX is an all-in-one, high-end solution that comes with a hefty price tag. It's available directly from NVIDIA or through its select network of partners. Despite its high cost, the DGX offers excellent value for money, given its powerful hardware, comprehensive software stack, and exceptional performance.

NVIDIA HGX Use Cases

Cloud Data Centers

The NVIDIA HGX platform is an ideal solution for cloud data centers due to its customizability and high performance. It can handle large-scale data processing tasks, making it perfect for cloud service providers who need to manage vast amounts of data efficiently.

High-Performance Computing (HPC)

NVIDIA HGX is also well-suited for high-performance computing tasks. Its powerful GPU acceleration technologies and scalable design make it a reliable solution for demanding computational tasks, such as climate modeling, bioinformatics, quantum chemistry, and more.

AI Research and Development at Scale

For AI researchers and developers, the NVIDIA HGX offers a flexible and powerful platform. It provides the necessary computational resources to develop, train, and deploy complex AI models, making it a valuable tool for AI research and development at scale.

Customizable Infrastructure

Lastly, the NVIDIA HGX's modular architecture allows for a customizable infrastructure. It enables users to scale their system based on their computational needs, making it a versatile solution for a variety of advanced computing applications.

NVIDIA DGX Use Cases

AI and Deep Learning Development

The NVIDIA DGX system is specifically designed for AI and deep learning development. Its powerful hardware, comprehensive software stack, and ease of use make it an excellent tool for organizations undergoing AI transformation.

Edge Computing

With its robust computational capabilities, the NVIDIA DGX system can handle the demands of edge computing. It can process large amounts of data at the edge of the network, reducing latency and improving the performance of applications.

Healthcare and Medical Research

In the field of healthcare and medical research, the NVIDIA DGX can accelerate the development of advanced AI models. Its high performance and user-friendly software stack make it an ideal solution for medical researchers looking to leverage AI.

Content Creation and Media

Lastly, the NVIDIA DGX is a powerful tool for content creation and media. Its high-performance GPUs and comprehensive software stack allow for the creation of high-quality digital content, from 3D modeling and animation to visual effects and video editing.

How to Choose Between NVIDIA HGX and DGX?

Making the decision between NVIDIA's HGX and DGX platforms largely depends on the specific requirements of the user or organization. Here are some key considerations to help guide your choice:

Purpose and application

• If you're an enterprise focusing on rapid AI development and deployment, NVIDIA DGX might be a better choice because of its all-in-one, ready-to-use system.
• For organizations and researchers who require flexibility, scalability, and a platform tailored to their specific needs, the HGX might be the more appropriate choice.

Customizability

• If you need a system that allows for easy customization and scalability in terms of hardware, the modular architecture of HGX will serve you better.
• If you're looking for an out-of-the-box solution without the need for customization, DGX might be a better option.

Budget

• The HGX, due to its modular nature, might provide more flexibility in terms of budget, allowing users to scale and invest based on their needs.
• While the DGX might come with a higher upfront cost, its comprehensive integration of software, hardware, and support offers an excellent value proposition, especially for enterprises.

Ease of use

• Organizations that want a seamless experience without the intricacies of setting up their environments might lean towards the DGX, given its well-integrated software stack.
• Those who want control over their software environment might prefer the HGX platform.

Support and partner network

Consider the availability of support and the network of partners when making a choice. While both platforms have robust support, your geographic location and access to NVIDIA's network of partners might influence the decision.

NVIDIA HGX vs DGX with Run:ai

Run:ai automates resource management and orchestration for machine learning infrastructure, including DGX servers and workstations. With Run:ai, you can automatically run as many compute intensive experiments as needed.

Here are some of the capabilities you gain when using Run:ai:

• Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
• No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
• A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
  

Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.

‍Learn more about the Run:ai GPU virtualization platform