NVIDIA DGX Servers

NVIDIA DGX-1—First Generation DGX Server

In 2017, NVIDIA introduced the DGX-1, a pioneering server designed to accelerate AI and deep learning applications.

This product launch marked a significant strategic pivot for NVIDIA from being primarily a GPU provider to offering comprehensive AI computational systems. The cost for the system was $US149,000.

It was Nvidia's strategic move into becoming a system vendor.

This transition was not just about selling hardware but involved a comprehensive approach including software and services to facilitate the rapid adoption and deployment of AI and deep learning technologies.

It integrated GPU technology and a comprehensive suite of software to provide out-of-the-box functionality for high-performance AI applications.

At the time Nvidia’s strategy was though to potentially strain relationships with other as it competes directly with them. However, Nvidia’s specialised offerings in AI and deep learning give it a competitive edge that is hard to match.

At the time the NVIDIA DGX-1 offers up to three times faster training speed compared to other GPU-based systems, reducing the time required for deep learning tasks.

Strategic Impact

Market Differentiation

By integrating hardware with tailored software and services, NVIDIA positioned the DGX-1 not just as a product but as a holistic AI solution, distinguishing itself from competitors who offered only components.

System Vendor Transformation

Transitioning into a system vendor, NVIDIA began to control a full-stack solution (hardware + software + support) enhancing customer lock-in and increasing the barriers to entry for competitors.

Innovation Leadership

The DGX-1 underscored NVIDIA's role as an innovator, especially with its early adoption and integration of the new Volta GPU architecture, reinforcing its brand as a leader in AI acceleration technologies.

Customer Base Expansion

NVIDIA targeted a wider range of data-centric businesses, extending beyond its traditional gaming and graphics markets. This shift helped capture new growth opportunities in sectors heavily investing in AI and machine learning.

Economic Impact

The DGX-1's release was strategically timed to capitalise on the growing demand for deep learning and complex AI model training capabilities. Its introduction significantly altered the economic landscape for companies engaging in AI research and development:

1. Cost Savings: Organisations saved on legacy hardware and reduced the need for extensive DIY setups, which were often costly and less efficient.

2. Efficiency Gains: The DGX-1 reduced deep learning model training time by days, directly translating to faster time-to-market for AI-driven products and services.

3. Revenue Impact: Accelerated product development enabled by the DGX-1's processing power led to quicker realisation of revenue from new AI enhancements and solutions.

4. Operational Efficiency: The integration of DGX-1 into enterprises allowed for smoother operations with fewer interruptions, thanks to its reliable performance and NVIDIA’s comprehensive support structure.

Table of Specifications and Performance Metrics

Feature	Detail
System Model	Nvidia DGX-1 with Volta V100 GPUs
Base Price	Approx. $US149,000 for the Volta-based system
GPUs	8x Nvidia Tesla V100
Performance	Up to 1 petaFLOPS in GPU FP16 performance
CPU	Dual 20-core Intel Xeon E5-2698 v4
System Memory	512 GB DDR4 LRDIMM
Storage	4x 1.92 TB SSD in RAID 0 configuration
Networking	Dual 10 GbE, Quad InfiniBand EDR
Software Stack	Includes NVIDIA DIGITS, CUDA toolkit, deep learning SDK, and NVIDIA Docker
Cooling	Advanced cooling solutions to manage high-performance heat output
Usage	AI research and development, deep learning training, and machine learning
Added Benefits	Tensor Core architecture for advanced AI computations

Practical Usage and Impact

The DGX-1, especially with its Volta upgrade, was designed for intensive computational tasks like training complex neural networks, conducting scientific research, and running large-scale simulations.

The systems was optimised to reduce the time required from initiating an AI project to obtaining actionable insights, dramatically speeding up the data processing and model training phases.