# NVIDIA DGX Servers

### <mark style="color:purple;">**NVIDIA DGX-1—First Generation DGX Server**</mark>

In 2017, NVIDIA introduced the DGX-1, a pioneering server designed to accelerate AI and deep learning applications.&#x20;

This product launch marked a significant <mark style="color:yellow;">**strategic pivot for NVIDIA**</mark> from being primarily a GPU provider to offering comprehensive AI computational systems.  The cost for the system was $US149,000.

It was Nvidia's <mark style="color:yellow;">**strategic move into becoming a system vendor.**</mark>

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.&#x20;

At the time Nvidia’s strategy was though to potentially strain relationships with other [OEMs and ODMs](#user-content-fn-1)[^1] 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.

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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.

### <mark style="color:purple;">**Strategic Impact**</mark>

#### <mark style="color:green;">**Market Differentiation**</mark>

By *<mark style="color:yellow;">integrating hardware with tailored software and services</mark>*, NVIDIA positioned the DGX-1 not just as a product but as a holistic AI solution, distinguishing itself from competitors who offered only components.

#### <mark style="color:green;">**System Vendor Transformation**</mark>

Transitioning into a system vendor, NVIDIA began to control a <mark style="color:yellow;">**full-stack solution**</mark> (hardware + software + support) enhancing customer lock-in and increasing the barriers to entry for competitors.

#### <mark style="color:green;">**Innovation Leadership**</mark>

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.

#### <mark style="color:green;">**Customer Base Expansion**</mark>

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.

### <mark style="color:purple;">**Economic Impact**</mark>

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.

### <mark style="color:purple;">Table of Specifications and Performance Metrics</mark>

| **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                      |

### <mark style="color:purple;">Practical Usage and Impact</mark>

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.&#x20;

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.

[^1]: OEMs design and manufacture products that are sold under another company's brand name, while ODMs manufacture products based on the designs and specifications provided by another company. OBM companies design, produce, and sell products under their own brand name.