computer science

Nvidia DGX

Nvidia DGX is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Nvidia DGX rather than just read about it. In short: The Nvidia DGX (Deep GPU Xceleration) is a series of servers and workstations designed by Nvidia, primarily geared towards enhancing deep learning applications through the use of general-purpose computing on graphics processing units (GPGPU). These systems typically come in a rackmount format, initially using high-performance x86 server CPUs, switching to ARMs around 2018, and releasing NUCs in 2025.

Key takeaways

Nvidia DGX belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Nvidia DGX to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Nvidia DGX from memory before moving on to harder problems.

Reference excerpt

The Nvidia DGX (Deep GPU Xceleration) is a series of servers and workstations designed by Nvidia, primarily geared towards enhancing deep learning applications through the use of general-purpose computing on graphics processing units (GPGPU). These systems typically come in a rackmount format, initially using high-performance x86 server CPUs, switching to ARMs around 2018, and releasing NUCs in 2025. The core feature of a DGX system is its inclusion of 4 to 8 Nvidia Tesla GPU modules, which are housed on an independent system board. These GPUs can be connected either via a version of the SXM socket or a PCIe x16 slot, facilitating flexible integration within the system architecture. To manage the substantial thermal output, DGX units are equipped with heatsinks and fans designed to maintain optimal operating temperatures. Nvidia GPGPUs are featured in TOP500 supercomputers.

Models

Pascal - Volta

DGX-1 DGX-1 servers feature 8 GPUs based on the Pascal or Volta daughter cards with 128 GB of total HBM2 memory, connected by an NVLink mesh network. The DGX-1 was announced on 6 April 2016. All models are based on a dual socket configuration of Intel Xeon E5 CPUs, and are equipped with the following features.

512 GB of DDR4-2133 Dual 10 Gb networking 4 x 1.92 TB SSDs 3200W of combined power supply capability 3U Rackmount Chassis The product line is intended to bridge the gap between GPUs and AI accelerators using specific features for deep learning workloads. The initial Pascal-based DGX-1 delivered 170 teraflops of half precision processing, while the Volta-based upgrade increased this to 960 teraflops. The DGX-1 was first available in only the Pascal-based configuration, with the first generation SXM socket. The later revision of the DGX-1 offered support for first generation Volta cards via the SXM-2 socket. Nvidia offered upgrade kits that allowed users with a Pascal-based DGX-1 to upgrade to a Volta-based DGX-1.

The Pascal-based DGX-1 has two variants, one with a 16 core Intel Xeon E5-2698 V3, and one with a 20 core E5-2698 V4. Pricing for the variant equipped with an E5-2698 V4 is unavailable, the Pascal-based DGX-1 with an E5-2698 V3 was priced at launch at $129,000 The Volta-based DGX-1 is equipped with an E5-2698 V4 and was priced at launch at $149,000.

DGX Station Designed as a turnkey deskside AI supercomputer, the DGX Station is a tower computer that can function completely independently without typical datacenter infrastructure such as cooling, redundant power, or 19 inch racks. The DGX station was first available with the following specifications.

Four Volta-based Tesla V100 accelerators, each with 16 GB of HBM2 memory 480 TFLOPS FP16 Single Intel Xeon E5-2698 v4 256 GB DDR4 4x 1.92 TB SSDs Dual 10 Gb Ethernet The DGX station is water-cooled to better manage the heat of almost 1500W of total system components, this allows it to keep a noise range below 35 dB under load. This, among other features, made this system a compelling purchase for customers without the infrastructure to run rackmount DGX systems, which can be loud, output a lot of heat, and take up a large area. This was Nvidia's first venture into bringing high performance computing deskside, which has since remained a prominent marketing strategy for Nvidia.

DGX-2 The Nvidia DGX-2, the successor to the DGX-1, uses sixteen Volta-based V100 32 GB (second generation) cards in a single unit. It was announced on 27 March 2018. The DGX-2 delivers 2 Petaflops with 512 GB of shared memory for tackling massive datasets and uses NVSwitch for high-bandwidth internal communication. DGX-2 has a total of 512 GB of HBM2 memory, a total of 1.5 TB of DDR4. Also present are eight 100 Gbit/s InfiniBand cards and 30.72 TB of SSD storage, all enclosed within a massive 10U rackmount chassis and drawing up to 10 kW under maximum load. The initial price for the DGX-2 was $399,000. The DGX-2 differs from other DGX models in that it contains two separate GPU daughterboards, each with eight GPUs. These boards are connected by an NVSwitch system that allows for full bandwidth communication across all GPUs in the system, without additional latency between boards. A higher performance variant of the DGX-2, the DGX-2H, was offered as well. The DGX-2H replaced the DGX-2's dual Intel Xeon Platinum 8168's with upgraded dual Intel Xeon Platinum 8174's. This upgrade does not increase core count per system, as both CPUs are 24 cores, nor does it enable any new functions of the system, but it does increase the base frequency of the CPUs from 2.7 GHz to 3.1 GHz.

Ampere

DGX A100 Server Announced and released on May 14, 2020. The DGX A100 was the 3rd generation of DGX server, including 8 Ampere-based A100 accelerators. Also included is 15 TB of PCIe gen 4 NVMe storage, 1 TB of RAM, and eight Mellanox-powered 200 GB/s HDR InfiniBand ConnectX-6 NICs. The DGX A100 is in a much smaller enclosure than its predecessor, the DGX-2, taking up only 6 Rack units. The DGX A100 also moved to a 64 core AMD EPYC 7742 CPU, the first DGX server to not be built with an Intel Xeon CPU. The initial price for the DGX A100 Server was $199,000.

DGX Station A100 As the successor to the original DGX Station, the DGX Station A100, aims to fill the same niche as the DGX station in being a quiet, efficient, turnkey cluster-in-a-box solution that can be purchased, leased, or rented by smaller companies or individuals who want to utilize machine learning. It follows many of the design choices of the original DGX station, such as the tower orientation, single socket CPU mainboard, a new refrigerant-based cooling system, and a reduced number of accelerators compared to the corresponding rackmount DGX A100 of the same generation. The price for the DGX Station A100 320G is $149,000 and $99,000 for the 160G model, Nvidia also offers Station rental at ~US$9000 per month through partners in the US (rentacomputer.com) and Europe (iRent IT Systems) to help reduce the costs of implementing these systems at a small scale. The DGX Station A100 comes with two different configurations of the built in A100.

Four Ampere-based A100 accelerators, configured with 40 GB (HBM) or 80 GB (HBM2e) memory,thus giving a total of 160 GB or 320 GB resulting either in DGX Station A100 variants 160G or 320G. 2.5 PFLOPS FP16 Single 64 Core AMD EPYC 7742 512 GB DDR4 1 x 1.92 TB NVMe OS drive 1 x 7.68 TB U.2 NVMe Drive Dual port 10 Gb Ethernet Single port 1 Gb BMC port

Hopper

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with Nvidia DGX

Start with the simplest possible case. Write down what Nvidia DGX claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Nvidia DGX before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Nvidia DGX ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Nvidia DGX

In research: Nvidia DGX appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Nvidia DGX in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Nvidia DGX is common in secondary-school and first-year university syllabi. It links to neighbouring topics GPGPU, Neural processing units, Nvidia products, so understanding it makes those chapters shorter.
In everyday life: Look for Nvidia DGX outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Nvidia DGX in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Nvidia DGX means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Nvidia DGX out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Nvidia DGX in simple terms?

Why does Nvidia DGX matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Nvidia DGX?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Nvidia DGX.