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43-400: The Nvidia DGX (Deep GPU Xceleration) represents 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 featuring high-performance x86 server CPUs on the motherboard. The core feature of

86-1107: A computer monitor or input device, audio hardware and USB interfaces. Many servers do not have a graphical user interface (GUI). They are configured and managed remotely. Remote management can be conducted via various methods including Microsoft Management Console (MMC), PowerShell , SSH and browser-based out-of-band management systems such as Dell's iDRAC or HP's iLo . Large traditional single servers would need to be run for long periods without interruption. Availability would have to be very high, making hardware reliability and durability extremely important. Mission-critical enterprise servers would be very fault tolerant and use specialized hardware with low failure rates in order to maximize uptime . Uninterruptible power supplies might be incorporated to guard against power failure. Servers typically include hardware redundancy such as dual power supplies , RAID disk systems, and ECC memory , along with extensive pre-boot memory testing and verification. Critical components might be hot swappable , allowing technicians to replace them on

129-418: A 72-GPU NVLink domain that acts as a single massive GPU [3] . Nvidia DGX GB200 offers 13.5 TB HBM3e of shared memory with linear scalability for giant AI models, less than its predecessor DGX GH200. The DGX Superpod is a high performance turnkey supercomputer solution provided by Nvidia using DGX hardware. This system combines DGX compute nodes with fast storage and high bandwidth networking to provide

172-640: 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. This framework makes DGX units suitable for computational tasks associated with artificial intelligence and machine learning models. DGX-1 servers feature 8 GPUs based on

215-571: 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. The successor of the Nvidia DGX-1 is the Nvidia DGX-2, which uses sixteen Volta -based V100 32 GB (second generation) cards in

258-403: A computer program that turns a computer into a server, e.g. Windows service . Originally used as "servers serve users" (and "users use servers"), in the sense of "obey", today one often says that "servers serve data", in the same sense as "give". For instance, web servers "serve [up] web pages to users" or "service their requests". The server is part of the client–server model ; in this model,

301-415: A device used for (or a device dedicated to) running one or several server programs. On a network, such a device is called a host . In addition to server , the words serve and service (as verb and as noun respectively) are frequently used, though servicer and servant are not. The word service (noun) may refer to the abstract form of functionality, e.g. Web service . Alternatively, it may refer to

344-497: 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

387-411: A server serves data for clients . The nature of communication between a client and server is request and response . This is in contrast with peer-to-peer model in which the relationship is on-demand reciprocation. In principle, any computerized process that can be used or called by another process (particularly remotely, particularly to share a resource) is a server, and the calling process or processes

430-423: A service for the requester, which often runs on a computer other than the one on which the server runs. The average utilization of a server in the early 2000s was 5 to 15%, but with the adoption of virtualization this figure started to increase to reduce the number of servers needed. Strictly speaking, the term server refers to a computer program or process (running program). Through metonymy , it refers to

473-407: A single unit. It was announced on 27 March in 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 Gb/sec InfiniBand cards and 30.72 TB of SSD storage, all enclosed within

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516-576: A solution to high demand machine learning workloads. The Selene Supercomputer , at the Argonne National Laboratory , is one example of a DGX SuperPod based system. Selene, built from 280 DGX A100 nodes, ranked 5th on the Top500 list for most powerful supercomputers at the time of its completion, and has continued to remain high in performance. This same integration is available to any customer with minimal effort on their behalf, and

559-406: Is a client. Thus any general-purpose computer connected to a network can host servers. For example, if files on a device are shared by some process, that process is a file server . Similarly, web server software can run on any capable computer, and so a laptop or a personal computer can host a web server. While request–response is the most common client-server design, there are others, such as

602-618: Is a collaborative effort, Open Compute Project around this concept. A class of small specialist servers called network appliances are generally at the low end of the scale, often being smaller than common desktop computers. A mobile server has a portable form factor, e.g. a laptop . In contrast to large data centers or rack servers, the mobile server is designed for on-the-road or ad hoc deployment into emergency, disaster or temporary environments where traditional servers are not feasible due to their power requirements, size, and deployment time. The main beneficiaries of so-called "server on

645-427: Is also less of a concern, but power consumption and heat output can be a serious issue. Server rooms are equipped with air conditioning devices. A server farm or server cluster is a collection of computer servers maintained by an organization to supply server functionality far beyond the capability of a single device. Modern data centers are now often built of very large clusters of much simpler servers, and there

688-405: Is contrasted with "user", distinguishing two types of host : "server-host" and "user-host". The use of "serving" also dates to early documents, such as RFC 4, contrasting "serving-host" with "using-host". The Jargon File defines server in the common sense of a process performing service for requests, usually remote, with the 1981 version reading: SERVER n. A kind of DAEMON which performs

731-704: The Internet is based upon a client–server model. High-level root nameservers , DNS , and routers direct the traffic on the internet. There are millions of servers connected to the Internet, running continuously throughout the world and virtually every action taken by an ordinary Internet user requires one or more interactions with one or more servers. There are exceptions that do not use dedicated servers; for example, peer-to-peer file sharing and some implementations of telephony (e.g. pre-Microsoft Skype ). Hardware requirement for servers vary widely, depending on

774-581: 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 the 6th of April in 2016. All models are based on a dual socket configuration of Intel Xeon E5 CPUs, and are equipped with the following features. 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

817-409: The publish–subscribe pattern . In the publish-subscribe pattern, clients register with a pub-sub server, subscribing to specified types of messages; this initial registration may be done by request-response. Thereafter, the pub-sub server forwards matching messages to the clients without any further requests: the server pushes messages to the client, rather than the client pulling messages from

860-514: The request–response model: a client sends a request to the server, which performs some action and sends a response back to the client, typically with a result or acknowledgment. Designating a computer as "server-class hardware" implies that it is specialized for running servers on it. This often implies that it is more powerful and reliable than standard personal computers , but alternatively, large computing clusters may be composed of many relatively simple, replaceable server components. The use of

903-574: The 5th fastest AI supercomputer in the world, according to TOP500 (November 2023 edition). As Nvidia does not produce any storage devices or systems, Nvidia SuperPods rely on partners to provide high performance storage. Current storage partners for Nvidia Superpods are Dell EMC , DDN , HPE , IBM , NetApp , Pavilion Data, and VAST Data . Comparison of accelerators used in DGX: Server (computing) Client–server systems are usually most frequently implemented by (and often identified with)

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946-630: The Ampere architecture GeForce 30 series consumer GPUs at a GeForce Special Event on September 1, 2020. Nvidia announced the A100 80 GB GPU at SC20 on November 16, 2020. Mobile RTX graphics cards and the RTX 3060 based on the Ampere architecture were revealed on January 12, 2021. Nvidia announced Ampere's successor, Hopper , at GTC 2022, and "Ampere Next Next" ( Blackwell ) for a 2024 release at GPU Technology Conference 2021. Architectural improvements of

989-482: The Ampere architecture include the following: Comparison of Compute Capability: GP100 vs GV100 vs GA100 Comparison of Precision Support Matrix Legend: Comparison of Decode Performance The Ampere-based A100 accelerator was announced and released on May 14, 2020. The A100 features 19.5 teraflops of FP32 performance, 6912 FP32/INT32 CUDA cores, 3456 FP64 CUDA cores, 40 GB of graphics memory, and 1.6 TB/s of graphics memory bandwidth. The A100 accelerator

1032-476: The DGX A100s 640GB HBM2 memory. This upgrade also increases VRAM bandwidth to 3 TB/s. The DGX H100 increases the rackmount size to 8U to accommodate the 700W TDP of each H100 SXM card. The DGX H100 also has two 1.92 TB SSDs for Operating System storage, and 30.72 TB of Solid state storage for application data. One more notable addition is the presence of two Nvidia Bluefield 3 DPUs , and

1075-473: The DGX Helios supercomputer features 4 DGX GH200 systems. Each is interconnected with Nvidia Quantum-2 InfiniBand networking to supercharge data throughput for training large AI models. Helios includes 1,024 H100 GPUs. Announced March 2024, GB200 NVL72 connects 36 Grace Neoverse V2 72-core CPUs and 72 B100 GPUs in a rack-scale design. The GB200 NVL72 is a liquid-cooled, rack-scale solution that boasts

1118-526: 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. 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 under 35 dB under load. This, among other features, made this system

1161-533: 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. Announced and released on May 14, 2020. The DGX A100

1204-847: The Internet, the dominant operating systems among servers are UNIX-like open-source distributions , such as those based on Linux and FreeBSD , with Windows Server also having a significant share. Proprietary operating systems such as z/OS and macOS Server are also deployed, but in much smaller numbers. Servers that run Linux are commonly used as Webservers or Databanks. Windows Servers are used for Networks that are made out of Windows Clients. Specialist server-oriented operating systems have traditionally had features such as: In practice, today many desktop and server operating systems share similar code bases , differing mostly in configuration. In 2010, data centers (servers, cooling, and other electrical infrastructure) were responsible for 1.1–1.5% of electrical energy consumption worldwide and 1.7–2.2% in

1247-562: 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. Announced March 22, 2022 and planned for release in Q3 2022, The DGX H100 is the 4th generation of DGX servers, built with 8 Hopper -based H100 accelerators, for a total of 32 PFLOPs of FP8 AI compute and 640 GB of HBM3 Memory, an upgrade over

1290-599: The United States. One estimate is that total energy consumption for information and communications technology saves more than 5 times its carbon footprint in the rest of the economy by increasing efficiency. Global energy consumption is increasing due to the increasing demand of data and bandwidth. Natural Resources Defense Council (NRDC) states that data centers used 91 billion kilowatt hours (kWh) electrical energy in 2013 which accounts to 3% of global electricity usage. Environmental groups have placed focus on

1333-649: 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. Designed as a turnkey deskside AI supercomputer,

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1376-513: The carbon emissions of data centers as it accounts to 200 million metric tons of carbon dioxide in a year. Ampere (microarchitecture) Ampere is the codename for a graphics processing unit (GPU) microarchitecture developed by Nvidia as the successor to both the Volta and Turing architectures. It was officially announced on May 14, 2020 and is named after French mathematician and physicist André-Marie Ampère . Nvidia announced

1419-488: 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 ~$ 9000 USD per month through partners in

1462-548: The first DGX server to not be built with an Intel Xeon CPU. The initial price for the DGX A100 Server was $ 199,000. 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

1505-411: The go" technology include network managers, software or database developers, training centers, military personnel, law enforcement, forensics, emergency relief groups, and service organizations. To facilitate portability, features such as the keyboard , display , battery ( uninterruptible power supply , to provide power redundancy in case of failure), and mouse are all integrated into the chassis. On

1548-625: The new Hopper based SuperPod can scale to 32 DGX H100 nodes, for a total of 256 H100 GPUs and 64 x86 CPUs. This gives the complete SuperPod 20 TB of HBM3 memory, 70.4 TB/s of bisection bandwidth, and up to 1 ExaFLOP of FP8 AI compute. These SuperPods can then be further joined to create larger supercomputers. Eos supercomputer , designed, built, and operated by Nvidia, was constructed of 18 H100 based SuperPods, totaling 576 DGX H100 systems, 500 Quantum-2 InfiniBand switches, and 360 NVLink Switches, that allow Eos to deliver 18 EFLOPs of FP8 compute, and 9 EFLOPs of FP16 compute, making Eos

1591-564: The running server without shutting it down, and to guard against overheating, servers might have more powerful fans or use water cooling . They will often be able to be configured, powered up and down, or rebooted remotely, using out-of-band management , typically based on IPMI . Server casings are usually flat and wide , and designed to be rack-mounted, either on 19-inch racks or on Open Racks . These types of servers are often housed in dedicated data centers . These will normally have very stable power and Internet and increased security. Noise

1634-411: The server as in request-response. The role of a server is to share data as well as to share resources and distribute work. A server computer can serve its own computer programs as well; depending on the scenario, this could be part of a quid pro quo transaction, or simply a technical possibility. The following table shows several scenarios in which a server is used. Almost the entire structure of

1677-412: The server's purpose and its software. Servers often are more powerful and expensive than the clients that connect to them. The name server is used both for the hardware and software pieces. For the hardware servers, it is usually limited to mean the high-end machines although software servers can run on a variety of hardwares. Since servers are usually accessed over a network, many run unattended without

1720-564: The upgrade to 400 Gb/s InfiniBand via Mellanox ConnectX-7 NICs , double the bandwidth of the DGX A100. The DGX H100 uses new 'Cedar Fever' cards, each with four ConnectX-7 400 GB/s controllers, and two cards per system. This gives the DGX H100 3.2 Tb/s of fabric bandwidth across Infiniband. The DGX H100 has two Xeon Platinum 8480C Scalable CPUs (Codenamed Sapphire Rapids ) and 2 Terabytes of System Memory . The DGX H100

1763-574: The word server in computing comes from queueing theory , where it dates to the mid 20th century, being notably used in Kendall (1953) (along with "service"), the paper that introduced Kendall's notation . In earlier papers, such as the Erlang (1909) , more concrete terms such as "[telephone] operators" are used. In computing, "server" dates at least to RFC 5 (1969), one of the earliest documents describing ARPANET (the predecessor of Internet ), and

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1806-669: Was priced at £379,000 or ~$ 482,000 USD at release. Announced May 2023, the DGX GH200 connects 32 Nvidia Hopper Superchips into a singular superchip, that consists totally of 256 H100 GPUs, 32 Grace Neoverse V2 72-core CPUs, 32 OSFT single-port ConnectX-7 VPI of with 400 Gb/s InfiniBand and 16 dual-port BlueField-3 VPI with 200 Gb/s of Mellanox [1] [2] . Nvidia DGX GH200 is designed to handle terabyte-class models for massive recommender systems, generative AI, and graph analytics, offering 19.5 TB of shared memory with linear scalability for giant AI models. Announced May 2023,

1849-406: 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,

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