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. • 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.
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. 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 DGX H100 Server 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 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 the upgrade to 400 Gbit/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 Tbit/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 was priced at £379,000 or ~US$482,000 at release.
DGX GH200 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 Gbit/s InfiniBand and 16 dual-port
BlueField-3 VPI with 200 Gbit/s of
Mellanox [https://resources.nvidia.com/en-us-dgx-gh200/nvidia-grace-hopper-superchip-datasheet . 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.
DGX Helios Announced May 2023, 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.
Blackwell DGX GB200 Announced March 2024, GB200 NVL72 connects 36 Grace Arm Neoverse V2 72-core CPUs and 72 B200 GPUs in a rack-scale design. The GB200 NVL72 is a liquid-cooled, rack-scale solution that boasts a 72-GPU NVLink domain that acts as a single massive GPU. Nvidia DGX GB200 offers 13.5 TB HBM3e of shared memory with linear scalability for giant AI models, less than its predecessor DGX GH200.
DGX SuperPod The DGX Superpod is a high performance turnkey
supercomputer system provided by Nvidia using DGX hardware. It combines DGX compute nodes with fast storage and high bandwidth
networking to provide 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 in June 2020, and has continued to remain high in performance. 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. 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 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.
DGX Spark In March 2025, Nvidia also announced the DGX Spark (previously DIGITS), a "desktop AI Supercomputer" based on Blackwell. These machines are targeted at AI researchers and programmers and have 128 GB of integrated RAM, making it possible to train or fine-tune fairly large models ("up to 200 billion parameters" with quantization). Several partner manufacturers also offer versions of the DGX Spark. It is available as of late 2025. == Accelerators ==