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Nvidia DGX

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A rack containing five DGX-1 supercomputers

Nvidia DGX is a line of Nvidia-produced servers and workstations which specialize in using GPGPU to accelerate deep learning applications.[1] The typical design of a DGX system is based upon a rackmount chassis with motherboard that carries high performance x86 server CPUs (Typically Intel Xeons, with the exception DGX A100 and DGX Station A100, which both utilize AMD EPYC CPUs).[2] The main component of a DGX system is a set of 4 to 16 Nvidia Tesla GPU modules on an independent system board. DGX systems have large heatsinks and powerful fans to adequately cool thousands of watts of thermal output. The GPU modules are typically integrated into the system using a version of the SXM socket or by a PCIe x16 slot.

Models

Pascal - Volta

DGX-1

DGX-1 servers feature 8 GPUs based on the Pascal or Volta daughter cards[3] with 128GB of total HBM2 memory, connected by an NVLink mesh network.[4] The DGX-1 was announced on the 6th of April in 2016.[5] 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 10Gb 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 in that the device has specific features specializing it for deep learning workloads.[6] The initial Pascal based DGX-1 delivered 170 teraflops of half precision processing,[7] while the Volta-based upgrade increased this to 960 teraflops.[8]

The DGX-1 was first available only with 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.[9][10]

  • 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[11]
  • The Volta based DGX-1 is equipped with an E5-2698 V4 and was priced at launch at $149,000.[11]

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.[12]

  • Four Volta-based Tesla V100 accelerators, each with 16 GB of HBM2 memory
  • 480 TFLOPS FP16
  • Single Intel Xeon E5-2698 v4[13]
  • 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 under 35 dB under load.[14] 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.[15]

DGX-2

The successor of the Nvidia DGX-1 is the Nvidia DGX-2, which uses sixteen Volta-based V100 32GB (second generation) cards in a single unit. It was announced on the 27th of March in 2018.[16] The DGX-2 delivers 2 Petaflops with 512GB of shared memory for tackling massive datasets and uses NVSwitch for high-bandwidth internal communication. DGX-2 has a total of 512GB of HBM2 memory, a total of 1.5TB of DDR4. Also present are eight 100Gb/sec InfiniBand cards and 30.72 TB of SSD storage,[17] all enclosed within a massive 10U rackmount chassis and drawing up to 10 kW under maximum load.[18] The initial price for the DGX-2 was $399,000.[19]

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.[18]

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.[20][21][22]

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.[23] Also included is 15TB of PCIe gen 4 NVMe storage,[24] 1 TB of RAM, and eight Mellanox-powered 200GB/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.[25]

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.[23]

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.[15] 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 the US (rentacomputer.com) and Europe (iRent IT Systems) to help reduce the costs of implementing these systems at a small scale.[26][27]

The DGX Station A100 comes with two different configurations of the built in A100.

  • Four Ampere-based A100 accelerators, configured with 40GB (HBM) or 80GB (HBM2e) memory,
    thus giving a total of 160GB or 320GB 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 10Gb Ethernet
  • Single port 1Gb BMC port

Hopper

DGX H100 Server

Announced March 22, 2022[28] and planned for release in Q3 2022,[29] 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 640GB of HBM3 Memory, an upgrade over the DGX A100s HBM2 memory. This upgrade also increases VRAM bandwidth to 3 TB/s.[30] 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 1.92TB 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,[31] and the upgrade to 400Gb/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 400GB/s controllers, and two cards per system. This gives the DGX H100 3.2Tb/s of fabric bandwidth across Infiniband.[32]

The DGX H100 has two Xeon Platinum 8480C Scalable CPUs (Codenamed Sapphire Rapids)[33] and 2 Terabytes of System Memory.[34]

The DGX H100 was priced at £379,000 or ~$482,000 USD at release.[35]

DGX GH200 AI Supercomputer

Announced May, 2023 open up enormous potential in the age of AI with a new class of AI supercomputer that fully connects 256 NVIDIA Grace Hopper™ Superchips into a singular GPU. NVIDIA DGX™ GH200 is designed to handle terabyte-class models for massive recommender systems, generative AI, and graph analytics, offering 144 terabytes (TB) of shared memory with linear scalability for giant AI models.[36]

DGX SuperPod

The DGX Superpod is a high performance turnkey supercomputer solution provided by Nvidia using DGX hardware.[37] This tightly integrated system combines high performance DGX compute nodes with fast storage and high bandwidth networking to provide a unique plug & play solution to extremely 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 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 a whopping 20TB of HBM3 memory, 70.4 TB/s of bisection bandwidth, and up to 1 ExaFLOP of FP8 AI compute.[38] These SuperPods can then be further joined to create even larger supercomputers.

The upcoming Eos supercomputer, designed, built, and operated by Nvidia,[39][40][41] will be constructed of 18 H100 based SuperPods, totaling 576 DGX H100 systems, 500 Quantum-2 InfiniBand switches, and 360 NVLink Switches, this will allow Eos to deliver 18 EFLOPs of FP8 compute, and 9 EFLOPs of FP16 compute, making Eos the fastest AI supercomputer in the world.[42][43]

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.[44]

DGX Helios

The DGX Helios supercomputer, which by means of Quantum-2 InfiniBand will combine four DGX GH200 at once.

Accelerators

Comparison of accelerators used in DGX:[45][46][47]

Model Architecture Socket FP32
CUDA
cores		 FP64 cores
(excl. tensor)		 Mixed
INT32/FP32
cores		 INT32
cores		 Boost
clock		 Memory
clock		 Memory
bus width		 Memory
bandwidth		 VRAM Single
precision
(FP32)		 Double
precision
(FP64)		 INT8
(non-tensor)		 INT8
dense tensor		 INT32 FP4
dense tensor		 FP16 FP16
dense tensor		 bfloat16
dense tensor		 TensorFloat-32
(TF32)
dense tensor		 FP64
dense tensor		 Interconnect
(NVLink)		 GPU L1 Cache L2 Cache TDP Die size Transistor
count		 Process Launched
B200 Blackwell SXM6 N/A N/A N/A N/A N/A 8 Gbit/s HBM3e 8192-bit 8 TB/sec 192 GB HBM3e N/A N/A N/A 4.5 POPS N/A 9 PFLOPS N/A 2.25 PFLOPS 2.25 PFLOPS 1.2 PFLOPS 40 TFLOPS 1.8 TB/sec GB100 N/A N/A 1000 W N/A 208 B TSMC 4NP Q4 2024 (expected)
B100 Blackwell SXM6 N/A N/A N/A N/A N/A 8 Gbit/s HBM3e 8192-bit 8 TB/sec 192 GB HBM3e N/A N/A N/A 3.5 POPS N/A 7 PFLOPS N/A 1.98 PFLOPS 1.98 PFLOPS 989 TFLOPS 30 TFLOPS 1.8 TB/sec GB100 N/A N/A 700 W N/A 208 B TSMC 4NP
H200 Hopper SXM5 16896 4608 16896 N/A 1980 MHz 6.3 Gbit/s HBM3e 6144-bit 4.8 TB/sec 141 GB HBM3e 67 TFLOPS 34 TFLOPS N/A 1.98 POPS N/A N/A N/A 990 TFLOPS 990 TFLOPS 495 TFLOPS 67 TFLOPS 900 GB/sec GH100 25344 KB (192 KB × 132) 51200 KB 1000 W 814 mm2 80 B TSMC 4N Q3 2023
H100 Hopper SXM5 16896 4608 16896 N/A 1980 MHz 5.2 Gbit/s HBM3 5120-bit 3.35 TB/sec 80 GB HBM3 67 TFLOPS 34 TFLOPS N/A 1.98 POPS N/A N/A N/A 990 TFLOPS 990 TFLOPS 495 TFLOPS 67 TFLOPS 900 GB/sec GH100 25344 KB (192 KB × 132) 51200 KB 700 W 814 mm2 80 B TSMC 4N Q3 2022
A100 80GB Ampere SXM4 6912 3456 6912 N/A 1410 MHz 3.2 Gbit/s HBM2e 5120-bit 1.52 TB/sec 80 GB HBM2e 19.5 TFLOPS 9.7 TFLOPS N/A 624 TOPS 19.5 TOPS N/A 78 TFLOPS 312 TFLOPS 312 TFLOPS 156 TFLOPS 19.5 TFLOPS 600 GB/sec GA100 20736 KB (192 KB × 108) 40960 KB 400 W 826 mm2 54.2 B TSMC N7 Q1 2020
A100 40GB Ampere SXM4 6912 3456 6912 N/A 1410 MHz 2.4 Gbit/s HBM2 5120-bit 1.52 TB/sec 40 GB HBM2 19.5 TFLOPS 9.7 TFLOPS N/A 624 TOPS 19.5 TOPS N/A 78 TFLOPS 312 TFLOPS 312 TFLOPS 156 TFLOPS 19.5 TFLOPS 600 GB/sec GA100 20736 KB (192 KB × 108) 40960 KB 400 W 826 mm2 54.2 B TSMC N7
V100 32GB Volta SXM3 5120 2560 N/A 5120 1530 MHz 1.75 Gbit/s HBM2 4096-bit 900 GB/sec 32 GB HBM2 15.7 TFLOPS 7.8 TFLOPS 62 TOPS N/A 15.7 TOPS N/A 31.4 TFLOPS 125 TFLOPS N/A N/A N/A 300 GB/sec GV100 10240 KB (128 KB × 80) 6144 KB 350 W 815 mm2 21.1 B TSMC 12FFN Q3 2017
V100 16GB Volta SXM2 5120 2560 N/A 5120 1530 MHz 1.75 Gbit/s HBM2 4096-bit 900 GB/sec 16 GB HBM2 15.7 TFLOPS 7.8 TFLOPS 62 TOPS N/A 15.7 TOPS N/A 31.4 TFLOPS 125 TFLOPS N/A N/A N/A 300 GB/sec GV100 10240 KB (128 KB × 80) 6144 KB 300 W 815 mm2 21.1 B TSMC 12FFN
P100 Pascal SXM/SXM2 N/A 1792 3584 N/A 1480 MHz 1.4 Gbit/s HBM2 4096-bit 720 GB/sec 16 GB HBM2 10.6 TFLOPS 5.3 TFLOPS N/A N/A N/A N/A 21.2 TFLOPS N/A N/A N/A N/A 160 GB/sec GP100 1344 KB (24 KB × 56) 4096 KB 300 W 610 mm2 15.3 B TSMC 16FF+ Q2 2016

See also

References

  1. ^ "NVIDIA DGX-1: Deep Learning Server for AI Research". NVIDIA. Retrieved 24 March 2022.
  2. ^ "NVIDIA DGX Systems for Enterprise AI". NVIDIA. Retrieved 24 March 2022.
  3. ^ "nvidia dgx-1" (PDF). Retrieved 15 November 2023.
  4. ^ "inside pascal". 5 April 2016. Eight GPU hybrid cube mesh architecture with NVLink
  5. ^ "NVIDIA Unveils the DGX-1 HPC Server: 8 Teslas, 3U, Q2 2016".
  6. ^ "deep learning supercomputer".
  7. ^ "DGX-1 deep learning system" (PDF). NVIDIA DGX-1 Delivers 75X Faster Training...Note: Caffe benchmark with AlexNet, training 1.28M images with 90 epochs
  8. ^ "DGX Server". DGX Server. Nvidia. Retrieved 7 September 2017.
  9. ^ Volta architecture whitepaper nvidia.com
  10. ^ Use Guide nvidia.com
  11. ^ a b Oh, Nate. "NVIDIA Ships First Volta-based DGX Systems". www.anandtech.com. Retrieved 24 March 2022.
  12. ^ "CompecTA | NVIDIA DGX Station Deep Learning System". www.compecta.com. Retrieved 24 March 2022.
  13. ^ "Intel® Xeon® Processor E5-2698 v4 (50M Cache, 2.20 GHz) - Product Specifications". Intel. Retrieved 19 August 2023.
  14. ^ Supercomputer datasheet nvidia.com
  15. ^ a b "NVIDIA DGX Platform". NVIDIA. Retrieved 15 November 2023.
  16. ^ "Nvidia launches the DGX-2 with two petaFLOPS of power". 28 March 2018.
  17. ^ "NVIDIA DGX -2 for Complex AI Challenges". NVIDIA. Retrieved 24 March 2022.
  18. ^ a b Cutress, Ian. "NVIDIA's DGX-2: Sixteen Tesla V100s, 30 TB of NVMe, only $400K". www.anandtech.com. Retrieved 28 April 2022.
  19. ^ "The NVIDIA DGX-2 is the world's first 2-petaflop single server supercomputer". www.hardwarezone.com.sg. Retrieved 24 March 2022.
  20. ^ DGX2 User Guide nvidia.com
  21. ^ "Product Specifications". www.intel.com. Retrieved 28 April 2022.
  22. ^ "Product Specifications". www.intel.com. Retrieved 28 April 2022.
  23. ^ a b Ryan Smith (14 May 2020). "NVIDIA Ampere Unleashed: NVIDIA Announces New GPU Architecture, A100 GPU, and Accelerator". AnandTech.
  24. ^ Tom Warren; James Vincent (14 May 2020). "Nvidia's first Ampere GPU is designed for data centers and AI, not your PC". The Verge.
  25. ^ "Boston Labs welcomes the DGX A100 to our remote testing portfolio!". www.boston.co.uk. Retrieved 24 March 2022.
  26. ^ Mayank Sharma (13 April 2021). "Nvidia will let you rent its mini supercomputers". TechRadar. Retrieved 31 March 2022.
  27. ^ Jarred Walton (12 April 2021). "Nvidia Refreshes Expensive, Powerful DGX Station 320G and DGX Superpod". Tom's Hardware. Retrieved 28 April 2022.
  28. ^ Newsroom, NVIDIA. "NVIDIA Announces DGX H100 Systems – World's Most Advanced Enterprise AI Infrastructure". NVIDIA Newsroom Newsroom. Retrieved 24 March 2022. {{cite web}}: |last= has generic name (help)
  29. ^ Albert (24 March 2022). "NVIDIA H100: Overview, Specs, & Release Date | SeiMaxim". www.seimaxim.com. Retrieved 22 August 2022.
  30. ^ Walton, Jarred (22 March 2022). "Nvidia Reveals Hopper H100 GPU With 80 Billion Transistors". Tom's Hardware. Retrieved 24 March 2022.
  31. ^ Newsroom, NVIDIA. "NVIDIA Announces DGX H100 Systems – World's Most Advanced Enterprise AI Infrastructure". NVIDIA Newsroom Newsroom. Retrieved 19 April 2022. {{cite web}}: |last= has generic name (help)
  32. ^ servethehome (14 April 2022). "NVIDIA Cedar Fever 1.6Tbps Modules Used in the DGX H100". ServeTheHome. Retrieved 19 April 2022.
  33. ^ "NVIDIA DGX H100 Datasheet". www.nvidia.com. Retrieved 2 August 2023.
  34. ^ "NVIDIA DGX H100". NVIDIA. Retrieved 24 March 2022.
  35. ^ Every NVIDIA DGX benchmarked & power efficiency & value compared, including the latest DGX H100., retrieved 1 March 2023
  36. ^ "NVIDIA DGX GH200". NVIDIA. Retrieved 24 March 2022.
  37. ^ "NVIDIA SuperPOD Datasheet". NVIDIA. Retrieved 15 November 2023.
  38. ^ Jarred Walton (22 March 2022). "Nvidia Reveals Hopper H100 GPU With 80 Billion Transistors". Tom's Hardware. Retrieved 24 March 2022.
  39. ^ Vincent, James (22 March 2022). "Nvidia reveals H100 GPU for AI and teases 'world's fastest AI supercomputer'". The Verge. Retrieved 16 May 2022.
  40. ^ Mellor, Chris (31 March 2022). "Nvidia Eos AI supercomputer will need a monster storage system". Blocks and Files. Retrieved 21 May 2022.
  41. ^ Comment, Sebastian Moss. "Nvidia announces Eos, "world's fastest AI supercomputer"". Data Center Dynamics. Retrieved 21 May 2022.
  42. ^ "Nvidia Announces 'Eos' Supercomputer". HPCwire. 22 March 2022. Retrieved 24 March 2022.
  43. ^ "NVIDIA Eos: the world's fastest AI supercomputer, 4608 x DGX H100 GPUs". TweakTown. 22 March 2022. Retrieved 21 May 2022.
  44. ^ Mellor, Chris (31 March 2022). "Nvidia Eos AI supercomputer will need a monster storage system". Blocks and Files. Retrieved 29 April 2022.
  45. ^ Smith, Ryan (22 March 2022). "NVIDIA Hopper GPU Architecture and H100 Accelerator Announced: Working Smarter and Harder". AnandTech.
  46. ^ Smith, Ryan (14 May 2020). "NVIDIA Ampere Unleashed: NVIDIA Announces New GPU Architecture, A100 GPU, and Accelerator". AnandTech.
  47. ^ "NVIDIA Tesla V100 tested: near unbelievable GPU power". TweakTown. 17 September 2017.
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