At GTC 2016, NVIDIA announced their behemoth DGX-1 supercomputer which features up to 170 TFLOPs of compute performance. The DGX-1 is an all-in-one supercomputing solution that houses several Tesla P100 graphics boards that were launched today. Based on the Pascal GPU architecture, the Tesla P100 delivers an insane boost in compute performance and allows high-performance supercomputing for deep learning.

NVIDIA DGX-1 Is A 16nm Pascal Based Super computing Solution With 170 TeraFlops of Compute Performance

The NVIDIA DGX-1 is a complete supercomputing solution that houses NVIDIA's latest hardware and software innovations ranging from Pascal and NVIDIA SDK suite. The DGX-1 has the performance throughput equivalent to 250 x86 servers. This insane amount of performance allows users to get their own supercomputer for HPC and AI specific workloads.

"Artificial intelligence is the most far-reaching technological advancement in our lifetime," said Jen-Hsun Huang, CEO and co-founder of NVIDIA. "It changes every industry, every company, everything. It will open up markets to benefit everyone. Data scientists and AI researchers today spend far too much time on home-brewed high performance computing solutions. The DGX-1 is easy to deploy and was created for one purpose: to unlock the powers of superhuman capabilities and apply them to problems that were once unsolvable." via NVIDIA

Some of the key specifications of NVIDIA's DGX-1 Supercomputer include:

• Up to 170 teraflops of half-precision (FP16) peak performance
• Eight Tesla P100 GPU accelerators, 16GB memory per GPU
• NVLink Hybrid Cube Mesh
• 7TB SSD DL Cache
• Dual 10GbE, Quad InfiniBand 100Gb networking
• 3U - 3200W

Supercomputing Performance Comes at a Super Insane Price - $129,000 US For NVIDIA's DGX-1

It's obvious that the NVIDIA DGX-1 isn't built for a specific user but will be aimed at big organizations such as universities and institutes involved in research. Just like Pascal which will be shipping in 2016 and available in June in the US and Q3 for the rest of the world, the DGX-1 orders commence from today but will be available at a later date. Probably by the end of this year. The NVIDIA DGX-1 comes with 8 Pascal based Tesla P100 graphics boards, Dual Intel Xeon processors and 7 TBs of SSD storage. The whole platform achieves an aggregate bandwidth of 768 GB/s.

Comprehensive Deep Learning Software Suite

The NVIDIA DGX-1 system includes a complete suite of optimized deep learning software that allows researchers and data scientists to quickly and easily train deep neural networks.

The DGX-1 software includes the NVIDIA Deep Learning GPU Training System (DIGITS), a complete, interactive system for designing deep neural networks (DNNs). It also includes the newly released NVIDIA CUDA Deep Neural Network library (cuDNN) version 5, a GPU-accelerated library of primitives for designing DNNs.
It also includes optimized versions of several widely used deep learning frameworks — Caffe, Theano and Torch. The DGX-1 additionally provides access to cloud management tools, software updates and a repository for containerized applications.

The Tesla P100 Housed Inside the DGX-100 Is a Monster Graphics Card

The Tesla P100 is the heart of the DGX-100 platform. Featuring the latest 5th generation Pascal architecture with 3584 CUDA Cores, 240 texture mapping units, clock speeds up to 1480 MHz and 16 GB of HBM2 VRAM (720 GB/s stream bandwidth), the DGX-1 is all prepped for the most intensive workloads pitted against it. We have already covered an extensive deal of architecture details in our article here so you definitely want to give that a read. The NVIDIA presentation was definitely enjoyable for the folks who love high performance computing products but we are very sure that NVIDIA will have products for consumers heading it in a very short time.

NVIDIA Volta Tesla V100S Specs:

NVIDIA Tesla Graphics Card	Tesla K40 (PCI-Express)	Tesla M40 (PCI-Express)	Tesla P100 (PCI-Express)	Tesla P100 (SXM2)	Tesla V100 (PCI-Express)	Tesla V100 (SXM2)	Tesla V100S (PCIe)
GPU	GK110 (Kepler)	GM200 (Maxwell)	GP100 (Pascal)	GP100 (Pascal)	GV100 (Volta)	GV100 (Volta)	GV100 (Volta)
Process Node	28nm	28nm	16nm	16nm	12nm	12nm	12nm
Transistors	7.1 Billion	8 Billion	15.3 Billion	15.3 Billion	21.1 Billion	21.1 Billion	21.1 Billion
GPU Die Size	551 mm2	601 mm2	610 mm2	610 mm2	815mm2	815mm2	815mm2
SMs	15	24	56	56	80	80	80
TPCs	15	24	28	28	40	40	40
CUDA Cores Per SM	192	128	64	64	64	64	64
CUDA Cores (Total)	2880	3072	3584	3584	5120	5120	5120
Texture Units	240	192	224	224	320	320	320
FP64 CUDA Cores / SM	64	4	32	32	32	32	32
FP64 CUDA Cores / GPU	960	96	1792	1792	2560	2560	2560
Base Clock	745 MHz	948 MHz	1190 MHz	1328 MHz	1230 MHz	1297 MHz	TBD
Boost Clock	875 MHz	1114 MHz	1329MHz	1480 MHz	1380 MHz	1530 MHz	1601 MHz
FP16 Compute	N/A	N/A	18.7 TFLOPs	21.2 TFLOPs	28.0 TFLOPs	30.4 TFLOPs	32.8 TFLOPs
FP32 Compute	5.04 TFLOPs	6.8 TFLOPs	10.0 TFLOPs	10.6 TFLOPs	14.0 TFLOPs	15.7 TFLOPs	16.4 TFLOPs
FP64 Compute	1.68 TFLOPs	0.2 TFLOPs	4.7 TFLOPs	5.30 TFLOPs	7.0 TFLOPs	7.80 TFLOPs	8.2 TFLOPs
Memory Interface	384-bit GDDR5	384-bit GDDR5	4096-bit HBM2	4096-bit HBM2	4096-bit HBM2	4096-bit HBM2	4096-bit HBM
Memory Size	12 GB GDDR5 @ 288 GB/s	24 GB GDDR5 @ 288 GB/s	16 GB HBM2 @ 732 GB/s 12 GB HBM2 @ 549 GB/s	16 GB HBM2 @ 732 GB/s	16 GB HBM2 @ 900 GB/s	16 GB HBM2 @ 900 GB/s	16 GB HBM2 @ 1134 GB/s
L2 Cache Size	1536 KB	3072 KB	4096 KB	4096 KB	6144 KB	6144 KB	6144 KB
TDP	235W	250W	250W	300W	250W	300W	250W