NVIDIA’s CEO Gives Away Their Mighty Volta GPU Based Tesla V100 AI Accelerators To Top 15 AI Research Institutions

Jul 26, 2017

In a surprise move, NVIDIA’s CEO, Jen-Hsun Huang, has given away their latest Volta GPU based Tesla V100 accelerators to the top 15 AI research institutions at the computer vision and pattern Recognition conference in Honolulu.

NVIDIA Reshaping AI Research With Their Volta Based Tesla V100 GPU Accelerators

At the event, NVIDIA’s CEO not only announced the new Tesla V100 PCIe accelerator, he also gave it away to the top 15 AI researchers. The event compromised of 150 elite deep learning researchers from 15 different AI research institutes were warmly welcomed and presented the Tesla V100 PCIe accelerator in the NVIDIA AI Labs program.

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NVIDIA’s CEO, Jen-Hsun Huang, gave away a Tesla V100 accelerator to the representatives of each institute (15 in total). The card was elegantly packaged inside a black colored box, each of which included his (Jen-Hsun’s) signature, along with an inscription on the accelerator’s box that read, “Do great AI!”.

One of the researchers, Silvio Savarese, an associate professor of computer science at Stanford University and director of the school’s SAIL-Toyota Center for AI Research, likened the signed V100 box to a bottle of fine wine.

Savarese’s research has broken ground in computer vision, robotic perception and machine learning. In recent years, he has received the Best Student Paper Award at CVPR 2016, the James R. Croes Medal in 2013, a TRW Automotive Endowed Research Award in 2012, an NSF Career Award in 2011 and a Google Research Award in 2010.

It was clear this moment meant something special to him.

“It’s exciting, especially to get Jensen’s signature,” Savarese said. “My students will be even more excited.”

He said the V100 would be used for new research on autonomous driving and virtual reality, among other areas.

“Everything is powered by deep learning,” said Savarese. “We can do things we’ve never done before.”

Breakthroughs made by researchers such as Savarese and others gathered at CVPR are unleashing technologies with superhuman capabilities.

So it’s fitting that the researchers in attendance will be among the first to put our latest technology to work. via NVIDIA

NVIDIA Volta V100 GPU Based Tesla V100 PCIe Graphics Card Announced – 14 TFLOPs of FP32 and 27 TFLOPs of FP16 Compute Performance in a 250 Watt Package

NVIDIA announced their Volta V100 GPU based Tesla V100 accelerator at GTC 2017. The new chip from NVIDIA is a behemoth that utilizes the new TSMC 12nm FFN (FinFET NVIDIA) process that is custom built to power NVIDIA’s Volta GPUs. The chip houses an incredible 21 Billion transistors under the hood and is an incredible feat of engineering. The graphics accelerator we saw at GTC ’17 utilized the SXM2 form factor and while NVIDIA did tease their PCI Express based variant, they are formally announcing it today.

The NVIDIA Tesla V100 for PCI Express based systems has the same Volta V100 GPU as the SXM2 variant. It features a GPU die size of 815mm2 (the biggest chip to date) and houses tons of HBM2 memory on board the main interposer. Let’s do a run down of the core specifications.

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NVIDIA Volta V100 GPU Based Tesla V100 PCI Express Specifications

The chip itself is a behometh, featuring a brand new chip architecture that is just insane in terms of raw specifications. The NVIDIA Volta GV100 GPU is composed of six GPC (Graphics Processing Clusters). It has a total of 84 Volta streaming multiprocessor units, 42 TPCs (each including two SMs).

The 84 SMs come with 64 CUDA cores per SM so we are looking at a total of 5376 CUDA cores on the complete die. All of the 5376 CUDA Cores can be used for FP32 and INT32 programming instructions while there are also a total of 2688 FP64 (double precision) cores. Aside from these, we are looking at 672 tensor processors, 336 texture units. The core clocks are maintained at a boost clock of around 1370 MHz which delivers 28 TFLOPs of FP16, 14 TFLOPs of FP32 and 7.0 TFs of FP64 compute performance.

The chip also delivers 112 DLOPs (Deep Learning Teraflops) which is the fastest any chip has delivered to date. This is achieved by the separate tensor cores that are dedicated to deep learning tasks. So while the clocks and compute performance is slightly lower than the SXM2 variant, it does feature a TDP of just 250W. Compared to 300W on the SXM2 card, this is an incredible feat that delivers increased efficiency.

The memory architecture is updated with eight 512-bit memory controllers. This rounds up to a total of 4096-bit bus interface that supports up to 16 GB of HBM2 VRAM. The bandwidth is boosted with a speed of 878 MHz, which delivers increased transfer rates of 900 GB/s compared to 720 GB/s on Pascal GP100. Each memory controller is attached to 768 KB of L2 cache which totals 6 MB of L2 cache for the entire chip.

NVIDIA Volta Tesla V100 Specs:

NVIDIA Tesla Graphics Card Tesla K40
Tesla M40
Tesla P100
Tesla P100
Tesla P100 (SXM2) Tesla V100 (PCI-Express) Tesla V100 (SXM2)
GPU GK110 (Kepler) GM200 (Maxwell) GP100 (Pascal) GP100 (Pascal) GP100 (Pascal) GV100 (Volta) GV100 (Volta)
Process Node 28nm 28nm 16nm 16nm 16nm 12nm 12nm
Transistors 7.1 Billion 8 Billion 15.3 Billion 15.3 Billion 15.3 Billion 21.1 Billion 21.1 Billion
GPU Die Size 551 mm2 601 mm2 610 mm2 610 mm2 610 mm2 815mm2 815mm2
SMs 15 24 56 56 56 80 80
TPCs 15 24 28 28 28 40 40
CUDA Cores Per SM 192 128 64 64 64 64 64
CUDA Cores (Total) 2880 3072 3584 3584 3584 5120 5120
FP64 CUDA Cores / SM 64 4 32 32 32 32 32
FP64 CUDA Cores / GPU 960 96 1792 1792 1792 2560 2560
Base Clock 745 MHz 948 MHz TBD TBD 1328 MHz TBD 1370 MHz
Boost Clock 875 MHz 1114 MHz 1300MHz 1300MHz 1480 MHz 1370 MHz 1455 MHz
FP16 Compute N/A N/A 18.7 TFLOPs 18.7 TFLOPs 21.2 TFLOPs 28.0 TFLOPs 30.0 TFLOPs
FP32 Compute 5.04 TFLOPs 6.8 TFLOPs 10.0 TFLOPs 10.0 TFLOPs 10.6 TFLOPs 14.0 TFLOPs 15.0 TFLOPs
FP64 Compute 1.68 TFLOPs 0.2 TFLOPs 4.7 TFLOPs 4.7 TFLOPs 5.30 TFLOPs 7.0 TFLOPs 7.50 TFLOPs
Texture Units 240 192 224 224 224 320 320
Memory Interface 384-bit GDDR5 384-bit GDDR5 4096-bit HBM2 4096-bit HBM2 4096-bit HBM2 4096-bit HBM2 4096-bit HBM2
Memory Size 12 GB GDDR5 @ 288 GB/s 24 GB GDDR5 @ 288 GB/s 12 GB HBM2 @ 549 GB/s 16 GB HBM2 @ 732 GB/s 16 GB HBM2 @ 732 GB/s 16 GB HBM2 @ 900 GB/s 16 GB HBM2 @ 900 GB/s
L2 Cache Size 1536 KB 3072 KB 4096 KB 4096 KB 4096 KB 6144 KB 6144 KB
TDP 235W 250W 250W 250W 300W 250W 300W

The other differences is that the Tesla V100 PCI Express doesn’t get NVLINK support like the SXM2 based variant. It comes with a passive dual slot cooler in the gold and black color scheme that was seen earlier. Compared to the competition, NVIDIA is offering much higher compute performance at lower wattage and much higher efficiency.

It should also be pointed out that NVIDIA offers double precision, single precision, half precision and INT8 at much higher rates than the competition’s yet to be released cards. NVIDIA has done really great in the AI field and with Volta, they are just getting started. The Volta GV100 GPU is now being shipped to multiple servers in SMX and PCIe form factors and will soon be powering the most powerful supercomputers in the world too.