Amazon.com: Nvidia Tesla K80 24GB GDDR5 CUDA Cores Graphic Cards: Computers & Accessories

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	Add to Cart	Add to Cart	Add to Cart	Add to Cart	Add to Cart	Add to Cart
Customer Rating	(55)	(2)	(10)	(45)	(22)	(487)
Price	$2,995.00	$3,009.62	$799.98	$299.99	$746.75	$912.57
Shipping	FREE Shipping	FREE Shipping	FREE Shipping	FREE Shipping	FREE Shipping	FREE Shipping
Sold By	Professional Graphics Solutions	J-Electronics	Amazon.com	Amazon.com	Amazon.com	Fateka USA
Device Type	—	Graphic Cards	Graphic Cards	Graphics Cards	Graphics Cards	Graphic Cards
Graphics Card Interface	—	PCI-E	PCI-E	PCI-E, PCI-E	PCI-E	PCI-E
Graphics Coprocessor	Nvidia Tesla	AMD FirePro W9100	quadro m 4000	NVIDIA Quadro	Nvidia Quadro	geforce gtx
Graphics Card Ram	24 GB	32 GB	8 GB	4 GB	8 GB	11 GB
Graphics Ram Type	GDDR5	DDR5 SDRAM	GDDR5	GDDR5	GDDR5	GDDR5
Hardware Connectivity	—	PCIE x 16	PCIE x 16	PCI	PCI	PCIE x 16
Built-In Media	—	AMD graphics card	NVIDIA Quadro P4000 Professional Graphics Board, Software Installation Disc, Printed Quick Start Guide, Auxiliary Power Cable, Stereo Connector Bracket, DisplayPort to DVI-D SL Adapter	NVIDIA Quadro K1200 for DisplayPort	NVIDIA Quadro M4000 Professional Graphics Board, Stereo Connector Bracket, Auxiliary Power Cable, DP to DVI-D SL Adapter, Software Installation Disc, Printed Quick Start Guide	EVGA GTX 1080 FTW3 GAMING iCX Technology Graphics Card, Driver CD, Installation Guide, 6Pin(2) to 8 Pin Adapter, EVGA Gaming Poster, Powered by EVGA Case Badge, EVGA Enthusiast Built Sticker
Item Dimensions	7.8 x 17.6 x 4.2 in	8 x 12 x 3.36 in	4.38 x 9.5 x 0.75 in	3.5 x 6.3 x 2.7 in	2.5 x 9.5 x 4.4 in	2.81 x 12.81 x 9.5 in
Memory Bus Width	—	512 bits	256 bits	128 bits	256 bits	352 bits
Memory Clock Speed	—	1,066 MHz	3,802 MHz	512 MHz	6,008 MHz	11,000 MHz
Style Name	—	—	—	—	—	FTW3

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Product description

NVIDIA Tesla K80 900-22080-0000-000 Passive Computing Accelerators - Memory Size: 24GB GDDR5 (12GB per GPU), , GPU: 2x Kepler GK210, Memory Bandwidth: 480 GB/sec (240 GB/sec per GPU), CUDA Cores: 4992 (2496 per GPU).

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K. Weaver

Great for simulations and intense math calculations

February 17, 2016

Verified Purchase

Before I get into the review itself, note that this card is intended for servers and datacenters, and requires an extensive cooling system. In particular, the K80 is passively cooled, and thus, doesn't have any fans built into it.

However, it IS possible to use it in a normal desktop, though such usage is not supported by Nvidia. The motherboard and CPU I used are the Asus X99 Pro and the i7 5820k. You must enable the "Above 4G Decoding" option in the Boot menu in BIOS for the K80 to be recognized. Cooling was tricky; I 3d printed a duct with ABS+ from a 240CFM fan which is admittedly quite noisy, but the K80 is stable at 67C at full load. If you're going to go for a custom solution like ours with a 3d printer, make sure you're not using PLA as the glass transition temperature is 60-65C.

Now for the actual review...
We use our K80 for simulations and for deep learning.
With deep learning, you're probably better off with 2 (or maybe even 4) Titan Xs as a single one of those has nearly as much single precision floating point performance as the K80. However, for use cases which require double precision, the K80 blows the Titan X out of the water. We've been very happy here with the K80, but if you're going to use a K80, you're going to be better off buying it from a vendor who has already built it into a system.

0Comment 81 people found this helpful. Was this review helpful to you?

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Desmond Di Wang

Dream card for training Neural Networks... and for those who sneer at it... get a life

March 19, 2015

Most of you think it's a joke but you don't capture its value in computing in Artificial Intelligence, especially Convolutional Neural Network. I participated in one Kaggle competition where you have to design an algorithm to classify ocean creatures based on their images. It was hard but tractable via Neural Networks, with a moderately-sized NN I trained it to 80% accuracy in 10 days with a GTX970 (4GB and ~1600 Cuda cores). Now with this beast you can train much larger NNs in just a few days!! GPUs are excellent for massive parallel programming as each Cuda core specializes in simple calculations, which can be used to high efficiency for things like matrix manipulation.

For all the people joking about this card and its price - get a life, read more, learn stuff that exists beyond your universe. There is a product there is a demand. Most successful image processing companies would own this card. Do you think movies like Avatar are made from your desktop GPU GTX980??? Google's latest deep learning results and Google's self-driven cars are all based on training all sorts of Neural Networks on such monstrous GPUs, because a NN with 100 million parameters cannot reside on a card with merely 6GB memory!

Will update my review once I got it. (Now in negotiation with a China's hospital to get sponsored for this card, and help them with diabetic retinopathy based on image recognition through patients' retina scan.)

0Comment 292 people found this helpful. Was this review helpful to you?

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Robert J. Broughton

it would have been faster then the fastest super computer (SGI Origin 2000) in the world

April 6, 2015

If this card was made in 1999, it would have been faster then the fastest super computer (SGI Origin 2000) in the world. An that thing cost 40 million dollars at the time and took up a large room with extensive electrical costs. If you put four of these Tesla together with the latest CPU, it would have held the world record until 2002. But then again that old supercomputer (ASCI White) beast wouldn't have fit in a full tower and the ASCI White cost close to 110 million dollars, weighed 106 tons, and consumed 6 Megawatts of power for running and cooling. This thing is a bargain in comparison.

0Comment 114 people found this helpful. Was this review helpful to you?

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Rodrigo A L Barbosa

CAUTION!!! Do not buy with this company ...

January 11, 2017

Verified Purchase

CAUTION!!! Do not buy with this company (Professional Graphics Solutions), even the purchase being sponsored by Amazon.

I made the purchase 6 months ago and my card no longer works. Neither the company nor Amazon had the decency to honor the guarantee.

I also tried directly with HP and they reported that the serial number is not recognized. I think the product is of dubious origin.

I simply lost an investment of $ 4000.

In the last year I bought 4 K80 boards on Amazon and I fear for future problems to increase my loss.

0Comment 34 people found this helpful. Was this review helpful to you?

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Fei Xia

Five Stars

May 17, 2017

I have been using 560 K80 in the cluster of my institution and they work very well.

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This is a great computational accelerator and when used properly it will give ...

June 18, 2015

This is a great computational accelerator and when used properly it will give many times more capacity for computations than a cpu alone.

0Comment 37 people found this helpful. Was this review helpful to you?

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Anthony

Thank God for video games because now we can do science.

June 29, 2016

I do not own this. However, my supercomputing center recently installed a few of these. I am giddy at how fast the computer is running right now. Literally like 300 times faster than our single processors. This is freaking awesome.

0Comment 15 people found this helpful. Was this review helpful to you?

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Rajiv Raghavan

Five Stars

January 14, 2016

Finally Ark is playable.

0Comment 76 people found this helpful. Was this review helpful to you?

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