4 ms·
Strong disagree. We need more democratization of ML capability, not less. Right now the cost of playing around with GANs is pretty prohibitive.
by esyir 7y ago
Strong disagree. We need more democratization of ML capability, not less. Right now the cost of playing around with GANs is pretty prohibitive.
- mike_d 7y agoWell Nivida just released a dev board for $99 that has the GPU chip from a Titan X in it. That is nearing Raspberry Pi levels of "democratized" computing. I don't know much about ML, but my knowledgeable friends say they are saving tons of money moving from GPU farms to GCP TPUs for training.
- close04 7y agoThe only thing it has in common with the (2015) GTX Titan X is the Maxwell architecture for the GPU but with only 128 cores and lower clocks. That would be some magic if Nvidia managed to get the GM200 GPU's power consumption down one order of magnitude from 250W. The Jetson delivers ~2 TFLOPs of single precision performance vs. Titan X's ~6 TFLOPS. Great from such a small board but not quite "same GPU".
- robotbikes 7y agoFrom what I can tell researching it, the 99$ Jetson Nano has around 1/2 teraflop of computing power for 7.5 watts of power. The Jetson TX2 has from 1.5-2 teraflops and costs around 500-600 but there was/is a student Dev kit for 300$, whereas the Titan X has 12 teraflops of computing power.
- close04 7y agoIndeed I posted the numbers for the bigger brother. The Nano is offically slated at 472GFLOPS. The 2015 GTX Titan X (GM200, ~6 TFLOPS, +/- 10% depending on source) is the only one that has something in common with the Jetson - the Maxwell architecture. The one with 10-12TFLOPS is the 2016 Titan X (GP102-400) but that was already using Turing so the comparison would be even more stretched.