Keras Deep Learning lieber 2 GPUs oder eine? (Inwieweit "stacken" mehrere GPUs)

[ObaeBaum]

Grund der fragestellung ist folgendes:
hierdrin (https://missinglink.ai/guides/computer-vision/complete-guide-deep-learning-gpus/) steht:
"

• Memory bandwidth is the most important characteristic of a GPU. Opt for a GPU with the highest bandwidth available within your budget.
• The number of cores determines the speed at which the GPU can process data, the higher the number of cores, the faster the GPU can compute data. Consider this especially when dealing with large amounts of data.
• Video RAM size (VRAM) is a measurement of how much data the GPU can handle at once. The amount of VRAM required depends on your tasks so plan accordingly.
• Processing power is a factor of the number of cores inside the GPU multiplied by the clock speed at which they run. The processing power indicates the speed at which your GPU can compute data and determines how fast your system will perform tasks.

• If you are running light tasks like small or simple deep learning models, you can use a low-end GPU like Nvidia’s GTX 1030.
• If you are handling complex tasks such as neural networks training you should equip your system with a high-end GPU like Nvidia’s RTX 2080 TI or even its most powerful Titan lineup. Alternatively, you can use a cloud service like Google’s GCP or Amazon’s AWS which provides strong GPU capabilities.
• If you are working on highly demanding tasks such as multiple simultaneous experiments or require on-premise GPU parallelism, then no matter how high end your GPU is, one GPU won’t be enough. In this case, you should purchase a system designed for multi-GPU computing."

Klar da ich meine modelle für komplexe regressions aufgaben trainieren muss, käme als erstes eine einzelne GPU in den Sinn, jedoch erhalte ich mehr Kerne wenn ich 2 günstigere GPUs kaufe.

Option A) wäre eine 2080Ti (1.200€)
Option B) 2x 2070 Super (2x500€)

NVIDIA Card	Number of CUDA Cores	Memory Interface Width	Memory Bandwidth GB/sec	Base Clock Speed	Boost Clock Speed	NOTES
RTX-2060	1920	192 bit	336 GB/s	1365 MHz	1680 MHz	6 GB VRAM
RTX-2060 Super	2176	256 bit	448 GB/s	1470 MHz	1650 MHz	8 GB VRAM
RTX-2070	2304	256 bit	448 GB/s	1410 MHz	1620 MHz	8 GB VRAM
RTX-2070 Super	2560	256 bit	448 GB/s	1605 MHz	1770 MHz	8 GB VRAM
RTX-2080	2944	256 bit	448 GB/s	1515 MHz	1710 MHz	8 GB VRAM
RTX-2080 Super	3072	256 bit	496 GB/s	1650 MHz	1815 MHz	8 GB VRAM
RTX-2080 Ti	4352	352 bit	616 GB/s	1350 MHz	1545 MHz	11 GB VRAM

Quelle: https://www.studio1productions.com/Articles/NVidia-GPU-Chart.htm

Meine erste Frage ist:
1) Inwieweit "stacken" GPUs in bezug auf Kerne, Bandbreite, VRAM (in Keras).

2) Was macht mehr Sinn eurer Erfahrung nach im Bezug aus Deep Learning?

 

[Silverangel]

Ein Thema zu deinen Fragen bezüglich Deeplearning und GPU´s reicht.
Du musst nicht für jede Frage die sich währenddessen stellt ein neues Thema eröffnen. Es ist genug Platz in deinem bisherigen.
https://www.computerbase.de/forum/threads/deep-learning-mit-cuda-pcie-x8-vs-x16.1959923/