Thank you very much for your reply.
So I looked at the information of convolutional neural network and opengl, and found that the problem seemed not so simple.
Let’s start with the convolution neural network. The more VRAM you mentioned, the better. So I guess it’s related to the amount of data in convolution calculation.
Question 1: Is the bigger the vram, the better? For example, AMD radeon VII has 16GB vram. Does that mean it is the best choice for VRAM card?
Question 2: If there are multiple graphics cards working together, such as two AMD r7, does that mean that for AIG, we have 32GB VRAM and two GPUs working together?
Then we discuss OpenGL (topaz points out based on v3.3).
In past forum posts, Joe also mentioned that there would be a lot of rendering in the gpu, which might involve light spot, shadow location and depth, etc.
“Benefit of using a Pro (non-gaming-optimized) video card”
Assuming that the calculation efficiency of these GPU OpenGL instructions depends on the floating-point performance of the gpu, the reference basis for selecting the equipment is much simpler and more accurate. But what operations are actually GPU-based?
