MNIST training time in CPU
am not really clear about the benchmark you're looking for, there is it performance from training perspective, or accuracy? for accuracy, there are some tools that can do the comparison between the predictions and actuals so you can measure the performance
I have created a simple feed forward Neural Network library in Java - and I need a benchmark to compare and troubleshoot my library. 
Computer specs: 

AMD Ryzen 7 2700X Eight-Core Processor 
RAM 16.0 GB
WINDOWS 10 OS  
JVM args: -Xms1024m -Xmx8192m

Note that I am not using a GPU. 
Please list the following specs:

Computer specs?
GPU or CPU (CPU is proffered but GPU is good info) 
Number of inputs 784 (this is fixed)
For each layer: 


How many nodes? 
What activation function?

Output layer:


How many nodes? (10 if classification or 1 as regression)
What activation function?

What loss function?
What gradient descent algorithm (i.e.: vanilla) 
What batch size?
How many epochs? (not iterations)
And finally, what is the training time and accuracy? 

Thank you so much
Edit 
Just to give an idea of what I am dealing with. I created a network with 

784 input nodes
784 in hidden layer 0
256 in hidden layer 1
128 in hidden layer 2
1 output nodes
mini-batch size 5
16 threads for backprop
And it has been training for ~8 hours and has only completed 694 iterations - that is not even 20% of one epoch. 

How is this done in minutes as I've seen some claims?