Thanks all for above replies.
I have read the topic (Couple of models in production 75) and according it I have implemented these codes:
First Scenario (Sequential Forward Pass):
import torch
import time
from torchvision import models
from torch.autograd import Variable

# Check use GPU or not
use_gpu = torch.cuda.is_available()  # use GPU

torch.manual_seed(123)
if use_gpu:
    torch.cuda.manual_seed(456)


# Define CNN Models:
model1 = models.resnet18(pretrained=True)
model2 = models.resnet50(pretrained=True)

# Eval Mode:
model1.eval()
model2.eval()

# Put on GPU:
if use_gpu:
    model1 = model1.cuda()
    model2 = model2.cuda()

# Create tmp Variable:
x = Variable(torch.randn(10, 3, 224, 224))
if use_gpu:
    x = x.cuda()


# Forward Pass:
tic1 = time.time()
out1 = model1(x)
out2 = model2(x)
tic2 = time.time()

sequential_forward_pass = tic2 - tic1
print('Time = ', sequential_forward_pass)  # example output --> Time =  0.6485

Now I want to perform the forward passes in parallel in just one single GPU.
Second Scenario (Parallel Forward Pass):
import time
import torch
from torchvision import models
import torch.multiprocessing as mp
from torch.autograd import Variable

# Check use GPU or not
use_gpu = torch.cuda.is_available()  # use GPU

torch.manual_seed(123)
if use_gpu:
    torch.cuda.manual_seed(456)


# Define Forward Pass Method:
def forward_pass_method(model, tmp_variable):
    output = model(tmp_variable)
    return output


# Define CNN Models:
model1 = models.resnet18(pretrained=True)
model2 = models.resnet50(pretrained=True)

# Eval Mode:
model1.eval()
model2.eval()

# Put on GPU:
if use_gpu:
    model1 = model1.cuda()
    model2 = model2.cuda()

# Create tmp Variable:
x = Variable(torch.randn(10, 3, 224, 224))
if use_gpu:
    x = x.cuda()

# Parallelized the Forward Passes:
tic1 = time.time()

model1.share_memory()
model2.share_memory()

processes = []
num_processes = 2
for i in range(num_processes):
    if i == 0:
        p = mp.Process(target=forward_pass_method, args=(model1, x))
    else:
        p = mp.Process(target=forward_pass_method, args=(model2, x))
    p.start()
    processes.append(p)
for p in processes:
    p.join()

tic2 = time.time()

parallel_forward_pass = tic2 - tic1
print('Time = ', parallel_forward_pass)

However the second method has the below error:
...RuntimeError: CUDA error (3): initialization error
Would you please kindly help me to address the error?
However, It is worth nothing that, I am in doubt that parallelizing by just one single GPU is a feasible task or not.