Thanks for the code.
I’ve used the default arguments and this script to run the test:
device = 'cuda'

# enters time in latent
model = DenseEDT(1,
                 2,
                 blocks=(4, 9, 4),
                 times=(5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100),
                 growth_rate=24,
                 drop_rate=0,
                 bn_size=100,
                 num_init_features=48,
                 bottleneck=False,
                 time_channels=1).to(device)


optimizer = torch.optim.Adam(model.parameters(), lr=0.001,
                             weight_decay=5e-4)
criterion = nn.MSELoss()

data = torch.randn(10, 1, 224, 224, device=device)
times = torch.randn(10, device=device)

output = model(data, times)
target = torch.empty_like(output).normal_()

loss = criterion(output, target)
loss.backward()

print('mem allocated {:.3f}MB'.format(torch.cuda.memory_allocated()/1024**2))
> mem allocated 16.433MB
print('max mem allocated {:.3f}MB'.format(torch.cuda.max_memory_allocated()/1024**2))
> max mem allocated 2146.784MB
print('mem cached {:.3f}MB'.format(torch.cuda.memory_cached() / 1024**2))
> mem cached 2440.000MB

nvidia-smi shows a usage of approx. 3500MB using a Titan V GPU with 12GB memory.
The additional usage comes from the cuda context, which will take some space on the GPU.