diff --git a/imperative/python/test/integration/test_dtr.py b/imperative/python/test/integration/test_dtr.py
index 25ad054f..7e4b98a4 100644
--- a/imperative/python/test/integration/test_dtr.py
+++ b/imperative/python/test/integration/test_dtr.py
@@ -90,7 +90,7 @@ class ResNet(M.Module):
 
 @pytest.mark.require_ngpu(1)
 def test_dtr_resnet1202():
-    batch_size = 64
+    batch_size = 8
     resnet1202 = ResNet(BasicBlock, [200, 200, 200])
     opt = optim.SGD(resnet1202.parameters(), lr=0.05, momentum=0.9, weight_decay=1e-4)
     gm = GradManager().attach(resnet1202.parameters())
@@ -103,12 +103,24 @@ def test_dtr_resnet1202():
             gm.backward(loss)
         return pred, loss
 
+    _, free_mem = mge.device.get_mem_status_bytes()
+    tensor_mem = free_mem - (2 ** 30)
+    if tensor_mem > 0:
+        x = np.ones((1, int(tensor_mem / 4)), dtype=np.float32)
+    else:
+        x = np.ones((1,), dtype=np.float32)
+    t = mge.tensor(x)
+
     mge.dtr.enable()
+    mge.dtr.enable_sqrt_sampling = True
 
     data = np.random.randn(batch_size, 3, 32, 32).astype("float32")
     label = np.random.randint(0, 10, size=(batch_size,)).astype("int32")
-    for step in range(10):
+    for _ in range(2):
         opt.clear_grad()
         _, loss = train_func(mge.tensor(data), mge.tensor(label), net=resnet1202, gm=gm)
         opt.step()
         loss.item()
+
+    t.numpy()
+    mge.dtr.disable()