diff --git a/thunder/core/rematerialization.py b/thunder/core/rematerialization.py
index ee9b8f4464..f24ee1fad2 100644
--- a/thunder/core/rematerialization.py
+++ b/thunder/core/rematerialization.py
@@ -361,7 +361,6 @@ def add_edges(var):
     if not required_producer_vars:
         # If there are no required producer variables, we need to make sure that
         # the source node is added to the graph.
-        print("#### hello")
         add_edge("source", "source", capacity=float("inf"))
 
     for var in required_producer_vars:
@@ -375,8 +374,6 @@ def add_edges(var):
     g = nx.DiGraph()
     g.add_edges_from(edges)
 
-    print("#####", dict(g.nodes), g.edges)
-
     try:
         _, (reachable, non_reachable) = nx.minimum_cut(g, "source", "sink")
     except Exception:
diff --git a/thunder/tests/test_nvfuser_remat.py b/thunder/tests/test_nvfuser_remat.py
index b7de2fba7a..690c4f93bc 100644
--- a/thunder/tests/test_nvfuser_remat.py
+++ b/thunder/tests/test_nvfuser_remat.py
@@ -67,7 +67,7 @@ def test_find_producer_symbols(executor, device, _):
     # We will try to find a subgraph for rematerializing __c and __d
     t0 = make_tensor(2, 2, dtype=torch.float32, device=device)
     initial_trace = thunder.trace()(func, t0)
-    compiled_func = thunder.jit(initial_trace.python_callable())
+    compiled_func = thunder.jit(initial_trace.python_callable(), use_rematerialization=True)
     _ = compiled_func(t0)
     traces = thunder.last_traces(compiled_func)
     trace = traces[-1]
@@ -117,7 +117,7 @@ def test_find_producer_symbols(executor, device, _):
 def test_apply_rematerialization_producer(executor, device, _):
     t0 = make_tensor(2, 2, dtype=torch.float32, device=device)
     initial_trace = thunder.trace()(func, t0)
-    compiled_func = thunder.jit(initial_trace.python_callable())
+    compiled_func = thunder.jit(initial_trace.python_callable(), use_rematerialization=True)
     _ = compiled_func(t0)
     traces = thunder.last_traces(compiled_func)
     trace = traces[-1]
@@ -151,7 +151,7 @@ def test_apply_rematerialization_producer(executor, device, _):
 def test_apply_rematerialization_consumer(executor, device, _):
     t0 = make_tensor(2, 2, dtype=torch.float32, device=device)
     initial_trace = thunder.trace()(func, t0)
-    compiled_func = thunder.jit(initial_trace.python_callable())
+    compiled_func = thunder.jit(initial_trace.python_callable(), use_rematerialization=True)
     _ = compiled_func(t0)
     traces = thunder.last_traces(compiled_func)
     trace = traces[-1]
@@ -217,7 +217,7 @@ def foo(t0):
 
     t0 = make_tensor(2, 2, dtype=torch.float32, device=device)
     initial_trace = thunder.trace()(foo, t0)
-    compiled_func = thunder.jit(initial_trace.python_callable())
+    compiled_func = thunder.jit(initial_trace.python_callable(), use_rematerialization=True)
     _ = compiled_func(t0)
     traces = thunder.last_traces(compiled_func)
     trace = traces[-1]
@@ -256,7 +256,7 @@ def func(t0):
 
     t0 = make_tensor(2, 2, dtype=torch.float32, device=device)
     initial_trace = thunder.trace()(func, t0)
-    compiled_func = thunder.jit(initial_trace.python_callable())
+    compiled_func = thunder.jit(initial_trace.python_callable(), use_rematerialization=True)
     _ = compiled_func(t0)
     traces = thunder.last_traces(compiled_func)
     trace = traces[-1]
@@ -368,7 +368,9 @@ def func(
     from thunder.executors.torch_compile import torch_compile_cat_ex
 
     try:
-        compiled_func = thunder.jit(func, executors=(torch_compile_cat_ex, thunder.nvfuser_executor))
+        compiled_func = thunder.jit(
+            func, executors=(torch_compile_cat_ex, thunder.nvfuser_executor), use_rematerialization=True
+        )
         _ = compiled_func(
             t0,
             t1,
@@ -390,7 +392,7 @@ def func(
 def test_find_cut(executor, device, _):
     t0 = make_tensor(2, 2, dtype=torch.float32, device=device)
     intial_trace = thunder.trace()(func, t0)
-    compiled_func = thunder.jit(intial_trace.python_callable())
+    compiled_func = thunder.jit(intial_trace.python_callable(), use_rematerialization=True)
     _ = compiled_func(t0)
     traces = thunder.last_traces(compiled_func)
     trace = traces[-1]
@@ -417,7 +419,7 @@ def test_find_cut_dropout(executor, device, _):
 
     with patch("thunder.core.rematerialization.replace_uniform", new=replace_uniform_mock):
         intial_trace = thunder.trace()(func_with_dropout, t0)
-        compiled_func = thunder.jit(intial_trace.python_callable())
+        compiled_func = thunder.jit(intial_trace.python_callable(), use_rematerialization=True)
         _ = compiled_func(t0)
     traces = thunder.last_traces(compiled_func)
     trace = traces[-1]
@@ -458,10 +460,12 @@ def func(t0):
 
     # Result with rematerialization and without rematerialization should match
     initial_trace = thunder.trace()(func, t0)
-    result_with_remat = thunder.jit(initial_trace.python_callable())(t0)
+    result_with_remat = thunder.jit(initial_trace.python_callable(), use_rematerialization=True)(t0)
     assert not isinstance(result_with_remat, Exception)
 
-    result_without_remat = disable_rematerialization_in_nvfuser_fusion(thunder.jit(initial_trace.python_callable()))(t0)
+    result_without_remat = disable_rematerialization_in_nvfuser_fusion(
+        thunder.jit(initial_trace.python_callable(), use_rematerialization=True)
+    )(t0)
 
     torch.testing.assert_close(result_with_remat, result_without_remat)
 
@@ -473,7 +477,7 @@ def test_rematerialization_name_collision():
     def forward(x):
         return x.softmax(dim=1, dtype=torch.float)
 
-    jforward = thunder.jit(forward)
+    jforward = thunder.jit(forward, use_rematerialization=True)
 
     x = torch.randn([32768, 8], dtype=torch.bfloat16, device="cuda", requires_grad=True)