diff --git a/test/inductor/test_torchinductor.py b/test/inductor/test_torchinductor.py
index e4aeae1a5ca84..a215d4529ac3a 100644
--- a/test/inductor/test_torchinductor.py
+++ b/test/inductor/test_torchinductor.py
@@ -11798,6 +11798,37 @@ def f(m, x):
             f"Ref:\n{ref_grad_list}\nAct:\n{act_grad_list}",
         )
 
+    def test_chunk_recompiles(self):
+        def f(x):
+            return x.chunk(4)
+
+        def run(size):
+            input = torch.randn(size)
+            expected_out = f(input)
+            actual_out = optf(input)
+            self.assertEqual(expected_out, actual_out)
+
+        cnts = CompileCounterWithBackend("inductor")
+        optf = torch.compile(f, backend=cnts, fullgraph=True)
+
+        # The first run should compile once with static shapes.
+        run(4)
+        self.assertEqual(cnts.frame_count, 1)
+
+        # Varying the input size should trigger a recompilation.
+        # Since the input size is a multiple of 4 (i.e. all runs shall
+        # generate 4 output tensors), there should be no further
+        # recompilation.
+        for i in range(2, 12):
+            run(4 * i)
+        self.assertEqual(cnts.frame_count, 2)
+
+        # Input size: 9
+        # Yields one less output tensor, which should trigger a
+        # recompilation.
+        run(9)
+        self.assertEqual(cnts.frame_count, 3)
+
 
 @dataclasses.dataclass
 class TestFailure:
diff --git a/torch/_inductor/lowering.py b/torch/_inductor/lowering.py
index 4e4bea2613e70..60fe25cc15544 100644
--- a/torch/_inductor/lowering.py
+++ b/torch/_inductor/lowering.py
@@ -1670,13 +1670,11 @@ def select(x, dim, idx):
 @register_lowering(aten.split, type_promotion_kind=None)
 def split(x, sizes, dim=0, clamp=True):
     dim = _validate_dim(x, dim, 0)
-    if isinstance(sizes, sympy.Expr):
-        # TODO: We don't have to guard on sizes per se, but the number
-        # of splits must stay constant
-        sizes = V.graph.sizevars.evaluate_static_shape(sizes)
-    if isinstance(sizes, (int, sympy.Integer)):
-        x_size = V.graph.sizevars.evaluate_static_shape(x.get_size()[dim])
-        sizes = [sizes] * ((x_size + sizes - 1) // sizes)
+    if not isinstance(sizes, (list, tuple)):
+        chunks = V.graph.sizevars.evaluate_static_shape(
+            FloorDiv(x.get_size()[dim] + sizes - 1, sizes)
+        )
+        sizes = [sizes] * chunks
     result = []
     start = 0
     for size in sizes: