diff --git a/thunder/executors/nvfuserex_impl.py b/thunder/executors/nvfuserex_impl.py
index 1788bd5430..5c410aca46 100644
--- a/thunder/executors/nvfuserex_impl.py
+++ b/thunder/executors/nvfuserex_impl.py
@@ -1740,14 +1740,17 @@ def bitwise_xor(a: TensorProxy | Number, b: TensorProxy | Number, *, fd: FusionD
 register_supported(PrimIDs.BITWISE_XOR, bitwise_xor, _elementwise_binary_check)
 
 
-# TODO nvFuser's div operation is not equivalent to the div primitive
-#   (mruberry) I need to investigate if nvFuser exposes a truncation division operation
 def div(a: TensorProxy | Number, b: TensorProxy | Number, *, fd: FusionDefinition, lc_to_nv_map: dict) -> Any:
     nva = getnv(a, fd, lc_to_nv_map)
     nvb = getnv(b, fd, lc_to_nv_map)
 
+    a_dtype = dtypes.to_dtype(a)
+    b_dtype = dtypes.to_dtype(b)
+
+    if dtypes.is_integer_dtype(a_dtype) and dtypes.is_integer_dtype(b_dtype):
+        return fd.ops.div(nva, nvb)
+
     # NOTE It's currently significantly faster for nvFuser to multiply the reciprocal than divide
-    # return fd.ops.div(nva, nvb)
     return fd.ops.mul(nva, fd.ops.reciprocal(nvb))
 
 
diff --git a/thunder/tests/test_nvfuser.py b/thunder/tests/test_nvfuser.py
index a4dad1f1aa..1f2d042242 100644
--- a/thunder/tests/test_nvfuser.py
+++ b/thunder/tests/test_nvfuser.py
@@ -961,3 +961,33 @@ def foo(t, ab):
     out_ref = foo(t, ab)
 
     assert out.equal(out_ref)
+
+
+# TODO: we should improve our consistency testing
+# to also include checks for the result of meta functions.
+@instantiate(
+    dtypes=(thunder.int64, thunder.int32),
+    executors=(nvFuserExecutor,),
+)
+def test_div_truediv_integer_tensors_consistency_nvfuser(executor, device, thunder_dtype):
+    dtype = ltorch.to_torch_dtype(thunder_dtype)
+
+    def div(a, b):
+        return thunder.prims.div(a, b)
+
+    def truediv(a, b):
+        return a // b
+
+    def make_integer_tensor():
+        half_len = 5
+        t = torch.tensor([*range(-half_len, 0), *range(1, half_len + 1)], device=device, dtype=dtype)
+        perm = torch.randperm(2 * half_len)
+        return t[perm]
+
+    x = make_integer_tensor()
+    y = make_integer_tensor()
+
+    for f in (thunder.jit(div), thunder.jit(truediv)):
+        rout = f(x.cpu(), y.cpu()).to(device)
+        jout = f(x, y)
+        assert rout.equal(jout)