diff --git a/test/test_dtype.py b/test/test_dtype.py
index ebd64449..57175d0c 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -112,11 +112,7 @@ class TestDType(unittest.TestCase):
     dtypes = list(map(np.dtype, ["bool", "uint8", "int8", "int16", "int32", "int64", "float32", "float64"]))
     data = [1., 2., 0., 0.5, -1.5, 5.25]
     for dt in dtypes:
-      try:
-        arr = np.asarray(data, dtype=dt)
-      except OverflowError:
-        # TODO: this happens with numpy 2.0, update with proper behavior
-        continue
+      arr = np.asarray(data).astype(dt)
       tin = Tensor(arr).numpy()
       tor = torch.as_tensor(arr).detach().numpy()
       assert dt == tin.dtype == tor.dtype, f"dtype mismatch: expected={dt} | tinygrad={tin.dtype} | torch={tor.dtype}"
diff --git a/test/test_tensor.py b/test/test_tensor.py
index c791aa2c..f1ce7ea4 100644
--- a/test/test_tensor.py
+++ b/test/test_tensor.py
@@ -307,18 +307,17 @@ class TestTinygrad(unittest.TestCase):
     if is_dtype_supported(dtypes.float16):
       data = [math.nan, -math.inf, 65504, 65519, 65519.999, 65520, 65520.1]
       data = data + [-x for x in data]
-      np.testing.assert_allclose(Tensor(data, dtype=dtypes.float16).numpy(), np.array(data, dtype=np.float16))
+      np.testing.assert_allclose(Tensor(data, dtype=dtypes.float16).numpy(), np.array(data).astype(np.float16))
 
-    # TODO: numpy changed this behavior in 2.0
-    # # uint32
-    # data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
-    # data = data + [-x for x in data]
-    # np.testing.assert_allclose(Tensor(data, dtype=dtypes.uint32).numpy(), np.array(data, dtype=np.uint32))
+    # uint32
+    data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
+    data = data + [-x for x in data]
+    np.testing.assert_allclose(Tensor(data, dtype=dtypes.uint32).numpy(), np.array(data).astype(np.uint32))
 
-    # # int32
-    # data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
-    # data = data + [-x for x in data]
-    # np.testing.assert_allclose(Tensor(data, dtype=dtypes.int32).numpy(), np.array(data, dtype=np.int32))
+    # int32
+    data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
+    data = data + [-x for x in data]
+    np.testing.assert_allclose(Tensor(data, dtype=dtypes.int32).numpy(), np.array(data).astype(np.int32))
 
   def test_tensor_list_ndarray(self):
     data = [np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2, 3])]
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 8b6a2d4c..562eb8e3 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -128,7 +128,7 @@ class Tensor:
         if (d := fully_flatten(data)) and all(isinstance(s, bool) for s in d): dtype = dtypes.bool
         else: dtype = dtypes.default_int if d and all_int(d) else dtypes.default_float
       if dtype == dtypes.bfloat16: data = Tensor(_fromnp(np.array(data, np.float32)), device=device).cast(dtypes.bfloat16).lazydata
-      else: data = _fromnp(np.array(data, _to_np_dtype(dtype)))
+      else: data = _fromnp(np.array(data).astype(_to_np_dtype(dtype)))
     elif data is None: data = _loadop(LoadOps.EMPTY, (0,), dtype or dtypes.default_float, device)
     elif isinstance(data, np.ndarray):
       if data.shape == (): data = _loadop(LoadOps.CONST, tuple(), dtype or _from_np_dtype(data.dtype), device, data.item())