diff --git a/test/test_dtype.py b/test/test_dtype.py
index 79fbfb8f6d..a0c17090e9 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -748,9 +748,20 @@ class TestAutoCastType(unittest.TestCase):
   def test_matmul(self, dt1, dt2, acc_dt):
     t1 = Tensor([0, 1], dtype=dt1)
     t2 = Tensor([0, 1], dtype=dt2)
-    assert (t1 @ t2).dtype == least_upper_dtype(dt1, dt2)
+    self.assertEqual(t1.matmul(t2).dtype, least_upper_dtype(t1.dtype, t2.dtype))
     # if dtype is specified, return in dtype
-    assert (t1.matmul(t2, dtype=acc_dt).dtype == acc_dt)
+    self.assertEqual(t1.matmul(t2, dtype=acc_dt).dtype, acc_dt)
+
+  @given(strat.sampled_from(core_dtypes), strat.sampled_from(core_dtypes), strat.sampled_from(core_dtypes), strat.sampled_from(core_dtypes))
+  def test_linear(self, dt1, dt2, dt3, acc_dt):
+    x = Tensor([0, 1], dtype=dt1)
+    w = Tensor([0, 1], dtype=dt2)
+    b = Tensor([0, 1], dtype=dt3)
+    self.assertEqual(x.linear(w).dtype, least_upper_dtype(x.dtype, w.dtype))
+    self.assertEqual(x.linear(w, b).dtype, least_upper_dtype(least_upper_dtype(x.dtype, w.dtype), b.dtype))
+    # if dtype is specified, return in dtype
+    self.assertEqual(x.linear(w, dtype=acc_dt).dtype, acc_dt)
+    self.assertEqual(x.linear(w, b, dtype=acc_dt).dtype, acc_dt)
 
   @staticmethod
   def check_where_alternate_input_other(input_, other, data_type):
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 83dd917677..d39c0f9c2d 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -3665,7 +3665,7 @@ class Tensor(SimpleMathTrait):
 
   # ***** functional nn ops *****
 
-  def linear(self, weight:Tensor, bias:Tensor|None=None) -> Tensor:
+  def linear(self, weight:Tensor, bias:Tensor|None=None, dtype:DTypeLike|None=None) -> Tensor:
     """
     Applies a linear transformation to `self` using `weight` and `bias`.
 
@@ -3678,6 +3678,7 @@ class Tensor(SimpleMathTrait):
     print(t.linear(weight, bias).numpy())
     ```
     """
+    if dtype is not None: return self.cast(dtype).linear(weight.cast(dtype), bias.cast(dtype) if bias is not None else bias)
     x = self.mul(weight) if len(weight.shape) == 1 else self.dot(weight)
     return x.add(bias) if bias is not None else x