diff --git a/test/test_ops.py b/test/test_ops.py
index 0d10dde115..6e441448f8 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -1695,6 +1695,42 @@ class TestOps(unittest.TestCase):
       lambda x: torch.nn.functional.avg_pool2d(x, kernel_size=(111,28)),
       lambda x: Tensor.avg_pool2d(x, kernel_size=(111,28)), rtol=1e-5)
 
+  def test_interpolate_linear(self):
+    for in_sz, out_sz in [((52,),(29,)), ((29,),(52,))]:
+      helper_test_op([(2,3)+in_sz],
+        lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="linear"),
+        lambda x: Tensor.interpolate(x, size=out_sz, mode="linear"))
+
+  def test_interpolate_linear_corners_aligned(self):
+    for in_sz, out_sz in [((52,),(29,)), ((29,),(52,))]:
+      helper_test_op([(2,3)+in_sz],
+        lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="linear", align_corners=True),
+        lambda x: Tensor.interpolate(x, size=out_sz, mode="linear", align_corners=True))
+
+  def test_interpolate_bilinear(self):
+    for in_sz, out_sz in [((52,40),(29,31)), ((52,29),(31,40)), ((29,31),(40,52))]:
+      helper_test_op([(2,3)+in_sz],
+        lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="bilinear"),
+        lambda x: Tensor.interpolate(x, size=out_sz, mode="linear"), atol=1e-4)
+
+  def test_interpolate_bilinear_corners_aligned(self):
+    for in_sz, out_sz in [((52,40),(29,31)), ((52,29),(31,40)), ((29,31),(40,52))]:
+      helper_test_op([(2,3)+in_sz],
+        lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="bilinear", align_corners=True),
+        lambda x: Tensor.interpolate(x, size=out_sz, mode="linear", align_corners=True), atol=1e-4)
+
+  def test_interpolate_trilinear(self):
+    for in_sz, out_sz in [((5,2,8),(3,6,4))]:
+      helper_test_op([(2,3)+in_sz],
+        lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="trilinear"),
+        lambda x: Tensor.interpolate(x, size=out_sz, mode="linear"), atol=1e-4)
+
+  def test_interpolate_trilinear_corners_aligned(self):
+    for in_sz, out_sz in [((5,2,8),(3,6,4))]:
+      helper_test_op([(2,3)+in_sz],
+        lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="trilinear", align_corners=True),
+        lambda x: Tensor.interpolate(x, size=out_sz, mode="linear", align_corners=True), atol=1e-4)
+
   def test_cat(self):
     for dim in range(-2, 3):
       helper_test_op([(45,65,9), (45,65,9), (45,65,9)], lambda x,y,z: torch.cat((x,y,z), dim), lambda x,y,z: x.cat(y, z, dim=dim))
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 764a168ef6..72a7607b89 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -1922,6 +1922,33 @@ class Tensor:
     """
     return Tensor._tri(self.shape[-2], self.shape[-1], diagonal=diagonal+1, device=self.device, dtype=dtypes.bool).where(0, self).cast(self.dtype)
 
+  def interpolate(self, size:Tuple[int, ...], mode:str="linear", align_corners:bool=False) -> Tensor:
+    """
+    Downsamples or Upsamples to the requested size, accepts 0 to N batch dimensions.
+
+    The type of sampling is selected with `mode` which currently only supports `linear`.
+    To run `bilinear` or `trilinear` pass in a 2D or 3D size.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor([[1, 2, 3, 4], [21, 22, 23, 24], [41, 42, 43, 44]])
+    print(t.numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(t.interpolate(size=(2,3), mode="linear").numpy())
+    ```
+    """
+    assert isinstance(size, (tuple,list)) and all(isinstance(s, int) for s in size) and len(size) > 0 and len(size) <= self.ndim
+    assert mode == "linear", "only linear interpolate supported right now"
+    x, expand = self, list(s for s in self.shape)
+    for i in range(-len(size), 0):
+      scale = (self.shape[i] - (1 if align_corners else 0)) / (size[i] - (1 if align_corners else 0))
+      arr, reshape = Tensor.arange(size[i]).cast(dtypes.float32), [1 for _ in range(self.ndim)]
+      index = (scale*arr if align_corners else (scale*(arr+0.5))-0.5).clip(0, self.shape[i]-1)
+      reshape[i] = expand[i] = size[i]
+      low, high, perc = [y.reshape(reshape).expand(expand) for y in (index.floor(), index.ceil(), index - index.floor())]
+      x = x.gather(i, low)*(1.0 - perc) + x.gather(i, high)*perc
+    return x
+
   # ***** unary ops *****
 
   def logical_not(self):