diff --git a/test/test_zero_copy.py b/test/test_zero_copy.py
index 7b7cd2f6..265e9d81 100644
--- a/test/test_zero_copy.py
+++ b/test/test_zero_copy.py
@@ -14,7 +14,7 @@ def time_tensor_numpy(out:Tensor):
 
 N = 4096
 class TestZeroCopy(unittest.TestCase):
-  @unittest.skipIf(Device.DEFAULT not in {"CLANG", "LLVM", "CPU", "TORCH", "METAL"}, "device isn't zero copy")
+  @unittest.skipIf(Device.DEFAULT not in {"CLANG", "LLVM", "CPU", "METAL"}, "device isn't zero copy")
   def test_zero_copy_from_default_to_cpu(self):
     demo = Tensor.rand(1).realize()
     t1 = time_tensor_numpy(demo)
diff --git a/tinygrad/runtime/ops_torch.py b/tinygrad/runtime/ops_torch.py
index 21f43612..75b64aa8 100644
--- a/tinygrad/runtime/ops_torch.py
+++ b/tinygrad/runtime/ops_torch.py
@@ -3,7 +3,7 @@ import numpy as np
 from typing import Dict, Callable
 from tinygrad.ops import BufferOps, UnaryOps, BinaryOps, MovementOps, TernaryOps, ReduceOps, Op
 from tinygrad.device import Interpreted, Allocator
-from tinygrad.helpers import getenv, dtypes, DType, flat_mv
+from tinygrad.helpers import getenv, dtypes, DType
 from tinygrad.runtime.ops_cpu import einsum_mulacc, shape_to_axis
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else ("mps" if getenv("MPS", 0) else "cpu"))
@@ -43,7 +43,6 @@ torch_fxn_for_op: Dict[Op, Callable] = {
 
 class TorchAllocator(Allocator):
   def _alloc(self, size:int, dtype:DType): return torch.empty([size], device=device, dtype=inverse_type_map[dtype])
-  def as_buffer(self, src:torch.Tensor) -> memoryview: return flat_mv(np.require(src.numpy(), requirements='C').data)
   def copyin(self, dest:torch.Tensor, src:memoryview): dest.copy_(torch.frombuffer(src, dtype=dest.dtype))
   def copyout(self, dest:memoryview, src:torch.Tensor): torch.frombuffer(dest, dtype=src.dtype).copy_(src.flatten())