diff --git a/bitsandbytes/backends/cpu/ops.py b/bitsandbytes/backends/cpu/ops.py
index 4efaa98ea..44fb5dceb 100755
--- a/bitsandbytes/backends/cpu/ops.py
+++ b/bitsandbytes/backends/cpu/ops.py
@@ -37,6 +37,7 @@ def _(A: torch.Tensor, B: torch.Tensor):
 
     @register_kernel("bitsandbytes::quantize_blockwise", "cpu")
     def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor, torch.Tensor]:
+        A = A.contiguous()
         n = A.numel()
         blocks = -(n // -blocksize)
 
@@ -94,6 +95,7 @@ def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> tuple[torch.Tensor
     def _(
         A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype
     ) -> torch.Tensor:
+        A = A.contiguous()
         out = torch.empty_like(A, dtype=dtype)
         if dtype == torch.float32:
             lib.cdequantize_blockwise_cpu_fp32(
diff --git a/tests/test_ops.py b/tests/test_ops.py
index 69589dcc0..36d4b4e12 100644
--- a/tests/test_ops.py
+++ b/tests/test_ops.py
@@ -382,9 +382,6 @@ class TestNonContiguousInputs:
     @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32], ids=id_formatter("dtype"))
     @pytest.mark.parametrize("blocksize", [64, 128, 256])
     def test_quantize_blockwise_non_contiguous(self, device, dtype, blocksize):
-        if device == "cpu":
-            pytest.skip("Non-contiguous fix targets CUDA backend only")
-
         code = bitsandbytes.functional.create_dynamic_map().to(device)
 
         # Create non-contiguous tensor via slicing
@@ -404,9 +401,6 @@ def test_quantize_blockwise_non_contiguous(self, device, dtype, blocksize):
     @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32], ids=id_formatter("dtype"))
     @pytest.mark.parametrize("blocksize", [64, 128, 256])
     def test_dequantize_blockwise_non_contiguous(self, device, dtype, blocksize):
-        if device == "cpu":
-            pytest.skip("Non-contiguous fix targets CUDA backend only")
-
         code = bitsandbytes.functional.create_dynamic_map().to(device, dtype=torch.float32)
 
         # Quantize a contiguous tensor, then create non-contiguous uint8 via transpose