diff --git a/vllm/v1/serial_utils.py b/vllm/v1/serial_utils.py
index 0dcf02113f..fbd38fc472 100644
--- a/vllm/v1/serial_utils.py
+++ b/vllm/v1/serial_utils.py
@@ -158,10 +158,8 @@ class MsgpackEncoder:
         self, obj: torch.Tensor
     ) -> tuple[str, tuple[int, ...], Union[int, memoryview]]:
         assert self.aux_buffers is not None
-        # this creates a copy of the tensor if it's not already contiguous
-        obj = obj.contiguous()
         #  view the tensor as a 1D array of bytes
-        arr = obj.view((obj.numel(), )).view(torch.uint8).numpy()
+        arr = obj.flatten().view(torch.uint8).numpy()
         if obj.nbytes < self.size_threshold:
             # Smaller tensors are encoded inline, just like ndarrays.
             data = msgpack.Ext(CUSTOM_TYPE_RAW_VIEW, arr.data)
@@ -169,7 +167,7 @@ class MsgpackEncoder:
             # Otherwise encode index of backing buffer to avoid copy.
             data = len(self.aux_buffers)
             self.aux_buffers.append(arr.data)
-        dtype = str(obj.dtype)[6:]  # remove 'torch.' prefix
+        dtype = str(obj.dtype).removeprefix("torch.")
         return dtype, obj.shape, data
 
     def _encode_nested_tensors(self, nt: NestedTensors) -> Any:
@@ -245,7 +243,7 @@ class MsgpackDecoder:
         # zero-copy decode. We assume the ndarray will not be kept around,
         # as it now locks the whole received message buffer in memory.
         buffer = self.aux_buffers[data] if isinstance(data, int) else data
-        return np.ndarray(buffer=buffer, dtype=np.dtype(dtype), shape=shape)
+        return np.frombuffer(buffer, dtype=dtype).reshape(shape)
 
     def _decode_tensor(self, arr: Any) -> torch.Tensor:
         dtype, shape, data = arr
@@ -254,12 +252,15 @@ class MsgpackDecoder:
         # not complain about a readonly memoryview.
         buffer = self.aux_buffers[data] if isinstance(data, int) \
             else bytearray(data)
-        # Create numpy wrapper around the bytes
-        arr = np.ndarray(buffer=buffer, dtype=np.uint8, shape=(len(buffer), ))
         torch_dtype = getattr(torch, dtype)
         assert isinstance(torch_dtype, torch.dtype)
+        if not buffer:  # torch.frombuffer doesn't like empty buffers
+            assert 0 in shape
+            return torch.empty(shape, dtype=torch_dtype)
+        # Create uint8 array
+        arr = torch.frombuffer(buffer, dtype=torch.uint8)
         # Convert back to proper shape & type
-        return torch.from_numpy(arr).view(torch_dtype).view(shape)
+        return arr.view(torch_dtype).view(shape)
 
     def _decode_mm_items(self, obj: list) -> list[MultiModalKwargsItem]:
         decoded_items = []