diff --git a/tests/pytorch/test_grouped_tensor.py b/tests/pytorch/test_grouped_tensor.py
index 5bc2faa007..04a0376019 100644
--- a/tests/pytorch/test_grouped_tensor.py
+++ b/tests/pytorch/test_grouped_tensor.py
@@ -410,6 +410,27 @@ def test_quantize_grouped_mxfp8(self, shape: List[Tuple[int, int]], output_dbias
             expected_dbias = torch.stack([t.sum(dim=0) for t in input_tensors])
             assert torch.allclose(dbias, expected_dbias)
 
+    @pytest.mark.skipif(not mxfp8_available, reason=reason_for_no_mxfp8)
+    def test_bgrad_group_quantize_zero_size_tensor(self) -> None:
+        """Test bgrad_group_quantize handles zero-row input without error."""
+        num_tensors = 3
+        last_dim = 1024
+        grouped_input = torch.empty(0, last_dim, dtype=torch.bfloat16, device="cuda")
+
+        quantizer = MXFP8Quantizer(fp8_dtype=tex.DType.kFloat8E4M3)
+        quantizer.set_usage(rowwise=True, columnwise=False)
+        first_dims = torch.zeros(num_tensors, dtype=torch.int64, device="cuda")
+
+        grouped_output, dbias = tex.bgrad_group_quantize(
+            grouped_input,
+            quantizer,
+            num_tensors,
+            first_dims,
+        )
+
+        assert dbias.shape == (num_tensors, last_dim)
+        assert torch.all(dbias == 0)
+
     @pytest.mark.parametrize("output_dbias", [False, True])
     @pytest.mark.skipif(not mxfp8_available, reason=reason_for_no_mxfp8)
     def test_group_quantize_cudagraph_capturable(self, output_dbias: bool) -> None:
diff --git a/transformer_engine/pytorch/csrc/extensions/cast.cpp b/transformer_engine/pytorch/csrc/extensions/cast.cpp
index f150e90507..b689a1c1b4 100644
--- a/transformer_engine/pytorch/csrc/extensions/cast.cpp
+++ b/transformer_engine/pytorch/csrc/extensions/cast.cpp
@@ -247,8 +247,7 @@ py::object bgrad_group_quantize(const at::Tensor &tensor, py::handle quantizer,
   const auto logical_first_dim = logical_shape[0];
   const auto logical_last_dim = logical_shape[1];
 
-  NVTE_CHECK(logical_first_dim > 0 && logical_last_dim > 0,
-             "bgrad_group_quantize: empty input tensor is not supported.");
+  bool empty_input_buffer = logical_first_dim == 0 || logical_last_dim == 0;
 
   NVTE_CHECK(detail::IsMXFP8Quantizers(quantizer.ptr()),
              "bgrad_group_quantize: only MXFP8 quantizer is supported.");
@@ -264,6 +263,14 @@ py::object bgrad_group_quantize(const at::Tensor &tensor, py::handle quantizer,
       py::reinterpret_borrow<py::object>(quantizer), first_dims, logical_first_dim,
       logical_last_dim);
 
+  if (empty_input_buffer) {
+    at::Tensor dbias_torch =
+        at::zeros({static_cast<int64_t>(num_tensors), static_cast<int64_t>(logical_last_dim)},
+                  tensor.options());
+    return py::make_tuple(py::reinterpret_borrow<py::object>(grouped_output_py),
+                          py::cast(std::move(dbias_torch)));
+  }
+
   const std::vector<size_t> dbias_logical_shape = {num_tensors, logical_last_dim};
   GroupedTensorWrapper grouped_dbias(num_tensors, dbias_logical_shape, NVTE_DELAYED_TENSOR_SCALING);
   at::Tensor dbias_torch =