diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index c45d8025386..41de07cdf5d 100755
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -64,6 +64,7 @@ Changelog
 - Fix Megatron Top-K/Top-P distillation gradients being multiplied by the tensor-parallel size when tensor parallelism is enabled; reported loss values are unchanged.
 - Fix the Puzzletron memory objective pricing the KV cache with ``hidden_size // num_attention_heads`` instead of the model's ``head_dim``, which misestimated it for models such as Mistral-Small, GPT-OSS and Gemma-3 and for every candidate with a pruned hidden size. Recompute subblock statistics produced with earlier versions.
 - Fix ONNX INT4 quantization ending the Python process when the INT4 packing extension fails to build, for example without a C++ compiler. It now falls back to the Python implementation.
+- Fix ``--dq_only`` ONNX export producing a model that ONNX Runtime cannot load when the weights are quantized to an unsigned type. Such exports now load and match the source model.
 - Fix ``fsdp2_aware_weight_update`` masking an error raised during its unshard (e.g. a CUDA OOM) with a spurious ``UnboundLocalError``.
 
 0.48.0 (2026-10-xx)
diff --git a/modelopt/onnx/quantization/qdq_utils.py b/modelopt/onnx/quantization/qdq_utils.py
index 89259c2bdce..619bc25dc74 100644
--- a/modelopt/onnx/quantization/qdq_utils.py
+++ b/modelopt/onnx/quantization/qdq_utils.py
@@ -60,6 +60,13 @@
 onnx_bit_dtype_signed_map = {4: "INT4", 8: "INT8"}
 onnx_bit_dtype_unsigned_map = {4: "UINT4", 8: "UINT8"}
 
+# Integer weight types qdq_to_dq can emit, keyed by the zero point's ONNX type. The
+# DequantizeLinear left in the graph keeps its zero point, so the weight must match it.
+qdq_weight_np_dtype_map = {
+    onnx.TensorProto.INT8: np.int8,
+    onnx.TensorProto.UINT8: np.uint8,
+}
+
 np_dtype_map = {
     "Float": np.float32,
     "Half": np.float16,
@@ -657,7 +664,7 @@ def _convert_weight(
         ValueError: If scale shape doesn't match weight shape for the operation
 
     Note:
-        - INT8 weights are clipped to [-128, 127]
+        - Integer weights are clipped to the range of the zero point's type
         - FP8 weights use float8e4m3fn format
     """
     weight_shape = weight_array.shape
@@ -690,16 +697,29 @@ def _convert_weight(
     scale_array = scale_array.reshape(*reshape_dims)
     zp_array = zp_array.reshape(*reshape_dims)
 
-    # Convert to INT8/FP8
+    # Convert to integer/FP8
     if zp.data_type == onnx_dtype_map["Float8"]:
         scaled = np.asarray(weight_array / scale_array) + zp_array
     else:
+        weight_dtype = _qdq_weight_dtype(zp)
+        bounds = np.iinfo(weight_dtype)
         scaled = np.asarray((weight_array / scale_array).round())
-        np.clip(scaled + zp_array, -128, 127, out=scaled)
+        np.clip(scaled + zp_array, bounds.min, bounds.max, out=scaled)
 
     return scaled
 
 
+def _qdq_weight_dtype(zp: onnx.TensorProto) -> type[np.signedinteger | np.unsignedinteger]:
+    """Returns the numpy type a converted weight must use for this zero point."""
+    weight_dtype = qdq_weight_np_dtype_map.get(zp.data_type)
+    if weight_dtype is None:
+        raise ValueError(
+            f"Unsupported zero point type for real weight quantization: "
+            f"{onnx.TensorProto.DataType.Name(zp.data_type)}"
+        )
+    return weight_dtype
+
+
 def _cast_fp8(array: np.ndarray) -> np.ndarray:
     """Cast a numpy array to FLOAT8E4M3FN using PyTorch."""
     array_f32_t = torch.from_numpy(array)
@@ -791,8 +811,9 @@ def qdq_to_dq(onnx_model: onnx.ModelProto) -> onnx.ModelProto:
                 new_weight = _create_fp8_tensor(scaled, weight_name)
                 logger.debug(f"Converted {weight_name} to FP8")
             else:
-                new_weight = onnx.numpy_helper.from_array(scaled.astype("int8"), weight_name)
-                logger.debug(f"Converted {weight_name} to INT8")
+                weight_dtype = _qdq_weight_dtype(zp)
+                new_weight = onnx.numpy_helper.from_array(scaled.astype(weight_dtype), weight_name)
+                logger.debug(f"Converted {weight_name} to {weight_dtype.__name__.upper()}")
             if len(tensor_consumers[weight_name]) > 1 or weight_name in graph_outputs:
                 # Keep the float value for other consumers and reuse the removed Q output name.
                 new_weight.name = node.output[0]
diff --git a/tests/unit/onnx/quantization/test_qdq_utils.py b/tests/unit/onnx/quantization/test_qdq_utils.py
index 1b4670b6457..f932c38f7bf 100644
--- a/tests/unit/onnx/quantization/test_qdq_utils.py
+++ b/tests/unit/onnx/quantization/test_qdq_utils.py
@@ -1176,6 +1176,67 @@ def test_constant_node_scale_path_still_patched(self):
         assert (scale_arr > 0).all()
 
 
+def create_test_model_with_matmul_qdq(zero_point: np.ndarray, scale: np.ndarray, weight):
+    """Create weight -> Q -> DQ -> MatMul, per-channel on the weight's output axis."""
+    graph = helper.make_graph(
+        nodes=[
+            helper.make_node(
+                "QuantizeLinear", ["weight", "w_scale", "w_zp"], ["w_q"], name="w_q", axis=1
+            ),
+            helper.make_node(
+                "DequantizeLinear", ["w_q", "w_scale", "w_zp"], ["w_dq"], name="w_dq", axis=1
+            ),
+            helper.make_node("MatMul", ["input", "w_dq"], ["output"], name="matmul"),
+        ],
+        name="matmul_qdq",
+        inputs=[helper.make_tensor_value_info("input", TensorProto.FLOAT, [4, weight.shape[0]])],
+        outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, [4, weight.shape[1]])],
+        initializer=[
+            numpy_helper.from_array(weight, "weight"),
+            numpy_helper.from_array(scale, "w_scale"),
+            numpy_helper.from_array(zero_point, "w_zp"),
+        ],
+    )
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 21)])
+    model.ir_version = 10
+    onnx.checker.check_model(model)
+    return model
+
+
+class TestQdqToDqWeightType:
+    """The converted weight must keep the type of the zero point the DQ node still uses."""
+
+    @pytest.mark.parametrize("unsigned", [False, True])
+    def test_converted_weight_matches_zero_point_type(self, unsigned):
+        weight = np.random.RandomState(3).randn(16, 32).astype(np.float32)
+        if unsigned:
+            # Asymmetric UINT8: the zero point is non-zero and the weight spans 0..255.
+            low, high = weight.min(axis=0), weight.max(axis=0)
+            scale = ((high - low) / 255.0).astype(np.float32)
+            zero_point = np.round(-low / scale).astype(np.uint8)
+            expected_type = TensorProto.UINT8
+        else:
+            scale = (np.abs(weight).max(axis=0) / 127.0).astype(np.float32)
+            zero_point = np.zeros(32, dtype=np.int8)
+            expected_type = TensorProto.INT8
+
+        model = create_test_model_with_matmul_qdq(zero_point, scale, weight)
+        inputs = {"input": np.random.RandomState(5).randn(4, 16).astype(np.float32)}
+
+        def run(m):
+            return ort.InferenceSession(
+                m.SerializeToString(), providers=["CPUExecutionProvider"]
+            ).run(None, inputs)[0]
+
+        expected = run(model)
+        converted = qdq_to_dq(onnx.ModelProto.FromString(model.SerializeToString()))
+
+        stored = next(t for t in converted.graph.initializer if t.name == "weight")
+        assert stored.data_type == expected_type
+        # onnx.checker does not catch a weight/zero-point type mismatch, so load it.
+        assert np.array_equal(run(converted), expected)
+
+
 def create_test_model_with_dq_transpose_matmul(
     out_features: int,
     in_features: int,