diff --git a/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor.py b/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor.py
index 70e400c6..34ae6b44 100644
--- a/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor.py
+++ b/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor.py
@@ -119,6 +119,7 @@ def fix_quantization_params_rank(
       symmetric=quantization_params.symmetric,
       quantized_dimension=quantization_params.quantized_dimension,
       quantized_data=quantization_params.quantized_data,
+      block_size=quantization_params.block_size,
   )
 
 
@@ -204,13 +205,16 @@ def _broadcast_scale_zp_for_blockwise(
       ),
       tensor_content.shape,
   )
-  expanded_zp = np.reshape(
-      np.broadcast_to(
-          np.expand_dims(quant_params.zero_point, quantized_dim + 1),
-          expanded_tensor_shape,
-      ),
-      tensor_content.shape,
-  )
+  if quant_params.zero_point is None or quant_params.zero_point.size == 0:
+    expanded_zp = np.zeros_like(tensor_content, dtype=np.int32)
+  else:
+    expanded_zp = np.reshape(
+        np.broadcast_to(
+            np.expand_dims(quant_params.zero_point, quantized_dim + 1),
+            expanded_tensor_shape,
+        ),
+        tensor_content.shape,
+    )
   return qtyping.UniformQuantParams(
       scale=expanded_scale,
       zero_point=expanded_zp,
@@ -290,6 +294,26 @@ def uniform_dequantize(
   Returns:
     The dequantized tensor.
   """
+  if quantization_params.block_size != 0:
+    # b/443830202: The quantized dimension is currently increased by 1 because
+    # AEQ expects 1 and XNNPack expects 0.
+    quantization_params = dataclasses.replace(
+        quantization_params,
+        quantized_dimension=quantization_params.quantized_dimension + 1,
+    )
+    scale_shape = list(tensor_data.shape)
+    scale_shape[quantization_params.quantized_dimension] = (
+        scale_shape[quantization_params.quantized_dimension]
+        // quantization_params.block_size
+    )
+    quantization_params = dataclasses.replace(
+        quantization_params,
+        scale=quantization_params.scale.reshape(scale_shape),
+    )
+    quantization_params = _broadcast_scale_zp_for_blockwise(
+        tensor_data, quantization_params
+    )
+
   # quant params in flatbuffer is flattened, expand the rank to be the same
   # as the tensor rank to avoid ambiguous broadcasting.
   quantization_params = fix_quantization_params_rank(
diff --git a/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor_test.py b/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor_test.py
index 811c621f..8e186073 100644
--- a/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor_test.py
+++ b/ai_edge_quantizer/algorithms/uniform_quantize/uniform_quantize_tensor_test.py
@@ -278,6 +278,30 @@ def test_uniform_dequantize_wrong_shape(self):
           ),
       )
 
+  def test_uniform_dequantize_blockwise(self):
+    quantized_tensor = np.array([[-8, -5, -4, 7], [-4, 7, -8, -5]])
+    expected_output_tensor = np.array([
+        [-10.1333336, -6.3333335, -5.0666668, 8.8666669],
+        [-5.0666668, 8.8666669, -10.1333336, -6.3333335],
+    ])
+    quant_params = qtyping.UniformQuantParams(
+        # b/443830202:
+        quantized_dimension=0,
+        num_bits=4,
+        scale=np.array([[[1.2666667, 1.2666667], [1.2666667, 1.2666667]]]),
+        zero_point=np.array([[0]]),
+        symmetric=True,
+        block_size=2,
+    )
+
+    dequantized_tensor = uniform_quantize_tensor.uniform_dequantize(
+        np.array(quantized_tensor), quant_params
+    )
+
+    self.assertSequenceAlmostEqual(
+        expected_output_tensor.flatten(), dequantized_tensor.flatten(), places=4
+    )
+
   @parameterized.parameters(
       (8, 8, True, True),
       (8, 4, False, True),
diff --git a/ai_edge_quantizer/qtyping.py b/ai_edge_quantizer/qtyping.py
index 56e0ca6c..28eb5d9d 100644
--- a/ai_edge_quantizer/qtyping.py
+++ b/ai_edge_quantizer/qtyping.py
@@ -212,6 +212,7 @@ def from_tfl_tensor_details(cls, tensor_detail) -> 'UniformQuantParams':
         scale=quant_params['scales'],
         zero_point=quant_params['zero_points'],
         symmetric=symmetric,
+        block_size=quant_params['block_size'],
     )
 
   def __eq__(self, other):
diff --git a/ai_edge_quantizer/transformations/quantize_tensor.py b/ai_edge_quantizer/transformations/quantize_tensor.py
index 26a7fe6c..8a2996b9 100644
--- a/ai_edge_quantizer/transformations/quantize_tensor.py
+++ b/ai_edge_quantizer/transformations/quantize_tensor.py
@@ -154,9 +154,11 @@ def _perform_blockwise_quantization(
       transformation_input.buffers,
   )
   blockwise_details.scales = scale_tensor_id
+  blockwise_details.zeroPoints = -1
   blockwise_details.blockSize = transformation_input.quant_params.block_size
   # TODO: b/404909258 - Add optional zero point to blockwise quantization.
   flatbuffer_quantization.details = blockwise_details
+  flatbuffer_quantization.quantizedDimension = 0
   return flatbuffer_quantization
 
 
diff --git a/ai_edge_quantizer/transformations/quantize_tensor_test.py b/ai_edge_quantizer/transformations/quantize_tensor_test.py
index 5460907f..ba90ccdc 100644
--- a/ai_edge_quantizer/transformations/quantize_tensor_test.py
+++ b/ai_edge_quantizer/transformations/quantize_tensor_test.py
@@ -170,7 +170,7 @@ def test_blockwise_quantization_with_zero_point(self):
     # Check if the scale and zero point tensors are inserted correctly.
     self.assertEqual(quant_param.details.scales, 9)
     # So far we don't have zero point in blockwise quantization.
-    self.assertEqual(quant_param.details.zeroPoints, 0)
+    self.assertEqual(quant_param.details.zeroPoints, -1)
 
   def test_int4_constant_packed_correctly(self):
     subgraph = self._model.subgraphs[0]