diff --git a/onnxruntime/python/tools/quantization/qdq_quantizer.py b/onnxruntime/python/tools/quantization/qdq_quantizer.py
index ec1b9db3dfaaf..a970e72aa1f86 100644
--- a/onnxruntime/python/tools/quantization/qdq_quantizer.py
+++ b/onnxruntime/python/tools/quantization/qdq_quantizer.py
@@ -97,6 +97,13 @@ def __init__(
             False if "AddQDQPairToWeight" not in extra_options else extra_options["AddQDQPairToWeight"]
         )
 
+        # Some scenarios do not need the bias quantized. For example, in the case of Quantization Aware Training,
+        # quantizing the bias is not needed. This is because in QAT, all model parameters are expected to be in
+        # floating point format. To that end, we can use the FakeQuant operator for weights and activations that
+        # can always have QDQ pairs (by using AddQDQPairToWeight). But for biases in a quantized model, we can't use
+        # FakeQuant because it only ever appears before a DQ (since it is quantized as int32).
+        self.quantize_bias = True if "QuantizeBias" not in extra_options else extra_options["QuantizeBias"]
+
         # The default behavior is that multiple nodes can share a QDQ pair as their inputs.
         # In TRT, QDQ pair can’t be shared between nodes, so it will create dedicated QDQ pairs for each node.
         self.dedicated_qdq_pair = (
@@ -211,7 +218,8 @@ def quantize_model(self):
 
         self._quantize_normal_tensors()
         self._quantize_sharing_param_tensors()
-        self._quantize_bias_tensors()
+        if self.quantize_bias:
+            self._quantize_bias_tensors()
         self.remove_nodes()
         if not self.add_qdq_pair_to_weight:
             self.model.clean_initializers()
diff --git a/onnxruntime/python/tools/quantization/quantize.py b/onnxruntime/python/tools/quantization/quantize.py
index 80ae592d49071..1d4aae268f79e 100644
--- a/onnxruntime/python/tools/quantization/quantize.py
+++ b/onnxruntime/python/tools/quantization/quantize.py
@@ -139,6 +139,11 @@ def __init__(
                         Default is 0.01. Constant smoothing factor to use when computing the moving average of the
                         minimum and maximum values. Effective only when the calibration method selected is MinMax and
                         when CalibMovingAverage is set to True.
+                    QuantizeBias = True/False :
+                        Default is True which quantizes floating-point biases and it solely inserts
+                        a DeQuantizeLinear node. If False, it remains floating-point bias and does not insert
+                        any quantization nodes associated with biases.
+                        This extra option is only effective when quant_format is QuantFormat.QDQ.
             execution_provider : A enum indicates the Execution Provider such as: CPU, TRT, NNAPI, SNE, etc.
         Raises:
             ValueError: Raise ValueError if execution provider is unknown