diff --git a/onnxruntime/python/tools/quantization/execution_providers/qnn/quant_config.py b/onnxruntime/python/tools/quantization/execution_providers/qnn/quant_config.py
index 9384bab13f3cc..ea995d4707ba3 100644
--- a/onnxruntime/python/tools/quantization/execution_providers/qnn/quant_config.py
+++ b/onnxruntime/python/tools/quantization/execution_providers/qnn/quant_config.py
@@ -55,6 +55,7 @@ def get_qnn_qdq_config(
     stride: int | None = None,
     calibration_providers: list[str] | None = None,
     op_types_to_quantize: list[str] | None = None,
+    nodes_to_exclude: list[str] | None = None,
 ) -> StaticQuantConfig:
     """
     Returns a static quantization configuration suitable for running QDQ models on QNN EP.
@@ -122,6 +123,8 @@ def get_qnn_qdq_config(
         calibration_providers: Execution providers to run the session during calibration. Default is None which uses
             [ "CPUExecutionProvider" ].
         op_types_to_quantize: If set to None, all operator types will be quantized except for OP_TYPES_TO_EXCLUDE
+        nodes_to_exclude: List of nodes names to exclude from quantization. The nodes in this list will be excluded from
+            quantization when it is not None.
 
     Returns:
         A StaticQuantConfig object
@@ -167,10 +170,13 @@ def get_qnn_qdq_config(
     )
 
     op_types_to_quantize_set = set(op_types_to_quantize) if op_types_to_quantize else None
+    nodes_to_exclude_set = set(nodes_to_exclude) if nodes_to_exclude else None
 
     for node in model.graph.node:
         if op_types_to_quantize_set and node.op_type not in op_types_to_quantize_set:
             continue
+        if nodes_to_exclude_set and node.name in nodes_to_exclude_set:
+            continue
         op_types.add(node.op_type)
         qnn_compat.process_node(node)
 
@@ -201,6 +207,7 @@ def get_qnn_qdq_config(
         op_types_to_quantize=op_types_to_quantize
         if op_types_to_quantize
         else list(op_types.difference(OP_TYPES_TO_EXCLUDE)),
+        nodes_to_exclude=nodes_to_exclude,
         per_channel=per_channel,
         use_external_data_format=(model_has_external_data or model.ByteSize() >= MODEL_SIZE_THRESHOLD),
         calibration_providers=calibration_providers,