diff --git a/backends/xnnpack/partition/config/generic_node_configs.py b/backends/xnnpack/partition/config/generic_node_configs.py
index e7e298053c6..5875e35cdd7 100644
--- a/backends/xnnpack/partition/config/generic_node_configs.py
+++ b/backends/xnnpack/partition/config/generic_node_configs.py
@@ -9,6 +9,7 @@
 import logging
 from typing import cast, List, Optional
 
+import numpy as np
 import torch
 from executorch.backends.xnnpack.partition.config.xnnpack_config import (
     ConfigPrecisionType,
@@ -523,6 +524,17 @@ class SubConfig(GenericNodePartitionerConfig):
     def supported_precision_types(self) -> List[ConfigPrecisionType]:
         return [ConfigPrecisionType.FP32, ConfigPrecisionType.STATIC_QUANT]
 
+    def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool:
+        if not self.check_common_constraints(node, ep):
+            return False
+        # No support for sub nodes with alpha != 1
+        if "alpha" in node.kwargs and not np.isclose(
+            node.kwargs["alpha"], 1.0, atol=1e-9, rtol=1e-9
+        ):
+            why(node, reason="Sub node doesn't support alpha != 1")
+            return False
+        return True
+
 
 class BMMConfig(GenericNodePartitionerConfig):
     """
diff --git a/backends/xnnpack/test/ops/test_sub.py b/backends/xnnpack/test/ops/test_sub.py
index 06219730ddb..de958e2186e 100644
--- a/backends/xnnpack/test/ops/test_sub.py
+++ b/backends/xnnpack/test/ops/test_sub.py
@@ -152,3 +152,27 @@ def forward(self, x, y):
             .serialize()
             .run_method_and_compare_outputs()
         )
+
+    class SubWithAlpha(torch.nn.Module):
+        def forward(self, x, y):
+            # node with alpha = 1.0 will be partitioned
+            out1 = torch.sub(x, y, alpha=1)
+            # node with alpha != 1.0 will not be partitioned
+            out2 = torch.sub(x, y, alpha=2)
+            return out1, out2
+
+    def test_add_with_alpha(self):
+        inputs = (torch.randn(1, 1, 4, 4), torch.randn(1, 1, 4, 4))
+        (
+            Tester(self.SubWithAlpha(), inputs)
+            .export()
+            .check_count({"torch.ops.aten.sub.Tensor": 2})
+            .to_edge_transform_and_lower()
+            # unpartitioned node
+            .check_count({"executorch_exir_dialects_edge__ops_aten_sub_Tensor": 1})
+            # partitioned node
+            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
+            .to_executorch()
+            .serialize()
+            .run_method_and_compare_outputs()
+        )