diff --git a/backends/apple/metal/tests/test_modules.py b/backends/apple/metal/tests/test_modules.py
index 4908b2e0ffc..b3f592fa509 100644
--- a/backends/apple/metal/tests/test_modules.py
+++ b/backends/apple/metal/tests/test_modules.py
@@ -24,22 +24,17 @@
 
 import numpy as np
 import torch
+
+# Need to import to load the torchao metal ops
+import torchao.experimental.ops.mps  # noqa: F401
+
 from executorch.backends.apple.metal.metal_backend import MetalBackend
 from executorch.backends.apple.metal.metal_partitioner import MetalPartitioner
 from executorch.exir import to_edge_transform_and_lower
 from torch import nn
 from torch.export import export
 from torch.nn.attention import SDPBackend
-
-try:
-    # Need to import to load the ops
-    import torchao.experimental.ops.mps  # noqa: F401
-    from torchao.experimental.quant_api import UIntxWeightOnlyConfig
-    from torchao.quantization.quant_api import quantize_
-
-    TORCHAO_AVAILABLE = True
-except ImportError:
-    TORCHAO_AVAILABLE = False
+from torchao.experimental.quant_api import quantize_, UIntxWeightOnlyConfig
 
 
 # Check if MPS is available for export tests
@@ -245,7 +240,6 @@ def forward(self, x: torch.Tensor):
     "rtol_float32": 5e-2,
     "atol_bfloat16": 1e-1,
     "rtol_bfloat16": 1e-1,
-    "skip": not TORCHAO_AVAILABLE,
 }
 
 
@@ -270,7 +264,6 @@ def forward(self, x: torch.Tensor):
     "rtol_float32": 5e-2,
     "atol_bfloat16": 1e-1,
     "rtol_bfloat16": 1e-1,
-    "skip": not TORCHAO_AVAILABLE,
 }
 
 
@@ -295,7 +288,6 @@ def forward(self, x: torch.Tensor):
     "rtol_float32": 5e-2,
     "atol_bfloat16": 1e-1,
     "rtol_bfloat16": 1e-1,
-    "skip": not TORCHAO_AVAILABLE,
 }
 
 
@@ -320,7 +312,6 @@ def forward(self, x: torch.Tensor):
     "rtol_float32": 5e-2,
     "atol_bfloat16": 1e-1,
     "rtol_bfloat16": 1e-1,
-    "skip": not TORCHAO_AVAILABLE,
 }
 
 
@@ -345,7 +336,6 @@ def forward(self, x: torch.Tensor):
     "rtol_float32": 5e-2,
     "atol_bfloat16": 1e-1,
     "rtol_bfloat16": 1e-1,
-    "skip": not TORCHAO_AVAILABLE,
 }
 
 
@@ -697,11 +687,6 @@ def quantize_model(model: nn.Module, qlinear: str, qlinear_group_size: int = 32)
             - "fpa4w": Floating point activation, 4-bit weight (Metal backend)
         qlinear_group_size: Group size for quantization (default: 32).
     """
-    if not TORCHAO_AVAILABLE:
-        raise RuntimeError(
-            "torchao is not available. Install torchao to use quantization."
-        )
-
     if qlinear == "fpa4w":
         linear_config = UIntxWeightOnlyConfig(
             group_size=qlinear_group_size,