diff --git a/backends/test/suite/operators/test_linear.py b/backends/test/suite/operators/test_linear.py
new file mode 100644
index 00000000000..30ae963a1ba
--- /dev/null
+++ b/backends/test/suite/operators/test_linear.py
@@ -0,0 +1,124 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+
+import torch
+from executorch.backends.test.suite.flow import TestFlow
+
+from executorch.backends.test.suite.operators import (
+    dtype_test,
+    operator_test,
+    OperatorTest,
+)
+
+
+class Model(torch.nn.Module):
+    def __init__(
+        self,
+        in_features=67,
+        out_features=43,
+        bias=True,
+    ):
+        super().__init__()
+        self.linear = torch.nn.Linear(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+@operator_test
+class Linear(OperatorTest):
+    @dtype_test
+    def test_linear_dtype(self, flow: TestFlow, dtype) -> None:
+        self._test_op(
+            Model().to(dtype),
+            ((torch.rand(16, 64) * 10).to(dtype),),
+            flow,
+        )
+
+    @dtype_test
+    def test_linear_no_bias_dtype(self, flow: TestFlow, dtype) -> None:
+        self._test_op(
+            Model(bias=False).to(dtype),
+            ((torch.rand(16, 64) * 10).to(dtype),),
+            flow,
+        )
+
+    def test_linear_feature_sizes(self, flow: TestFlow) -> None:
+        self._test_op(
+            Model(in_features=32, out_features=16),
+            (torch.randn(20, 32),),
+            flow,
+        )
+        self._test_op(
+            Model(in_features=128, out_features=64),
+            (torch.randn(8, 128),),
+            flow,
+        )
+        self._test_op(
+            Model(in_features=256, out_features=1),
+            (torch.randn(4, 256),),
+            flow,
+        )
+        self._test_op(
+            Model(in_features=1, out_features=512),
+            (torch.randn(1024, 1),),
+            flow,
+        )
+
+    def test_linear_no_bias(self, flow: TestFlow) -> None:
+        self._test_op(
+            Model(bias=False),
+            (torch.randn(16, 64),),
+            flow,
+        )
+        self._test_op(
+            Model(in_features=128, out_features=96, bias=False),
+            (torch.randn(8, 128),),
+            flow,
+        )
+
+    def test_linear_batch_sizes(self, flow: TestFlow) -> None:
+        self._test_op(
+            Model(),
+            (torch.randn(8, 64),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(32, 64),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(100, 64),),
+            flow,
+        )
+
+    def test_linear_unbatched(self, flow: TestFlow) -> None:
+        self._test_op(
+            Model(in_features=512),
+            (torch.randn(512),),
+            flow,
+        )
+
+    def test_linear_leading_batch(self, flow: TestFlow) -> None:
+        self._test_op(
+            Model(),
+            (torch.randn(4, 8, 64),),
+            flow,
+        )
+        self._test_op(
+            Model(),
+            (torch.randn(2, 4, 8, 64),),
+            flow,
+        )