diff --git a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
index 898cd78ed1..c273ef12b2 100644
--- a/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
+++ b/py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
@@ -1404,7 +1404,47 @@ def aten_ops_clamp(
     )
 
 
-@dynamo_tensorrt_converter(torch.ops.aten.gather.default)
+def gather_validator(
+    node: Node, settings: Optional[CompilationSettings] = None
+) -> bool:
+    """Keep cases the TensorRT gather cannot serve on the PyTorch path.
+
+    An empty index gives the engine a zero size output binding, which TensorRT refuses to
+    enqueue. The call still returns, so every other output of that engine silently comes
+    back zeroed. An engine built for a float64 input expects float32 and rejects the
+    caller's tensor at run time unless truncate_double is set, and a uint8 output fails the
+    engine build outright. All three ran correctly in PyTorch before this converter
+    accepted dynamic shapes.
+    """
+    data_meta = (
+        node.args[0].meta.get("tensor_meta") if hasattr(node.args[0], "meta") else None
+    )
+    index_meta = (
+        node.args[2].meta.get("tensor_meta") if hasattr(node.args[2], "meta") else None
+    )
+    if index_meta is not None and 0 in tuple(index_meta.shape):
+        _LOGGER.debug("gather with an empty index is not supported, falling back")
+        return False
+    if data_meta is None:
+        return True
+    if data_meta.dtype == torch.uint8:
+        _LOGGER.debug("gather with a uint8 input is not supported, falling back")
+        return False
+    if data_meta.dtype == torch.float64 and not (
+        settings is not None and settings.truncate_double
+    ):
+        _LOGGER.debug(
+            "gather with a float64 input needs truncate_double=True, falling back"
+        )
+        return False
+    return True
+
+
+@dynamo_tensorrt_converter(
+    torch.ops.aten.gather.default,
+    capability_validator=gather_validator,
+    supports_dynamic_shapes=True,
+)
 @enforce_tensor_types(
     {
         0: (TRTTensor,),
diff --git a/tests/py/dynamo/conversion/test_gather_aten.py b/tests/py/dynamo/conversion/test_gather_aten.py
index b4cbe475b4..926b142ec7 100644
--- a/tests/py/dynamo/conversion/test_gather_aten.py
+++ b/tests/py/dynamo/conversion/test_gather_aten.py
@@ -73,3 +73,70 @@ def forward(self, input, index):
         input = torch.zeros(3, 5, dtype=torch.int32)
         inputs = [input, index]
         self.run_test(TestModule(), inputs)
+
+    @parameterized.expand(
+        [
+            ("positive_dim", 1),
+            ("negative_dim", -1),
+        ]
+    )
+    def test_gather_dynamic_shape(self, _, dim):
+        """The registry only consults supports_dynamic_shapes when a node carries symbolic
+        shape metadata, which the legacy tracer does not produce, so this needs the dynamo
+        tracer or it passes either way."""
+
+        class TestModule(torch.nn.Module):
+            def forward(self, input, index):
+                return torch.ops.aten.gather.default(input, dim, index)
+
+        input_specs = [
+            Input(
+                min_shape=(1, 5),
+                opt_shape=(3, 5),
+                max_shape=(6, 5),
+                dtype=torch.float32,
+            ),
+            Input(
+                min_shape=(1, 4),
+                opt_shape=(3, 4),
+                max_shape=(6, 4),
+                dtype=torch.int64,
+            ),
+        ]
+        self.run_test_with_dynamic_shape(
+            TestModule(),
+            input_specs,
+            use_dynamo_tracer=True,
+        )
+
+    def test_gather_dynamic_gathered_axis(self):
+        """The gathered axis itself is dynamic here, so index validity depends on the shape
+        the engine is given at run time rather than on the shape it was built at."""
+
+        class TestModule(torch.nn.Module):
+            def forward(self, input, index):
+                return torch.ops.aten.gather.default(input, 1, index)
+
+        input_specs = [
+            Input(
+                min_shape=(3, 1),
+                opt_shape=(3, 4),
+                max_shape=(3, 6),
+                dtype=torch.float32,
+            ),
+            Input(
+                min_shape=(3, 1),
+                opt_shape=(3, 4),
+                max_shape=(3, 6),
+                dtype=torch.int64,
+            ),
+        ]
+        self.run_test_with_dynamic_shape(
+            TestModule(),
+            input_specs,
+            use_dynamo_tracer=True,
+        )
+
+
+if __name__ == "__main__":
+    run_tests()