diff --git a/sagemaker-core/pyproject.toml b/sagemaker-core/pyproject.toml
index 2756ce0f1c..c0656ab16a 100644
--- a/sagemaker-core/pyproject.toml
+++ b/sagemaker-core/pyproject.toml
@@ -32,7 +32,6 @@ dependencies = [
     "smdebug_rulesconfig>=1.0.1",
     "schema>=0.7.5",
     "omegaconf>=2.1.0",
-    "torch>=1.9.0",
     "scipy>=1.5.0",
     # Remote function dependencies
     "cloudpickle>=2.0.0",
@@ -51,6 +50,12 @@ classifiers = [
 ]
 
 [project.optional-dependencies]
+torch = [
+    "torch>=1.9.0",
+]
+all = [
+    "sagemaker-core[torch]",
+]
 codegen = [
     "black>=24.3.0, <25.0.0",
     "pandas>=2.0.0, <3.0.0",
diff --git a/sagemaker-core/src/sagemaker/core/deserializers/base.py b/sagemaker-core/src/sagemaker/core/deserializers/base.py
index 4faae7db74..03138ed577 100644
--- a/sagemaker-core/src/sagemaker/core/deserializers/base.py
+++ b/sagemaker-core/src/sagemaker/core/deserializers/base.py
@@ -365,8 +365,11 @@ def __init__(self, accept="tensor/pt"):
             from torch import from_numpy
 
             self.convert_npy_to_tensor = from_numpy
-        except ImportError:
-            raise Exception("Unable to import pytorch.")
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import torch. Please install torch to use TorchTensorDeserializer: "
+                "pip install 'sagemaker-core[torch]'"
+            ) from e
 
     def deserialize(self, stream, content_type="tensor/pt"):
         """Deserialize streamed data to TorchTensor
diff --git a/sagemaker-core/src/sagemaker/core/serializers/base.py b/sagemaker-core/src/sagemaker/core/serializers/base.py
index a4ecf7c1dc..e8862b66f3 100644
--- a/sagemaker-core/src/sagemaker/core/serializers/base.py
+++ b/sagemaker-core/src/sagemaker/core/serializers/base.py
@@ -443,7 +443,13 @@ class TorchTensorSerializer(SimpleBaseSerializer):
 
     def __init__(self, content_type="tensor/pt"):
         super(TorchTensorSerializer, self).__init__(content_type=content_type)
-        from torch import Tensor
+        try:
+            from torch import Tensor
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import torch. Please install torch to use TorchTensorSerializer: "
+                "pip install 'sagemaker-core[torch]'"
+            ) from e
 
         self.torch_tensor = Tensor
         self.numpy_serializer = NumpySerializer()
diff --git a/sagemaker-core/tests/unit/test_optional_torch_dependency.py b/sagemaker-core/tests/unit/test_optional_torch_dependency.py
new file mode 100644
index 0000000000..5008244e27
--- /dev/null
+++ b/sagemaker-core/tests/unit/test_optional_torch_dependency.py
@@ -0,0 +1,152 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Tests to verify torch dependency is optional in sagemaker-core."""
+from __future__ import annotations
+
+import importlib
+import io
+import sys
+
+import numpy as np
+import pytest
+
+
+def _block_torch():
+    """Block torch imports by setting sys.modules['torch'] to None.
+
+    Returns a dict of saved torch submodule entries so they can be restored.
+    """
+    saved = {}
+    torch_keys = [key for key in sys.modules if key.startswith("torch.")]
+    saved = {key: sys.modules.pop(key) for key in torch_keys}
+    saved["torch"] = sys.modules.get("torch")
+    sys.modules["torch"] = None
+    return saved
+
+
+def _restore_torch(saved):
+    """Restore torch modules from saved dict."""
+    original_torch = saved.pop("torch", None)
+    if original_torch is not None:
+        sys.modules["torch"] = original_torch
+    elif "torch" in sys.modules:
+        del sys.modules["torch"]
+    for key, val in saved.items():
+        sys.modules[key] = val
+
+
+def test_serializer_module_imports_without_torch():
+    """Verify that importing non-torch serializers succeeds without torch installed."""
+    saved = {}
+    try:
+        saved = _block_torch()
+
+        # Reload the module so it re-evaluates imports with torch blocked
+        import sagemaker.core.serializers.base as ser_module
+
+        importlib.reload(ser_module)
+
+        # Verify non-torch serializers can be instantiated
+        assert ser_module.CSVSerializer() is not None
+        assert ser_module.NumpySerializer() is not None
+        assert ser_module.JSONSerializer() is not None
+        assert ser_module.IdentitySerializer() is not None
+    finally:
+        _restore_torch(saved)
+
+
+def test_deserializer_module_imports_without_torch():
+    """Verify that importing non-torch deserializers succeeds without torch installed."""
+    saved = {}
+    try:
+        saved = _block_torch()
+
+        import sagemaker.core.deserializers.base as deser_module
+
+        importlib.reload(deser_module)
+
+        # Verify non-torch deserializers can be instantiated
+        assert deser_module.StringDeserializer() is not None
+        assert deser_module.BytesDeserializer() is not None
+        assert deser_module.CSVDeserializer() is not None
+        assert deser_module.NumpyDeserializer() is not None
+        assert deser_module.JSONDeserializer() is not None
+    finally:
+        _restore_torch(saved)
+
+
+def test_torch_tensor_serializer_raises_import_error_without_torch():
+    """Verify TorchTensorSerializer raises ImportError when torch is not installed."""
+    import sagemaker.core.serializers.base as ser_module
+
+    saved = {}
+    try:
+        saved = _block_torch()
+
+        with pytest.raises(ImportError, match="Unable to import torch"):
+            ser_module.TorchTensorSerializer()
+    finally:
+        _restore_torch(saved)
+
+
+def test_torch_tensor_deserializer_raises_import_error_without_torch():
+    """Verify TorchTensorDeserializer raises ImportError when torch is not installed."""
+    import sagemaker.core.deserializers.base as deser_module
+
+    saved = {}
+    try:
+        saved = _block_torch()
+
+        with pytest.raises(ImportError, match="Unable to import torch"):
+            deser_module.TorchTensorDeserializer()
+    finally:
+        _restore_torch(saved)
+
+
+def test_torch_tensor_serializer_works_with_torch():
+    """Verify TorchTensorSerializer works when torch is available."""
+    try:
+        import torch
+    except ImportError:
+        pytest.skip("torch is not installed")
+
+    from sagemaker.core.serializers.base import TorchTensorSerializer
+
+    serializer = TorchTensorSerializer()
+    tensor = torch.tensor([1.0, 2.0, 3.0])
+    result = serializer.serialize(tensor)
+    assert result is not None
+    # Verify the result can be loaded back as numpy
+    array = np.load(io.BytesIO(result))
+    assert np.array_equal(array, np.array([1.0, 2.0, 3.0]))
+
+
+def test_torch_tensor_deserializer_works_with_torch():
+    """Verify TorchTensorDeserializer works when torch is available."""
+    try:
+        import torch
+    except ImportError:
+        pytest.skip("torch is not installed")
+
+    from sagemaker.core.deserializers.base import TorchTensorDeserializer
+
+    deserializer = TorchTensorDeserializer()
+    # Create a numpy array, save it, and deserialize to tensor
+    array = np.array([1.0, 2.0, 3.0])
+    buffer = io.BytesIO()
+    np.save(buffer, array)
+    buffer.seek(0)
+
+    result = deserializer.deserialize(buffer, "tensor/pt")
+    assert isinstance(result, torch.Tensor)
+    assert torch.equal(result, torch.tensor([1.0, 2.0, 3.0]))