@@ -30,6 +30,12 @@ jobs:
         uses: astral-sh/setup-uv@v5
         with:
           enable-cache: true
+      - name: Install torch (platform-specific)
+        run: |
+          if [[ "$RUNNER_OS" == "Linux" ]]; then
+            pip install torch==2.2.2+cpu torchvision==0.17.2+cpu \
+              -f https://download.pytorch.org/whl/torch_stable.html
+          fi
       - name: Install dependencies
         run: make install-python-dependencies-ci
       - name: Test Python
 
@@ -19,6 +19,7 @@
     TYPE_CHECKING,
     Any,
     Callable,
+    Dict,
     Iterable,
     List,
     Optional,
@@ -38,6 +39,7 @@
 from feast.on_demand_feature_view import OnDemandFeatureView
 from feast.repo_config import RepoConfig
 from feast.saved_dataset import SavedDatasetStorage
+from feast.torch_wrapper import get_torch
 
 if TYPE_CHECKING:
     from feast.saved_dataset import ValidationReference
@@ -137,6 +139,40 @@ def to_arrow(
 
         return features_table
 
+    def to_tensor(
+        self,
+        kind: str = "torch",
+        default_value: Any = float("nan"),
+        timeout: Optional[int] = None,
+    ) -> Dict[str, Any]:
+        """
+        Converts historical features into a dictionary of 1D torch tensors or lists (for non-numeric types).
+
+        Args:
+            kind: "torch" (default and only supported kind).
+            default_value: Value to replace missing (None or NaN) entries.
+            timeout: Optional timeout for query execution.
+
+        Returns:
+            Dict[str, Union[torch.Tensor, List]]: Feature column name -> tensor or list.
+        """
+        if kind != "torch":
+            raise ValueError(
+                f"Unsupported tensor kind: {kind}. Only 'torch' is supported."
+            )
+        torch = get_torch()
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        df = self.to_df(timeout=timeout)
+        tensor_dict = {}
+        for column in df.columns:
+            values = df[column].fillna(default_value).tolist()
+            first_non_null = next((v for v in values if v is not None), None)
+            if isinstance(first_non_null, (int, float, bool)):
+                tensor_dict[column] = torch.tensor(values, device=device)
+            else:
+                tensor_dict[column] = values
+        return tensor_dict
+
     def to_sql(self) -> str:
         """
         Return RetrievalJob generated SQL statement if applicable.
 
@@ -12,15 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, List
+from typing import TYPE_CHECKING, Any, Dict, List, Union
 
 import pandas as pd
 import pyarrow as pa
 
 from feast.feature_view import DUMMY_ENTITY_ID
 from feast.protos.feast.serving.ServingService_pb2 import GetOnlineFeaturesResponse
+from feast.torch_wrapper import get_torch
 from feast.type_map import feast_value_type_to_python_type
 
+if TYPE_CHECKING:
+    import torch
+
+    TorchTensor = torch.Tensor
+else:
+    TorchTensor = Any
+
 TIMESTAMP_POSTFIX: str = "__ts"
 
 
@@ -88,3 +96,47 @@ def to_arrow(self, include_event_timestamps: bool = False) -> pa.Table:
         """
 
         return pa.Table.from_pydict(self.to_dict(include_event_timestamps))
+
+    def to_tensor(
+        self,
+        kind: str = "torch",
+        default_value: Any = float("nan"),
+    ) -> Dict[str, Union[TorchTensor, List[Any]]]:
+        """
+        Converts GetOnlineFeaturesResponse features into a dictionary of tensors or lists.
+
+        - Numeric features (int, float, bool) -> torch.Tensor
+        - Non-numeric features (e.g., strings) -> list[Any]
+
+        Args:
+            kind: Backend tensor type. Currently only "torch" is supported.
+            default_value: Value to substitute for missing (None) entries.
+
+        Returns:
+            Dict[str, Union[torch.Tensor, List[Any]]]: Mapping of feature names to tensors or lists.
+        """
+        if kind != "torch":
+            raise ValueError(
+                f"Unsupported tensor kind: {kind}. Only 'torch' is supported currently."
+            )
+        torch = get_torch()
+        feature_dict = self.to_dict(include_event_timestamps=False)
+        feature_keys = set(self.proto.metadata.feature_names.val)
+        tensor_dict: Dict[str, Union[TorchTensor, List[Any]]] = {}
+        for key in feature_keys:
+            raw_values = feature_dict[key]
+            values = [v if v is not None else default_value for v in raw_values]
+            first_valid = next((v for v in values if v is not None), None)
+            if isinstance(first_valid, (int, float, bool)):
+                try:
+                    device = "cuda" if torch.cuda.is_available() else "cpu"
+                    tensor_dict[key] = torch.tensor(values, device=device)
+                except Exception as e:
+                    raise ValueError(
+                        f"Failed to convert values for '{key}' to tensor: {e}"
+                    )
+            else:
+                tensor_dict[key] = (
+                    values  # Return as-is for strings or unsupported types
+                )
+        return tensor_dict
@@ -0,0 +1,39 @@
+import importlib
+
+TORCH_AVAILABLE = False
+_torch = None
+_torch_import_error = None
+
+
+def _import_torch():
+    global _torch, TORCH_AVAILABLE, _torch_import_error
+    try:
+        _torch = importlib.import_module("torch")
+        TORCH_AVAILABLE = True
+    except Exception as e:
+        # Catch import errors including CUDA lib missing
+        TORCH_AVAILABLE = False
+        _torch_import_error = e
+
+
+_import_torch()
+
+
+def get_torch():
+    """
+    Return the torch module if available, else raise a friendly error.
+
+    This prevents crashing on import if CUDA libs are missing.
+    """
+    if TORCH_AVAILABLE:
+        return _torch
+    else:
+        error_message = (
+            "Torch is not available or failed to import.\n"
+            "Original error:\n"
+            f"{_torch_import_error}\n\n"
+            "If you are on a CPU-only system, make sure you install the CPU-only torch wheel:\n"
+            "  pip install torch==2.2.2+cpu torchvision==0.17.2+cpu -f https://download.pytorch.org/whl/torch_stable.html\n"
+            "Or check your CUDA installation if using GPU torch.\n"
+        )
+        raise ImportError(error_message) from _torch_import_error
@@ -18,6 +18,7 @@
 from feast.protos.feast.types.Value_pb2 import FloatList as FloatListProto
 from feast.protos.feast.types.Value_pb2 import Value as ValueProto
 from feast.repo_config import RegistryConfig
+from feast.torch_wrapper import get_torch
 from feast.types import ValueType
 from feast.utils import _utc_now
 from tests.integration.feature_repos.universal.feature_views import TAGS
@@ -129,6 +130,38 @@ def test_get_online_features() -> None:
         assert result["name"] == ["John", "John"]
         assert result["trips"] == [7, 7]
 
+        tensor_result = store.get_online_features(
+            features=[
+                "driver_locations:lon",
+                "customer_profile:avg_orders_day",
+                "customer_profile:name",
+                "customer_driver_combined:trips",
+            ],
+            entity_rows=[
+                {"driver_id": 1, "customer_id": "5"},
+                {"driver_id": 1, "customer_id": 5},
+            ],
+            full_feature_names=False,
+        ).to_tensor()
+
+        assert "lon" in tensor_result
+        assert "avg_orders_day" in tensor_result
+        assert "name" in tensor_result
+        assert "trips" in tensor_result
+        # Entity values
+        torch = get_torch()
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        assert torch.equal(
+            tensor_result["driver_id"], torch.tensor([1, 1], device=device)
+        )
+        assert tensor_result["customer_id"] == ["5", "5"]
+
+        # Feature values
+        assert tensor_result["lon"] == ["1.0", "1.0"]  # String -> not tensor
+        assert torch.equal(tensor_result["avg_orders_day"], torch.tensor([1.0, 1.0]))
+        assert tensor_result["name"] == ["John", "John"]
+        assert torch.equal(tensor_result["trips"], torch.tensor([7, 7], device=device))
+
         # Ensure features are still in result when keys not found
         result = store.get_online_features(
             features=["customer_driver_combined:trips"],
@@ -138,6 +171,15 @@ def test_get_online_features() -> None:
 
         assert "trips" in result
 
+        result = store.get_online_features(
+            features=["customer_driver_combined:trips"],
+            entity_rows=[{"driver_id": 0, "customer_id": 0}],
+            full_feature_names=False,
+        ).to_tensor()
+
+        assert "trips" in result
+        assert isinstance(result["trips"], torch.Tensor)
+
         with pytest.raises(KeyError) as excinfo:
             _ = store.get_online_features(
                 features=["driver_locations:lon"],
 
@@ -17,6 +17,7 @@
 )
 from feast.offline_server import OfflineServer, _init_auth_manager
 from feast.repo_config import RepoConfig
+from feast.torch_wrapper import get_torch
 from tests.utils.cli_repo_creator import CliRunner
 
 PROJECT_NAME = "test_remote_offline"
@@ -115,7 +116,9 @@ def test_remote_offline_store_apis():
         fs = remote_feature_store(server)
 
         _test_get_historical_features_returns_data(fs)
+        _test_get_historical_features_to_tensor(fs)
         _test_get_historical_features_returns_nan(fs)
+        _test_get_historical_features_to_tensor_with_nan(fs)
         _test_offline_write_batch(str(temp_dir), fs)
         _test_write_logged_features(str(temp_dir), fs)
         _test_pull_latest_from_table_or_query(str(temp_dir), fs)
@@ -187,6 +190,44 @@ def _test_get_historical_features_returns_data(fs: FeatureStore):
             assertpy.assert_that(value).is_not_nan()
 
 
+def _test_get_historical_features_to_tensor(fs: FeatureStore):
+    entity_df = pd.DataFrame.from_dict(
+        {
+            "driver_id": [1001, 1002, 1003],
+            "event_timestamp": [
+                datetime(2021, 4, 12, 10, 59, 42),
+                datetime(2021, 4, 12, 8, 12, 10),
+                datetime(2021, 4, 12, 16, 40, 26),
+            ],
+            "label_driver_reported_satisfaction": [1, 5, 3],
+            "val_to_add": [1, 2, 3],
+            "val_to_add_2": [10, 20, 30],
+        }
+    )
+
+    features = [
+        "driver_hourly_stats:conv_rate",
+        "driver_hourly_stats:acc_rate",
+        "driver_hourly_stats:avg_daily_trips",
+        "transformed_conv_rate:conv_rate_plus_val1",
+        "transformed_conv_rate:conv_rate_plus_val2",
+    ]
+
+    job = fs.get_historical_features(entity_df, features)
+    tensor_data = job.to_tensor()
+
+    assertpy.assert_that(tensor_data).is_not_none()
+    assertpy.assert_that(tensor_data["driver_id"].shape[0]).is_equal_to(3)
+    torch = get_torch()
+    for key, values in tensor_data.items():
+        if isinstance(values, torch.Tensor):
+            assertpy.assert_that(values.shape[0]).is_equal_to(3)
+            for val in values:
+                val_float = val.item()
+                assertpy.assert_that(val_float).is_instance_of((float, int))
+                assertpy.assert_that(val_float).is_not_nan()
+
+
 def _test_get_historical_features_returns_nan(fs: FeatureStore):
     entity_df = pd.DataFrame.from_dict(
         {
@@ -223,6 +264,28 @@ def _test_get_historical_features_returns_nan(fs: FeatureStore):
             assertpy.assert_that(value).is_nan()
 
 
+def _test_get_historical_features_to_tensor_with_nan(fs: FeatureStore):
+    entity_df = pd.DataFrame.from_dict(
+        {
+            "driver_id": [9991, 9992],  # IDs with no matching features
+            "event_timestamp": [
+                datetime(2021, 4, 12, 10, 59, 42),
+                datetime(2021, 4, 12, 10, 59, 42),
+            ],
+        }
+    )
+    features = ["driver_hourly_stats:conv_rate"]
+    job = fs.get_historical_features(entity_df, features)
+    tensor_data = job.to_tensor()
+    assert "conv_rate" in tensor_data
+    values = tensor_data["conv_rate"]
+    # conv_rate is a float feature, missing values should be NaN
+    torch = get_torch()
+    for val in values:
+        assert isinstance(val, torch.Tensor) or torch.is_tensor(val)
+        assertpy.assert_that(torch.isnan(val).item()).is_true()
+
+
 def _test_offline_write_batch(temp_dir, fs: FeatureStore):
     data_file = os.path.join(
         temp_dir, fs.project, "feature_repo/data/driver_stats.parquet"