diff --git a/oodeel/methods/dknn.py b/oodeel/methods/dknn.py
index 983316f..e7d1470 100644
--- a/oodeel/methods/dknn.py
+++ b/oodeel/methods/dknn.py
@@ -38,21 +38,28 @@ class DKNN(OODBaseDetector):
     Args:
         nearest: number of nearest neighbors to consider.
             Defaults to 1.
+        use_gpu (bool): Flag to enable GPU acceleration for FAISS. Defaults to False.
     """
 
-    def __init__(
-        self,
-        nearest: int = 50,
-    ):
+    def __init__(self, nearest: int = 50, use_gpu: bool = False):
         super().__init__()
-
         self.index = None
         self.nearest = nearest
+        self.use_gpu = use_gpu
+
+        if self.use_gpu:
+            try:
+                self.res = faiss.StandardGpuResources()
+            except AttributeError as e:
+                raise ImportError(
+                    "faiss-gpu is not installed, but use_gpu was set to True."
+                    + "Please install faiss-gpu or set use_gpu to False."
+                ) from e
 
     def _fit_to_dataset(self, fit_dataset: Union[TensorType, DatasetType]) -> None:
         """
         Constructs the index from ID data "fit_dataset", which will be used for
-        nearest neighbor search.
+        nearest neighbor search. Can operate on CPU or GPU based on the `use_gpu` flag.
 
         Args:
             fit_dataset: input dataset (ID) to construct the index with.
@@ -61,7 +68,13 @@ def _fit_to_dataset(self, fit_dataset: Union[TensorType, DatasetType]) -> None:
         fit_projected = self.op.convert_to_numpy(fit_projected[0])
         fit_projected = fit_projected.reshape(fit_projected.shape[0], -1)
         norm_fit_projected = self._l2_normalization(fit_projected)
-        self.index = faiss.IndexFlatL2(norm_fit_projected.shape[1])
+
+        if self.use_gpu:
+            cpu_index = faiss.IndexFlatL2(norm_fit_projected.shape[1])
+            self.index = faiss.index_cpu_to_gpu(self.res, 0, cpu_index)
+        else:
+            self.index = faiss.IndexFlatL2(norm_fit_projected.shape[1])
+
         self.index.add(norm_fit_projected)
 
     def _score_tensor(self, inputs: TensorType) -> Tuple[np.ndarray]: