diff --git a/src/awkward/operations/ak_from_raggedtensor.py b/src/awkward/operations/ak_from_raggedtensor.py
index 1c895506c2..69c3558281 100644
--- a/src/awkward/operations/ak_from_raggedtensor.py
+++ b/src/awkward/operations/ak_from_raggedtensor.py
@@ -28,20 +28,43 @@ def from_raggedtensor(array):
 
 
 def _impl(array):
+    try:
+        import tensorflow as tf
+    except ImportError as err:
+        raise ImportError(
+            """to use ak.from_raggedtensor, you must install the 'tensorflow' package with:
+
+        pip install tensorflow
+or
+        conda install tensorflow"""
+        ) from err
+
     try:
         # get the flat values
-        content = array.flat_values.numpy()
+        content = array.flat_values
     except AttributeError as err:
         raise TypeError(
             """only RaggedTensor can be converted to awkward array"""
         ) from err
-    # convert them to ak.contents right away
+
+    # handle gpu and cpu instances separately
+    device = content.device
+
+    # since TensorFlow currently does not support
+    # int32 variables being placed on the GPU, use CPU for them instead
+    if content.dtype == tf.int32:
+        device = "cpu"
+
+    content = _tensor_to_np_or_cp(content, device)
+
+    # convert flat_values to ak.contents right away
     content = ak.contents.NumpyArray(content)
 
     # get the offsets
     offsets_arr = []
     for splits in array.nested_row_splits:
-        split = splits.numpy()
+        # handle gpu and cpu instances separately
+        split = _tensor_to_np_or_cp(splits, device)
         # convert to ak.index
         offset = ak.index.Index64(split)
         offsets_arr.append(offset)
@@ -55,6 +78,18 @@ def _impl(array):
     return ak.Array(_recursive_call(content, offsets_arr, 0))
 
 
+def _tensor_to_np_or_cp(array, device):
+    import tensorflow as tf
+
+    if "GPU" in device:
+        from awkward._nplikes.cupy import Cupy
+
+        cp = Cupy.instance()
+        return cp.from_dlpack(tf.experimental.dlpack.to_dlpack(array))
+    else:
+        return array.numpy()
+
+
 def _recursive_call(content, offsets_arr, count):
     if count == len(offsets_arr) - 2:
         return ak.contents.ListOffsetArray(