diff --git a/src/transformers/generation/candidate_generator.py b/src/transformers/generation/candidate_generator.py
index ad5289f120ea19..e28615bb3c2621 100644
--- a/src/transformers/generation/candidate_generator.py
+++ b/src/transformers/generation/candidate_generator.py
@@ -169,6 +169,8 @@ def get_candidates(self, input_ids: torch.LongTensor) -> Tuple[torch.LongTensor,
             assessed by the model and a `torch.FloatTensor` of shape `(batch_size, candidate_length,
             vocabulary_size)` containing the logits associated to each candidate.
         """
+        input_ids = input_ids.to(self.assistant_model.device)
+
         # 1. If it is not the first round of candidate generation, prepare the inputs based on the input_ids length
         # (which implicitly contains the number of accepted candidates from the previous round)
         has_past_key_values = self.assistant_kwargs.get("past_key_values", None) is not None
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index ef9e19c8b11057..f36f76a27a390a 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -4591,11 +4591,10 @@ def assisted_decoding(
             cur_len = input_ids.shape[-1]
 
             #  1. Fetch candidate sequences from a `CandidateGenerator`
-            candidate_input_ids, candidate_logits = candidate_generator.get_candidates(
-                input_ids.to(candidate_generator.assistant_model.device)
-            )
+            candidate_input_ids, candidate_logits = candidate_generator.get_candidates(input_ids)
             candidate_input_ids = candidate_input_ids.to(self.device)
-            candidate_logits = candidate_logits.to(self.device)
+            if candidate_logits is not None:
+                candidate_logits = candidate_logits.to(self.device)
 
             candidate_length = candidate_input_ids.shape[1] - input_ids.shape[1]
             last_assistant_token_is_eos = (