diff --git a/python/sglang/srt/layers/rotary_embedding.py b/python/sglang/srt/layers/rotary_embedding.py
index 7cffccf6b..05f068557 100644
--- a/python/sglang/srt/layers/rotary_embedding.py
+++ b/python/sglang/srt/layers/rotary_embedding.py
@@ -1433,24 +1433,6 @@ class MRotaryEmbedding(RotaryEmbedding):
 
             return position_ids, mrope_position_deltas
 
-    @staticmethod
-    def get_next_input_positions(
-        mrope_position_delta: int,
-        context_len: int,
-        seq_len: int,
-    ) -> torch.Tensor:
-        return torch.tensor(
-            [
-                list(
-                    range(
-                        context_len + mrope_position_delta,
-                        seq_len + mrope_position_delta,
-                    )
-                )
-                for _ in range(3)
-            ]
-        )
-
 
 class DualChunkRotaryEmbedding(CustomOp):
     """Rotary positional embedding for Dual Chunk Attention."""
diff --git a/python/sglang/srt/model_executor/forward_batch_info.py b/python/sglang/srt/model_executor/forward_batch_info.py
index 65c0a07f8..8904e89f1 100644
--- a/python/sglang/srt/model_executor/forward_batch_info.py
+++ b/python/sglang/srt/model_executor/forward_batch_info.py
@@ -516,24 +516,23 @@ class ForwardBatch:
         for batch_idx in range(batch_size):
             mm_input = batch.multimodal_inputs[batch_idx]
             if self.forward_mode.is_decode():
-                mrope_position_deltas = (
-                    [0]
-                    if mm_input is None
-                    else flatten_nested_list(mm_input.mrope_position_delta.tolist())
-                )
-                next_input_positions = []
-                for mrope_position_delta in mrope_position_deltas:
-                    # batched deltas needs to be processed separately
-                    # Convert list of lists to tensor with shape [3, seq_len]
-                    next_input_positions += [
-                        MRotaryEmbedding.get_next_input_positions(
-                            mrope_position_delta,
-                            int(self.seq_lens[batch_idx]) - 1,
-                            int(self.seq_lens[batch_idx]),
-                        )
-                    ]
                 # 3 * N
-                mrope_positions_list[batch_idx] = torch.cat(next_input_positions, dim=1)
+                if mm_input is None:
+                    mrope_positions_list[batch_idx] = torch.full(
+                        (3, 1),
+                        self.seq_lens[batch_idx] - 1,
+                        dtype=torch.int64,
+                        device=model_runner.device,
+                    )
+                else:
+                    mrope_position_deltas = mm_input.mrope_position_delta.flatten().to(
+                        model_runner.device, non_blocking=True
+                    )
+                    mrope_positions_list[batch_idx] = (
+                        (mrope_position_deltas + self.seq_lens[batch_idx] - 1)
+                        .unsqueeze(0)
+                        .repeat(3, 1)
+                    )
             elif self.forward_mode.is_extend():
                 extend_seq_len, extend_prefix_len = (
                     batch.extend_seq_lens[batch_idx],