diff --git a/transformer_engine/pytorch/attention/rope.py b/transformer_engine/pytorch/attention/rope.py
index 613ecb1a7..b9102f22f 100644
--- a/transformer_engine/pytorch/attention/rope.py
+++ b/transformer_engine/pytorch/attention/rope.py
@@ -5,12 +5,32 @@
 """
 Rotary Position Embedding implementation of different types along with helper functions
 """
+import os
+import warnings
 from typing import Optional, Tuple, Union, List
 import torch
 
 import transformer_engine_torch as tex
 from transformer_engine.pytorch.cpp_extensions.fused_attn import QKVFormat
 
+# Optional AITER fused RoPE dispatch (ROCm).
+# Controlled by NVTE_USE_AITER_FUSED_ROPE (default "1" = enabled).
+_aiter_rope_fwd = None
+_aiter_rope_bwd = None
+_HAVE_AITER_ROPE = False
+_USE_AITER_FUSED_ROPE = os.environ.get("NVTE_USE_AITER_FUSED_ROPE", "1") == "1"
+
+if _USE_AITER_FUSED_ROPE:
+    try:
+        from aiter.ops.rope import rope_fwd as _aiter_rope_fwd, rope_bwd as _aiter_rope_bwd
+        _HAVE_AITER_ROPE = True
+    except Exception as e:
+        warnings.warn(
+            f"AITER fused RoPE import failed ({type(e).__name__}: {e}). "
+            f"Falling back to TE native kernels. "
+            f"Set NVTE_USE_AITER_FUSED_ROPE=0 to suppress this warning."
+        )
+
 
 __all__ = ["RotaryPositionEmbedding", "apply_rotary_pos_emb", "apply_fused_qkv_rotary_pos_emb"]
 
@@ -116,8 +136,24 @@ class FusedRoPEFunc(torch.autograd.Function):
     This implementation assumes the input tensor to be in `sbhd`, `bshd` or `thd` format and
     the RoPE tensor to be of shape (s, 1, 1, d). It accepts arbitrary memory layouts to avoid
     the expensive `.contiguous()` calls, thus it may not achieve the best memory access pattern.
+
+    When AITER is available and the configuration is compatible (sbhd, non-interleaved,
+    cp_size==1, no cu_seqlens/start_positions), the forward and backward are dispatched
+    to AITER's fused HIP kernels for higher throughput on AMD GPUs.
     """
 
+    @staticmethod
+    def _can_use_aiter(tensor_format, interleaved, cu_seqlens, cp_size, start_positions):
+        """Return True when AITER fused rope kernels can handle this configuration."""
+        return (
+            _HAVE_AITER_ROPE
+            and tensor_format == "sbhd"
+            and not interleaved
+            and cu_seqlens is None
+            and cp_size == 1
+            and start_positions is None
+        )
+
     @staticmethod
     def forward(
         ctx,
@@ -139,21 +175,36 @@ def forward(
             "bshd",
             "thd",
         ), f"Unsupported tensor_format: {tensor_format}."
-        output = tex.fused_rope_forward(
-            t,
-            freqs,
-            start_positions,
-            QKVFormat[tensor_format],
-            interleaved,
-            cu_seqlens,
-            cp_size,
-            cp_rank,
+
+        use_aiter = FusedRoPEFunc._can_use_aiter(
+            tensor_format, interleaved, cu_seqlens, cp_size, start_positions
         )
+
+        if use_aiter:
+            rotate_style = 1 if interleaved else 0
+            output = _aiter_rope_fwd(
+                t, freqs, rotate_style,
+                False,  # reuse_freqs_front_part
+                False,  # nope_first
+            )
+        else:
+            output = tex.fused_rope_forward(
+                t,
+                freqs,
+                start_positions,
+                QKVFormat[tensor_format],
+                interleaved,
+                cu_seqlens,
+                cp_size,
+                cp_rank,
+            )
+
         ctx.save_for_backward(freqs, cu_seqlens, start_positions)
         ctx.tensor_format = tensor_format
         ctx.cp_size = cp_size
         ctx.cp_rank = cp_rank
         ctx.interleaved = interleaved
+        ctx.use_aiter = use_aiter
 
         return output
 
@@ -161,16 +212,25 @@ def forward(
     def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
         """Fused RoPE backward."""
         freqs, cu_seqlens, start_positions = ctx.saved_tensors
-        grad_input = tex.fused_rope_backward(
-            grad_output,
-            freqs,
-            start_positions,
-            QKVFormat[ctx.tensor_format],
-            ctx.interleaved,
-            cu_seqlens,
-            ctx.cp_size,
-            ctx.cp_rank,
-        )
+
+        if ctx.use_aiter:
+            rotate_style = 1 if ctx.interleaved else 0
+            grad_input = _aiter_rope_bwd(
+                grad_output, freqs, rotate_style,
+                False,  # reuse_freqs_front_part
+                False,  # nope_first
+            )
+        else:
+            grad_input = tex.fused_rope_backward(
+                grad_output,
+                freqs,
+                start_positions,
+                QKVFormat[ctx.tensor_format],
+                ctx.interleaved,
+                cu_seqlens,
+                ctx.cp_size,
+                ctx.cp_rank,
+            )
 
         return grad_input, None, None, None, None, None, None, None, None