diff --git a/vllm/v1/sample/ops/topk_topp_sampler.py b/vllm/v1/sample/ops/topk_topp_sampler.py
index dbcdad07e4de..66f9ab22a789 100644
--- a/vllm/v1/sample/ops/topk_topp_sampler.py
+++ b/vllm/v1/sample/ops/topk_topp_sampler.py
@@ -75,6 +75,18 @@ def __init__(self, logprobs_mode: LogprobsMode = "raw_logprobs") -> None:
                 self.forward = self.forward_native
         elif current_platform.is_cpu():
             self.forward = self.forward_cpu
+        elif (
+            logprobs_mode not in ("processed_logits", "processed_logprobs")
+            and current_platform.is_rocm()
+            and envs.VLLM_ROCM_USE_AITER
+        ):
+            import aiter.ops.sampling  # noqa: F401
+
+            self.aiter_ops = torch.ops.aiter
+            logger.info_once(
+                "Using aiter sampler on ROCm (lazy import, sampling-only)."
+            )
+            self.forward = self.forward_hip
         else:
             self.forward = self.forward_native
 
@@ -120,9 +132,10 @@ def forward_cuda(
                     "PyTorch-native implementation."
                 )
             return self.forward_native(logits, generators, k, p)
-        assert self.logprobs_mode not in ("processed_logits", "processed_logprobs"), (
-            "FlashInfer does not support returning logits/logprobs"
-        )
+        assert self.logprobs_mode not in (
+            "processed_logits",
+            "processed_logprobs",
+        ), "FlashInfer does not support returning logits/logprobs"
         # flashinfer sampling functions expect contiguous logits.
         # In flex_attn/triton_attn fp32 inference, logits can be non-contiguous
         # because of slicing operation in logits_processor.
@@ -167,6 +180,64 @@ def compiled_random_sample(logits: torch.Tensor) -> torch.Tensor:
 
             return probs.div_(q).argmax(dim=-1).view(-1), logits_to_return
 
+    def forward_hip(
+        self,
+        logits: torch.Tensor,
+        generators: dict[int, torch.Generator],
+        k: Optional[torch.Tensor],
+        p: Optional[torch.Tensor],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """Optimized ROCm/aiter path (same structure as forward_cuda)."""
+        if (k is None and p is None) or generators:
+            if generators:
+                logger.warning_once(
+                    "aiter sampler does not support per-request generators; "
+                    "falling back to PyTorch-native."
+                )
+            return self.forward_native(logits, generators, k, p)
+        assert self.logprobs_mode not in (
+            "processed_logits",
+            "processed_logprobs",
+        ), "aiter sampler does not support returning logits/logprobs."
+        return self.aiter_sample(logits, k, p, generators), None
+
+    def aiter_sample(
+        self,
+        logits: torch.Tensor,
+        k: Optional[torch.Tensor],
+        p: Optional[torch.Tensor],
+        generators: dict[int, torch.Generator],
+    ) -> torch.Tensor:
+        """Sample from logits using aiter ops."""
+        use_top_k = k is not None
+        use_top_p = p is not None
+        # Joint k+p path
+        if use_top_p and use_top_k:
+            probs = logits.softmax(dim=-1, dtype=torch.float32).contiguous()
+            next_token_ids = self.aiter_ops.top_k_top_p_sampling_from_probs(
+                probs,
+                None,
+                *_to_tensor_scalar_tuple(k),
+                *_to_tensor_scalar_tuple(p),
+                deterministic=True,
+            )
+            return next_token_ids.view(-1)
+        # Top-p only path
+        elif use_top_p:
+            probs = logits.softmax(dim=-1, dtype=torch.float32).contiguous()
+            next_token_ids = self.aiter_ops.top_p_sampling_from_probs(
+                probs, None, *_to_tensor_scalar_tuple(p), deterministic=True
+            )
+            return next_token_ids.view(-1)
+        # Top-k only path
+        elif use_top_k:
+            probs = logits.softmax(dim=-1, dtype=torch.float32).contiguous()
+            renorm_probs = self.aiter_ops.top_k_renorm_probs(
+                probs, *_to_tensor_scalar_tuple(k)
+            )
+            return torch.multinomial(renorm_probs, num_samples=1).view(-1)
+        raise RuntimeError("aiter_sample was called with no active top-k or top-p.")
+
 
 def apply_top_k_top_p(
     logits: torch.Tensor,
@@ -300,3 +371,10 @@ def flashinfer_sample(
         )
 
     return next_token_ids.view(-1)
+
+
+def _to_tensor_scalar_tuple(x):
+    if isinstance(x, torch.Tensor):
+        return (x, 0)
+    else:
+        return (None, x)