diff --git a/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py b/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py
index bda63e1ae2..2bbaaad765 100644
--- a/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py
+++ b/vllm/v1/attention/backends/mla/rocm_aiter_mla_sparse.py
@@ -369,6 +369,8 @@ class ROCMAiterMLASparseMetadataBuilder(
         self.num_heads = self.model_config.get_num_attention_heads(parallel_config)
         self.mla_dims = get_mla_dims(self.model_config)
         self.topk_tokens = vllm_config.model_config.hf_config.index_topk
+        # Bounds the KV-split heuristic (see `_sparse_decode_max_split`).
+        self._num_compute_units = current_platform.num_compute_units()
         self.max_model_len_tensor = torch.tensor(
             [self.model_config.max_model_len], device=device, dtype=torch.int32
         )
@@ -464,6 +466,21 @@ class ROCMAiterMLASparseMetadataBuilder(
         self._prev_indices_extent: int = 0
         self._prev_metadata_key: tuple | None = None
 
+    def _sparse_decode_max_split(self, max_seq_len: int) -> int:
+        """Cap ``max_split_per_batch`` for the aiter sparse-MLA decode reduce.
+
+        The reduce only covers the selected tokens per row (``<= topk_tokens``),
+        so aiter's default (``-1`` => split across every CU) over-fragments it.
+        Mirror ``triton_mla.py``: aim for a minimum work per split, round to a
+        power of two, and cap by the CU count. Numerics are unchanged.
+        """
+        effective_len = min(max_seq_len, self.topk_tokens)
+        min_work_per_split = 128
+        ideal_splits = triton.next_power_of_2(
+            max(1, effective_len // min_work_per_split)
+        )
+        return min(ideal_splits, self._num_compute_units)
+
     def build(
         self,
         common_prefix_len: int,
@@ -547,6 +564,9 @@ class ROCMAiterMLASparseMetadataBuilder(
         if metadata_key != self._prev_metadata_key:
             from aiter import get_mla_metadata_v1
 
+            max_split_per_batch = self._sparse_decode_max_split(
+                int(common_attn_metadata.max_seq_len)
+            )
             get_mla_metadata_v1(
                 qo_indptr,
                 paged_kv_indptr,
@@ -565,6 +585,7 @@ class ROCMAiterMLASparseMetadataBuilder(
                 max_seqlen_qo=1,
                 uni_seqlen_qo=1,
                 fast_mode=True,
+                max_split_per_batch=max_split_per_batch,
             )
             # The persistent metadata buffers are read by graph replay. Order
             # the async metadata write before the graph-captured decode kernel.