diff --git a/python/cudnn/deepseek_sparse_attention/indexer_top_k/indexer_top_k_decode_varlen.py b/python/cudnn/deepseek_sparse_attention/indexer_top_k/indexer_top_k_decode_varlen.py
index d8b21341..24d66447 100644
--- a/python/cudnn/deepseek_sparse_attention/indexer_top_k/indexer_top_k_decode_varlen.py
+++ b/python/cudnn/deepseek_sparse_attention/indexer_top_k/indexer_top_k_decode_varlen.py
@@ -14,6 +14,8 @@
 
 """SM90+ CuTe DSL indexer top-K decode kernel."""
 
+import math
+
 import cuda.bindings.driver as cuda
 import cutlass
 import cutlass.cute as cute
@@ -687,6 +689,50 @@ def cute_dsl_topk_wrapper(
         buffer_numbers = 2
     else:
         buffer_numbers = 1
+
+    # Decode-varlen IMA workaround.
+    elems_per_row = buffer_numbers * num_cols
+    int32_max = (1 << 31) - 1
+    if elems_per_row > 0:
+        max_chunk_rows = int32_max // elems_per_row + 1
+    else:
+        max_chunk_rows = num_rows
+    # Alignment
+    input_elem_bytes = dtype.width // 8
+    align_rows = 32 // input_elem_bytes if input_elem_bytes > 0 else 1
+    if align_rows < 1:
+        align_rows = 1
+    # chunk_rows must be a common multiple of next_n and align_rows.
+    row_step = (next_n * align_rows) // math.gcd(next_n, align_rows)
+    if max_chunk_rows < row_step:
+        chunk_rows = num_rows
+    else:
+        chunk_rows = (max_chunk_rows // row_step) * row_step
+    if 0 < chunk_rows < num_rows:
+        g_global_counter_torch = None
+        for row_lo in range(0, num_rows, chunk_rows):
+            row_hi = min(row_lo + chunk_rows, num_rows)
+            batch_lo = row_lo // next_n
+            batch_hi = row_hi // next_n
+            chunk_input = input_values[row_lo:row_hi]
+            chunk_seqlens = seq_lens[batch_lo:batch_hi]
+            chunk_oi = output_indices_torch[row_lo:row_hi]
+            chunk_ov = output_values_torch[row_lo:row_hi] if return_val else None
+            chunk_extra = torch.empty(
+                row_hi - row_lo, buffer_numbers, num_cols,
+                dtype=torch.int32, device="cuda",
+            )
+            compiled_kernel(
+                chunk_input,
+                None,
+                chunk_extra,
+                g_global_counter_torch,
+                chunk_seqlens,
+                chunk_oi,
+                chunk_ov,
+            )
+        return output_indices_torch, output_values_torch
+
     # Note: zeros will trigger an elementwise_add kernel.
     buffer_torch = torch.empty(num_rows, buffer_numbers, num_cols, dtype=torch.int32, device="cuda")
     g_global_counter_torch = None