diff --git a/python/sglang/srt/layers/attention/nsa_backend.py b/python/sglang/srt/layers/attention/nsa_backend.py
index ad0d5840c..dea5c5348 100644
--- a/python/sglang/srt/layers/attention/nsa_backend.py
+++ b/python/sglang/srt/layers/attention/nsa_backend.py
@@ -260,6 +260,11 @@ class NSAIndexerMetadata(BaseIndexerMetadata):
                 row_starts=ks,
             )
         elif self.topk_transform_method == TopkTransformMethod.RAGGED:
+            if cu_topk_indices_offset is None:
+                raise RuntimeError(
+                    "RAGGED topk_transform requires topk_indices_offset; "
+                    "expected extend-without-speculative metadata."
+                )
             return fast_topk_transform_ragged_fused(
                 score=logits,
                 lengths=seq_lens_topk,
@@ -402,7 +407,9 @@ class NativeSparseAttnBackend(
 
         # Centralized dispatch: decide all strategies for this batch
         self.set_nsa_prefill_impl(forward_batch)
-        topk_transform_method = self.get_topk_transform_method()
+        topk_transform_method = self.get_topk_transform_method(
+            forward_batch.forward_mode
+        )
         # Batch indices selected when cp enabled: After splitting multiple sequences,
         # a certain cp rank may not have some of these sequences.
         # We use bs_idx_cpu to mark which sequences are finally selected by the current cp rank,
@@ -1343,7 +1350,9 @@ class NativeSparseAttnBackend(
             topk_indices = self._pad_topk_indices(topk_indices, q_nope.shape[0])
 
         # NOTE(dark): here, we use page size = 1
-        topk_transform_method = self.get_topk_transform_method()
+        topk_transform_method = self.get_topk_transform_method(
+            forward_batch.forward_mode
+        )
         if envs.SGLANG_NSA_FUSE_TOPK.get():
             page_table_1 = topk_indices
         else:
@@ -2095,7 +2104,9 @@ class NativeSparseAttnBackend(
                 # bf16 kv cache
                 self.nsa_prefill_impl = "flashmla_sparse"
 
-    def get_topk_transform_method(self) -> TopkTransformMethod:
+    def get_topk_transform_method(
+        self, forward_mode: Optional[ForwardMode] = None
+    ) -> TopkTransformMethod:
         """
         SGLANG_NSA_FUSE_TOPK controls whether to fuse the topk transform into the topk kernel.
         This method is used to select the topk transform method which can be fused or unfused.
@@ -2106,6 +2117,9 @@ class NativeSparseAttnBackend(
             and self.nsa_prefill_impl == "flashmla_sparse"
         ):
             topk_transform_method = TopkTransformMethod.RAGGED
+
+            if forward_mode is not None and (forward_mode.is_decode_or_idle()):
+                topk_transform_method = TopkTransformMethod.PAGED
         else:
             topk_transform_method = TopkTransformMethod.PAGED
         return topk_transform_method
@@ -2119,7 +2133,9 @@ class NativeSparseAttnBackend(
         )
         return NSAIndexerMetadata(
             attn_metadata=self.forward_metadata,
-            topk_transform_method=self.get_topk_transform_method(),
+            topk_transform_method=self.get_topk_transform_method(
+                forward_batch.forward_mode
+            ),
             paged_mqa_schedule_metadata=self.forward_metadata.paged_mqa_schedule_metadata,
             force_unfused_topk=force_unfused,
         )