diff --git a/csrc/sampler.cu b/csrc/sampler.cu
index 30bfef33c..2e76873c8 100644
--- a/csrc/sampler.cu
+++ b/csrc/sampler.cu
@@ -575,7 +575,7 @@ static __global__ __launch_bounds__(kNumThreadsPerBlock) void topKPerRowDecode(
   // The range of logits within the row.
   int rowStart = 0;
   int seq_len = seqLens[rowIdx / next_n];
-  int rowEnd = seq_len - next_n + (rowIdx % next_n) + 1;
+  int rowEnd = max(0, seq_len - next_n + (rowIdx % next_n) + 1);
 
   // Local pointers to this block
   if constexpr (!multipleBlocksPerRow && !mergeBlocks) {
diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py
index 70281b4a9..3b3be6ac9 100644
--- a/vllm/v1/attention/backends/mla/indexer.py
+++ b/vllm/v1/attention/backends/mla/indexer.py
@@ -206,6 +206,8 @@ def get_max_prefill_buffer_size(vllm_config: VllmConfig):
 
 class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
     reorder_batch_threshold: int = 1
+    natively_supported_next_n: list[int] = [1, 2]
+    # TODO (matt): integrate kernel with next_n = 4 support
 
     @classmethod
     def get_cudagraph_support(
@@ -231,7 +233,9 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
             if self.vllm_config.speculative_config
             else 0
         )
+        next_n = self.num_speculative_tokens + 1
         self.reorder_batch_threshold += self.num_speculative_tokens
+        self.use_flattening = next_n not in self.natively_supported_next_n
 
         sm_count = num_compute_units(self.device.index)
         self.num_sms = sm_count
@@ -241,10 +245,11 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
             dtype=torch.int32,
             device=self.device,
         )
-
-        # Pre-allocated buffers for flattening (spec decode).
+        self.offsets_buffer = torch.arange(
+            next_n, device=self.device, dtype=torch.int32
+        )
         self.arange_buffer = torch.arange(
-            scheduler_config.max_num_seqs * (1 + self.num_speculative_tokens),
+            scheduler_config.max_num_seqs * next_n,
             dtype=torch.int32,
             device=self.device,
         )
@@ -323,7 +328,9 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
         query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
         num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = (
             split_decodes_and_prefills(
-                common_attn_metadata, decode_threshold=self.reorder_batch_threshold
+                common_attn_metadata,
+                decode_threshold=self.reorder_batch_threshold,
+                require_uniform=not self.use_flattening,
             )
         )
 
@@ -372,11 +379,21 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
             block_table.clamp_(min=0)
 
             max_decode_len = int(decode_lens_cpu.max().item())
-            if max_decode_len > 1:
+            next_n = 1 + self.num_speculative_tokens
+            use_native = not self.use_flattening and max_decode_len == next_n
+
+            if use_native and next_n > 1:
+                offsets = self.offsets_buffer
+                batch_size = num_decodes
+            elif max_decode_len > 1:
                 # Flatten multi-token decode requests into single-token
                 # batch entries, expanding seq_lens and block tables so
                 # the kernel always sees next_n=1.
 
+                # Also handles the edge case where use_flattening=False
+                # but max_decode_len != next_n (e.g. a batch containing some
+                # short prefills (q_len < next_n) and no true decodes).
+
                 # Assume 4 requests with seq_lens [10, 7, 12, 0] (the final req is
                 # padding) and decode_lens [3, 1, 4, 0] in the below example comments.
                 # The context lengths are therefore
@@ -428,13 +445,7 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
                 offsets = None
                 batch_size = num_decode_tokens
             else:
-                next_n = 1 + self.num_speculative_tokens
-                if next_n > 1:
-                    offsets = torch.arange(
-                        next_n, device=self.device, dtype=torch.int32
-                    )
-                else:
-                    offsets = None
+                offsets = None
                 batch_size = num_decodes
 
             # DeepGEMM is required for the paged MQA logits on CUDA devices