diff --git a/vllm/model_executor/layers/sparse_attn_indexer.py b/vllm/model_executor/layers/sparse_attn_indexer.py
index e6e6f01cc..2767ebdd2 100644
--- a/vllm/model_executor/layers/sparse_attn_indexer.py
+++ b/vllm/model_executor/layers/sparse_attn_indexer.py
@@ -1982,6 +1982,19 @@ class SparseAttnIndexer(CustomOp):
         self.max_model_len = max_model_len
         self.max_total_seq_len = max_total_seq_len
         self.topk_indices_buffer = topk_indices_buffer
+        if topk_scores_buffer is None and topk_indices_buffer is not None:
+            try:
+                from vllm.distributed.parallel_state import get_dcp_group
+
+                dcp_world_size = get_dcp_group().world_size
+            except Exception:
+                dcp_world_size = 1
+            if dcp_world_size > 1 and use_b12x_sparse_indexer():
+                topk_scores_buffer = torch.empty(
+                    topk_indices_buffer.shape,
+                    dtype=torch.float32,
+                    device=topk_indices_buffer.device,
+                )
         self.topk_scores_buffer = topk_scores_buffer
         self.skip_k_cache_insert = skip_k_cache_insert
         self.use_fp4_cache = use_fp4_cache
diff --git a/vllm/model_executor/models/deepseek_mtp.py b/vllm/model_executor/models/deepseek_mtp.py
index 552d6438a..d0fdbaf36 100644
--- a/vllm/model_executor/models/deepseek_mtp.py
+++ b/vllm/model_executor/models/deepseek_mtp.py
@@ -346,10 +346,7 @@ class DeepSeekMultiTokenPredictorLayer(nn.Module):
                 device=self.device,
             )
             topk_scores_buffer = None
-            if (
-                vllm_config.parallel_config.decode_context_parallel_size > 1
-                and use_b12x_sparse_indexer()
-            ):
+            if vllm_config.parallel_config.decode_context_parallel_size > 1:
                 topk_scores_buffer = torch.empty(
                     vllm_config.scheduler_config.max_num_batched_tokens,
                     topk_tokens,
diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py
index cbdc9be96..6260ee159 100644
--- a/vllm/v1/attention/backends/mla/indexer.py
+++ b/vllm/v1/attention/backends/mla/indexer.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import os
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
 
 import numpy as np
 import torch
@@ -695,6 +695,7 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
         use_dcp_local_kv = (
             self.dcp_world_size > 1
             and common_attn_metadata.dcp_local_seq_lens is not None
+            and not getattr(self.kv_cache_spec, "dcp_replicated", False)
         )
         effective_dcp_world_size = self.dcp_world_size if use_dcp_local_kv else 1
         effective_dcp_rank = self.dcp_rank if use_dcp_local_kv else 0
@@ -1021,6 +1022,49 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
 
         return attn_metadata
 
+    def build_for_drafting(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        draft_index: int,
+    ) -> DeepseekV32IndexerMetadata:
+        if draft_index > 0:
+            num_tokens = common_attn_metadata.num_actual_tokens
+            num_reqs = min(common_attn_metadata.num_reqs, num_tokens)
+            if num_tokens > 0 and num_reqs > 0:
+                needs_unpadded_decode_view = (
+                    common_attn_metadata.batch_topology is not None
+                    or common_attn_metadata.max_query_len > 1
+                    or common_attn_metadata.num_reqs != num_reqs
+                    or int(common_attn_metadata.query_start_loc_cpu[num_reqs])
+                    != num_tokens
+                )
+                if needs_unpadded_decode_view:
+                    query_start_loc = torch.arange(
+                        num_reqs + 1,
+                        dtype=common_attn_metadata.query_start_loc.dtype,
+                        device=common_attn_metadata.query_start_loc.device,
+                    )
+                    query_start_loc_cpu = torch.arange(
+                        num_reqs + 1,
+                        dtype=common_attn_metadata.query_start_loc_cpu.dtype,
+                        device=common_attn_metadata.query_start_loc_cpu.device,
+                    )
+                    common_attn_metadata = replace(
+                        common_attn_metadata.unpadded(
+                            num_actual_tokens=num_tokens,
+                            num_actual_reqs=num_reqs,
+                        ),
+                        query_start_loc=query_start_loc,
+                        query_start_loc_cpu=query_start_loc_cpu,
+                        max_query_len=1,
+                    )
+
+        return self.build(
+            common_prefix_len=0,
+            common_attn_metadata=common_attn_metadata,
+            fast_build=True,
+        )
+
 
 def build_prefill_chunk_metadata(
     start_idx: int,