diff --git a/vllm/v1/attention/backends/mla/common.py b/vllm/v1/attention/backends/mla/common.py
index a990cb2f1a97..3dd817a0564f 100755
--- a/vllm/v1/attention/backends/mla/common.py
+++ b/vllm/v1/attention/backends/mla/common.py
@@ -359,6 +359,8 @@ class ChunkedContextMetadata(
 class MLACommonDecodeMetadata:
     block_table: torch.Tensor
     seq_lens: torch.Tensor
+    query_base_positions: Optional[torch.Tensor] = field(default=None,
+                                                         kw_only=True)
 
 
 D = TypeVar("D", bound=MLACommonDecodeMetadata)
@@ -615,9 +617,19 @@ def _build_decode(self, block_table_tensor: torch.Tensor,
                       query_start_loc_cpu: torch.Tensor,
                       query_start_loc_device: torch.Tensor,
                       num_decode_tokens: int) -> MLACommonDecodeMetadata:
+
+        # Compute DCP query base positions if using DCP
+        query_base_positions = None
+
+        if self.dcp_world_size > 1:
+            query_lens = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
+            query_base_positions = (seq_lens_cpu - query_lens).to(
+                seq_lens_device.device)
+
         return MLACommonDecodeMetadata(
             block_table=block_table_tensor,
             seq_lens=seq_lens_device,
+            query_base_positions=query_base_positions,
         )
 
     def build_for_cudagraph_capture(
diff --git a/vllm/v1/attention/backends/mla/flashattn_mla.py b/vllm/v1/attention/backends/mla/flashattn_mla.py
index 472095e13615..c2f727ba77c5 100644
--- a/vllm/v1/attention/backends/mla/flashattn_mla.py
+++ b/vllm/v1/attention/backends/mla/flashattn_mla.py
@@ -11,7 +11,6 @@
 from vllm.attention.utils.fa_utils import (flash_attn_supports_mla,
                                            get_flash_attn_version)
 from vllm.config import VllmConfig
-from vllm.distributed.parallel_state import get_dcp_group
 from vllm.logger import init_logger
 from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
                                                    MLACommonDecodeMetadata,
@@ -99,11 +98,6 @@ def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
             # pre-allocated during capture.
             self.max_num_splits = _DEFAULT_MAX_NUM_SPLITS_FOR_CUDA_GRAPH
 
-        # TODO(lucas): Until we add support for the DCP custom masking we need
-        #   to restrict decodes to q_len == 1 when DCP is enabled.
-        self.__class__.reorder_batch_threshold = 1 \
-            if get_dcp_group().world_size > 1 else self.reorder_batch_threshold
-
     def _schedule_decode(self, num_reqs, cu_query_lens, max_query_len, seqlens,
                          max_seq_len, causal):
         if self.fa_aot_schedule:
@@ -262,6 +256,9 @@ def _forward_decode(
             fa_version=3,  # only version 3 is supported
             scheduler_metadata=attn_metadata.decode.scheduler_metadata,
             num_splits=attn_metadata.decode.max_num_splits,
+            dcp_rank=self.dcp_rank,
+            dcp_world_size=self.dcp_world_size,
+            query_base_positions=attn_metadata.decode.query_base_positions,
         )
 
         if self.need_to_return_lse_for_decode:
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index d4afaf51e6e8..c8c8f9d68b04 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -474,12 +474,6 @@ def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
             return
 
         if self.reorder_batch_threshold is not None:
-            # NOTE(lucas): currently no backend supports the custom masking
-            #  required for DCP with q_len > 1, so we assert here. Remove this
-            #  assert once the custom mask is support is added to FA3.
-            if self.dcp_world_size > 1:
-                assert self.reorder_batch_threshold == 1, \
-                    "DCP not support reorder_batch_threshold > 1 now."
             reorder_batch_to_split_decodes_and_prefills(
                 self.input_batch,
                 scheduler_output,