diff --git a/vllm/v1/attention/backends/mla/common.py b/vllm/v1/attention/backends/mla/common.py
index f7ec18f5e9f6..3fb00f5917ea 100755
--- a/vllm/v1/attention/backends/mla/common.py
+++ b/vllm/v1/attention/backends/mla/common.py
@@ -190,7 +190,7 @@ return curr_o @ W_O
 import functools
 from abc import abstractmethod
 from dataclasses import dataclass, field
-from typing import Generic, Optional, TypeVar, Union
+from typing import ClassVar, Generic, Optional, TypeVar, Union
 
 import torch
 from tqdm import tqdm
@@ -454,6 +454,20 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
     understand this class
     """
 
+    # Whether the backend supports reordering the batch such that
+    # short sequences (i.e. verification for speculative decoding) are
+    # classified as decode requests.
+    # If True, this will increase `reorder_batch_threshold` (below) when
+    # speculative decoding is enabled, and set `require_uniform=True` when
+    # when reordering the batch. Non-uniform decode requests will
+    # fall back to prefill in this case.
+    supports_uniform_spec_as_decode: ClassVar[bool] = False
+
+    # The threshold for reordering the batch into decode and prefill requests.
+    # If > 1, the batch will be reordered such that requests with
+    # query length <= threshold are classified as decode requests.
+    # Use `supports_uniform_spec_as_decode` (above) to set this automatically
+    # when speculative decoding is enabled.
     reorder_batch_threshold: int = 1
 
     @staticmethod
@@ -503,6 +517,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
         self.model_config = vllm_config.model_config
         parallel_config = vllm_config.parallel_config
         self.compilation_config = vllm_config.compilation_config
+        self.vllm_config = vllm_config
         self.device = device
 
         self.num_heads = self.model_config.get_num_attention_heads(parallel_config)
@@ -578,6 +593,11 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
                 device=device,
             )
 
+        supports_spec_as_decode = self.supports_uniform_spec_as_decode
+        self._init_reorder_batch_threshold(
+            self.reorder_batch_threshold, supports_spec_as_decode
+        )
+
     def _build_fi_prefill_wrappers(self, prefill: FlashInferPrefillMetadata):
         qo_indptr = prefill.query_start_loc
 
@@ -714,7 +734,9 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
 
         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=self.supports_uniform_spec_as_decode,
             )
         )
 
diff --git a/vllm/v1/attention/backends/mla/flashinfer_mla.py b/vllm/v1/attention/backends/mla/flashinfer_mla.py
index 13552edab87b..206f96ea366a 100644
--- a/vllm/v1/attention/backends/mla/flashinfer_mla.py
+++ b/vllm/v1/attention/backends/mla/flashinfer_mla.py
@@ -22,6 +22,9 @@ FLASHINFER_MLA_WORKSPACE_BUFFER_SIZE = 128 * 1024 * 1024
 
 
 class FlashInferMLAMetadataBuilder(MLACommonMetadataBuilder[MLACommonMetadata]):
+    # enable spec-as-decode optimization
+    supports_uniform_spec_as_decode: ClassVar[bool] = True
+
     # enable full CUDA Graph support for decode-only capture
     cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH
 
@@ -111,7 +114,15 @@ class FlashInferMLAImpl(MLACommonImpl[MLACommonMetadata]):
             q = torch.cat([q_nope, q_pe], dim=-1)
 
         # trtllm API requires extra dimension q_len_per_request for MTP
-        q = q.unsqueeze(1)
+        if attn_metadata.num_decode_tokens % attn_metadata.num_decodes != 0:
+            logger.warning_once(
+                """FlashInferMLAImpl got a query of uneven length.
+                This usually indicates an issue in batch reordering
+                or incorrect setup in dummy_run."""
+            )
+            q = q.unsqueeze(1)
+        else:
+            q = q.view(attn_metadata.num_decodes, -1, q.shape[-2], q.shape[-1])
 
         if self.bmm1_scale is None:
             self.bmm1_scale = layer._q_scale_float * layer._k_scale_float * self.scale
@@ -132,6 +143,9 @@ class FlashInferMLAImpl(MLACommonImpl[MLACommonMetadata]):
             bmm2_scale=self.bmm2_scale,
         )
 
+        # Flatten the output for consistent shape
+        o = o.view(-1, o.shape[-2], o.shape[-1])
+
         # TODO: Return LSE pending support from Flashinfer API:
         # https://github.com/flashinfer-ai/flashinfer/pull/1566
         return o, None
diff --git a/vllm/v1/attention/backends/utils.py b/vllm/v1/attention/backends/utils.py
index 003c7253e553..add2c3cb8d59 100644
--- a/vllm/v1/attention/backends/utils.py
+++ b/vllm/v1/attention/backends/utils.py
@@ -275,8 +275,9 @@ class AttentionMetadataBuilder(abc.ABC, Generic[M]):
                 speculative_config is not None
                 and speculative_config.num_speculative_tokens is not None
             ):
-                self.reorder_batch_threshold = (
-                    1 + speculative_config.num_speculative_tokens
+                self.reorder_batch_threshold = max(
+                    self.reorder_batch_threshold,
+                    1 + speculative_config.num_speculative_tokens,
                 )
 
     @abstractmethod