diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py
index 1177d8b27a..2a21445f5d 100644
--- a/vllm/v1/attention/backends/mla/indexer.py
+++ b/vllm/v1/attention/backends/mla/indexer.py
@@ -157,8 +157,11 @@ class DeepseekV32IndexerBackend(AttentionBackend):
     @classmethod
     def supports_device_cpu_query_lens_mismatch(cls) -> bool:
         # Only the varlen paged MQA logits kernel takes per-request query
-        # lengths from device tensors; otherwise the indexer needs uniform ones.
-        return _supports_varlen_paged_mqa_logits()
+        # lengths from device tensors natively. Hopper can instead flatten each
+        # query into a single-token row using device-built metadata.
+        return _supports_varlen_paged_mqa_logits() or (
+            _supports_flattened_device_query_lens()
+        )
 
     @staticmethod
     def get_name() -> str:
@@ -493,6 +496,14 @@ def _supports_varlen_paged_mqa_logits() -> bool:
     )
 
 
+def _supports_flattened_device_query_lens() -> bool:
+    return (
+        current_platform.is_cuda()
+        and current_platform.is_device_capability_family(90)
+        and has_deep_gemm()
+    )
+
+
 def _supports_native_decode(next_n: int) -> bool:
     """Whether decode can pass `next_n` Q rows per request to the kernel
     instead of flattening to one single-token row per query, which re-reads
@@ -507,6 +518,16 @@ def _supports_native_decode(next_n: int) -> bool:
     return next_n in (1, 2)
 
 
+def _use_flattening(vllm_config: VllmConfig) -> bool:
+    speculative_config = vllm_config.speculative_config
+    next_n = 1 + vllm_config.num_speculative_tokens
+    return not _supports_native_decode(next_n) or (
+        speculative_config is not None
+        and speculative_config.enable_adaptive_verification
+        and _supports_flattened_device_query_lens()
+    )
+
+
 class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
     # The indexer opts out of the shared reorder-threshold vote (see __init__),
     # so this is None; its own split uses self.decode_threshold.
@@ -519,7 +540,7 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
         vllm_config: VllmConfig,
         kv_cache_spec: KVCacheSpec,
     ) -> AttentionCGSupport:
-        if _supports_varlen_paged_mqa_logits():
+        if _supports_varlen_paged_mqa_logits() or _use_flattening(vllm_config):
             return AttentionCGSupport.ALWAYS
         return AttentionCGSupport.UNIFORM_BATCH
 
@@ -553,7 +574,7 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
         next_n = self.num_speculative_tokens + 1
         self.decode_threshold = next_n
         self.reorder_batch_threshold = None
-        self.use_flattening = not _supports_native_decode(next_n)
+        self.use_flattening = _use_flattening(self.vllm_config)
         self.supports_varlen = _supports_varlen_paged_mqa_logits()
         logger.info_once(
             "DSA indexer decode path: use_flattening=%s supports_varlen=%s "
@@ -674,11 +695,14 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
         max_decode_len: int,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, bool]:
         """Prepare native or per-token flattened decode tensors."""
+        spec_config = self.vllm_config.speculative_config
+        adaptive = bool(spec_config and spec_config.enable_adaptive_verification)
         min_decode_len = int(decode_lens_cpu.min().item())
         if not use_native:
             assert self.decode_seq_lens_buffer.dim() == 1
             if (
                 not self.supports_varlen
+                and (num_decodes == 1 or not adaptive)
                 and min_decode_len == max_decode_len
                 and num_decodes * max_decode_len == num_decode_tokens
             ):