diff --git a/docs/design/cuda_graphs.md b/docs/design/cuda_graphs.md
index af9e5b5ba6f..b27c8d34e46 100644
--- a/docs/design/cuda_graphs.md
+++ b/docs/design/cuda_graphs.md
@@ -182,6 +182,7 @@ The following table lists backends that support full CUDA Graphs at the time of
 | FlashInfer | `UNIFORM_SINGLE_TOKEN_DECODE` | Will be set to `UNIFORM_BATCH` when using TRTLLM attention on Blackwell |
 | FlashMLA | `UNIFORM_BATCH` | |
 | FlashInferMLA | `UNIFORM_BATCH` | |
+| FlashInferMLASparse | `UNIFORM_BATCH` | |
 | AITER MLA | `UNIFORM_SINGLE_TOKEN_DECODE` | |
 | CUTLASS MLA | `UNIFORM_SINGLE_TOKEN_DECODE` | |
 | Mamba attention| `UNIFORM_SINGLE_TOKEN_DECODE` | |
diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py
index 368b217f0ba..a26fd8fbc69 100644
--- a/vllm/v1/attention/backends/mla/indexer.py
+++ b/vllm/v1/attention/backends/mla/indexer.py
@@ -196,9 +196,7 @@ def get_max_prefill_buffer_size(vllm_config: VllmConfig):
 
 
 class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
-    _cudagraph_support: ClassVar[AttentionCGSupport] = (
-        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
-    )
+    _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH
 
     reorder_batch_threshold: int = 1
 
@@ -212,8 +210,14 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
             if self.vllm_config.speculative_config
             else 0
         )
-        # Now deepgemm fp8_paged_mqa_logits does not support next_n > 2
-        self.reorder_batch_threshold += min(self.num_speculative_tokens, 1)
+        if self.num_speculative_tokens > 1:
+            raise ValueError(
+                "Sparse MLA only supports "
+                "num_speculative_tokens <= 1 because the DeepGEMM "
+                "fp8_paged_mqa_logits kernel does not support next_n > 2. "
+                f"Got num_speculative_tokens={self.num_speculative_tokens}."
+            )
+        self.reorder_batch_threshold += self.num_speculative_tokens
 
         props = torch.cuda.get_device_properties(self.device)
         sm_count = props.multi_processor_count
@@ -342,8 +346,14 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
                 self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
                     seq_lens, self.kv_cache_spec.block_size, self.num_sms
                 )
+            block_table = common_attn_metadata.block_table_tensor[:num_decodes, ...]
+            # Padded CUDA graph requests have block_table entries of -1.
+            # Clamp to 0 to prevent OOB access in the DeepGEMM kernel.
+            # This is safe because padded requests have seq_lens=0, so the
+            # kernel produces no meaningful output for those rows.
+            block_table.clamp_(min=0)
             decode_metadata = DeepSeekV32IndexerDecodeMetadata(
-                block_table=common_attn_metadata.block_table_tensor[:num_decodes, ...],
+                block_table=block_table,
                 seq_lens=common_attn_metadata.seq_lens[:num_decodes],
                 decode_lens=decode_lens,
                 requires_padding=requires_padding,