diff --git a/vllm_ascend/patch/__init__.py b/vllm_ascend/patch/__init__.py
index d0d89846..b4d4148d 100644
--- a/vllm_ascend/patch/__init__.py
+++ b/vllm_ascend/patch/__init__.py
@@ -438,3 +438,17 @@
 #       patch Qwen3_5GatedDeltaNet._forward_core to use triton ops like `fused_recurrent_gated_delta_rule`.
 #    Future Plan:
 #       Remove this patch when all ops in _forward_core support both Qwen3_5 and Qwen3Next.
+#
+# ** 20. File: worker/patch_cudagraph.py**
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#   1. `vllm.v1.cudagraph_dispatcher.CudagraphDispatcher._create_padded_batch_descriptor`
+#    Why:
+#       vllm's FULL mode will cause error, we use a patch to avoid it.
+#       After that, FULL can be enable now.
+#    How：
+#       Dynamically replace the `_create_padded_batch_descriptor` function at runtime,
+#       and change the condition of if.
+#    Related PR (if no, explain why):
+#       https://github.com/vllm-project/vllm/pull/34880
+#    Future Plan:
+#       Remove this patch when vLLM merges the PR.
diff --git a/vllm_ascend/patch/worker/__init__.py b/vllm_ascend/patch/worker/__init__.py
index 2493ecb2..1664c84a 100644
--- a/vllm_ascend/patch/worker/__init__.py
+++ b/vllm_ascend/patch/worker/__init__.py
@@ -43,3 +43,4 @@ import vllm_ascend.patch.worker.patch_routed_experts_capturer  # noqa
 import vllm_ascend.patch.worker.patch_npugraph_ex_triton  # noqa
 import vllm_ascend.patch.worker.patch_kimi_k25  # noqa
 import vllm_ascend.patch.worker.patch_draft_quarot  # noqa
+import vllm_ascend.patch.worker.patch_cudagraph  # noqa
diff --git a/vllm_ascend/patch/worker/patch_cudagraph.py b/vllm_ascend/patch/worker/patch_cudagraph.py
new file mode 100644
index 00000000..bff74fdd
--- /dev/null
+++ b/vllm_ascend/patch/worker/patch_cudagraph.py
@@ -0,0 +1,38 @@
+from vllm.config import CUDAGraphMode
+from vllm.forward_context import BatchDescriptor
+from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
+
+
+def _create_padded_batch_descriptor(
+    self,
+    num_tokens: int,
+    uniform_decode: bool,
+    has_lora: bool,
+    num_active_loras: int = 0,
+) -> BatchDescriptor:
+    max_num_seqs = self.vllm_config.scheduler_config.max_num_seqs
+    uniform_decode_query_len = self.uniform_decode_query_len
+    num_tokens_padded = self._bs_to_padded_graph_size[num_tokens]
+
+    # FULL mode should not be treated as uniform decode
+    if (
+        uniform_decode
+        and self.cudagraph_mode.has_mode(CUDAGraphMode.FULL)
+        and self.cudagraph_mode != CUDAGraphMode.FULL
+    ):
+        num_reqs = min(num_tokens_padded // uniform_decode_query_len, max_num_seqs)
+        assert num_tokens_padded % uniform_decode_query_len == 0
+    else:
+        uniform_decode = False
+        num_reqs = min(num_tokens_padded, max_num_seqs)
+
+    return BatchDescriptor(
+        num_tokens=num_tokens_padded,
+        num_reqs=num_reqs,
+        uniform=uniform_decode,
+        has_lora=has_lora,
+        num_active_loras=num_active_loras,
+    )
+
+
+CudagraphDispatcher._create_padded_batch_descriptor = _create_padded_batch_descriptor
diff --git a/vllm_ascend/spec_decode/eagle_proposer.py b/vllm_ascend/spec_decode/eagle_proposer.py
index b821524a..b8e0c2f9 100644
--- a/vllm_ascend/spec_decode/eagle_proposer.py
+++ b/vllm_ascend/spec_decode/eagle_proposer.py
@@ -89,7 +89,7 @@ class AscendEagleProposer(EagleProposer):
         self.use_async_scheduling = self.vllm_config.scheduler_config.async_scheduling
 
         self.decode_threshold = 1 + self.num_speculative_tokens
-        self.query_start_loc = self.runner._make_buffer(self.runner.max_num_reqs + 1, dtype=torch.int32)
+        self.query_start_loc = self.runner._make_buffer(self.runner.max_num_reqs + 2, dtype=torch.int32)
         self.arange_cpu = torch.arange(self.arange.shape[0], device="cpu", dtype=torch.int32)
         self.attn_mask_builder = AttentionMaskBuilder(self.device)
 
@@ -362,7 +362,9 @@ class AscendEagleProposer(EagleProposer):
 
         model_positions = self._get_positions(num_tokens)
 
-        batch_size = num_tokens // (self.num_speculative_tokens + 1)  # if not is_profile else self.runner.max_num_reqs
+        batch_size = max(
+            num_tokens // (self.num_speculative_tokens + 1), 1
+        )  # if not is_profile else self.runner.max_num_reqs
         if is_profile:
             batch_size = min(batch_size, self.runner.max_num_reqs)