diff --git a/lightllm/models/qwen3next/layer_infer/transformer_layer_infer.py b/lightllm/models/qwen3next/layer_infer/transformer_layer_infer.py
index 3a8807ca5..bb48bfe49 100644
--- a/lightllm/models/qwen3next/layer_infer/transformer_layer_infer.py
+++ b/lightllm/models/qwen3next/layer_infer/transformer_layer_infer.py
@@ -7,6 +7,7 @@
 from lightllm.models.llama.layer_infer.transformer_layer_infer import LlamaTransformerLayerInfer
 from lightllm.models.qwen3next.infer_struct import Qwen3NextInferStateInfo
 from lightllm.utils.log_utils import init_logger
+from lightllm.utils.tensor_utils import tensor_to_no_ref_tensor
 from lightllm.common.kv_cache_mem_manager import Qwen3NextMemManager
 from typing import Tuple
 from lightllm.models.qwen3next.triton_kernel.causal_conv1d import causal_conv1d_fn, causal_conv1d_update
@@ -249,16 +250,13 @@ def gdn_forward(
         assert isinstance(infer_state.mem_manager, Qwen3NextMemManager)
 
         input = input.view(-1, self.embed_dim_)
-        conv_states, ssm_states = infer_state.req_manager.get_mamba_cache(self.layer_num_)
-
         mixed_qkvzba = layer_weight.linear_in_proj.mm(input)
-        mixed_qkv, z, b, a = self._split_qkvzba(mixed_qkvzba, is_decode=not is_prefill)
 
         if is_prefill:
-            core_attn_out = self._gdn_prefill_kernel(
-                mixed_qkv, conv_states, ssm_states, a, b, infer_state, layer_weight
-            )
+            core_attn_out, z = self._gdn_prefill_wrapper_run(mixed_qkvzba, infer_state, layer_weight)
         else:
+            mixed_qkv, z, b, a = self._split_qkvzba(mixed_qkvzba)
+            conv_states, ssm_states = infer_state.req_manager.get_mamba_cache(self.layer_num_)
             core_attn_out = self._gdn_decode_kernel(
                 mixed_qkv,
                 conv_states,
@@ -277,7 +275,55 @@ def gdn_forward(
         output = layer_weight.linear_out_proj.mm(core_attn_out)
         return output
 
-    def _split_qkvzba(self, mixed_qkvzba, is_decode=False):
+    def _gdn_prefill_wrapper_run(
+        self,
+        mixed_qkvzba: torch.Tensor,
+        infer_state: Qwen3NextInferStateInfo,
+        layer_weight: Qwen3NextTransformerLayerWeight,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if torch.cuda.is_current_stream_capturing():
+            mixed_qkvzba = mixed_qkvzba.contiguous()
+            _mixed_qkvzba = tensor_to_no_ref_tensor(mixed_qkvzba)
+            pre_capture_graph = infer_state.prefill_cuda_graph_get_current_capture_graph()
+            pre_capture_graph.__exit__(None, None, None)
+
+            # _gdn_prefill_kernel returns the pre-projection value stream. Its
+            # logical size is num_tokens * local value heads * value head dim.
+            # We avoid a dry-run because FlashQLA may do host-side syncs while
+            # preparing varlen chunk metadata, which is illegal during capture.
+            num_tokens = mixed_qkvzba.shape[0]
+            o_shape = (num_tokens, self.tp_num_v_heads, self.head_v_dim)
+            o_dtype = mixed_qkvzba.dtype
+            o_device = mixed_qkvzba.device
+            z_shape = o_shape
+
+            infer_state.prefill_cuda_graph_create_graph_obj()
+            infer_state.prefill_cuda_graph_get_current_capture_graph().__enter__()
+            o = torch.empty(o_shape, dtype=o_dtype, device=o_device)
+            _o = tensor_to_no_ref_tensor(o)
+            z = torch.empty(z_shape, dtype=o_dtype, device=o_device)
+            _z = tensor_to_no_ref_tensor(z)
+
+            def gdn_prefill_func(new_infer_state: Qwen3NextInferStateInfo):
+                conv_states, ssm_states = new_infer_state.req_manager.get_mamba_cache(self.layer_num_)
+                mixed_qkv, tmp_z, b, a = self._split_qkvzba(_mixed_qkvzba)
+                _z.copy_(tmp_z)
+                tmp_o = self._gdn_prefill_kernel(
+                    mixed_qkv, conv_states, ssm_states, a, b, new_infer_state, layer_weight
+                )
+                tmp_o = tmp_o.view(_o.shape)
+                _o.copy_(tmp_o)
+                return
+
+            infer_state.prefill_cuda_graph_add_cpu_runnning_func(func=gdn_prefill_func, after_graph=pre_capture_graph)
+            return o, z
+
+        conv_states, ssm_states = infer_state.req_manager.get_mamba_cache(self.layer_num_)
+        mixed_qkv, z, b, a = self._split_qkvzba(mixed_qkvzba)
+        core_attn_out = self._gdn_prefill_kernel(mixed_qkv, conv_states, ssm_states, a, b, infer_state, layer_weight)
+        return core_attn_out, z
+
+    def _split_qkvzba(self, mixed_qkvzba):
         qkv_dim = self.tp_key_dim * 2 + self.tp_value_dim
         z_end = qkv_dim + self.tp_value_dim
         b_end = z_end + self.tp_num_v_heads
diff --git a/test/acc/test_qwen3.5.sh b/test/acc/test_qwen3.5.sh
index 69169ec23..a590aa083 100644
--- a/test/acc/test_qwen3.5.sh
+++ b/test/acc/test_qwen3.5.sh
@@ -9,6 +9,15 @@ LOADWORKER=18 CUDA_VISIBLE_DEVICES=6,7 python -m lightllm.server.api_server \
 # second
 export no_proxy="localhost,127.0.0.1,0.0.0.0,::1" HF_ALLOW_CODE_EVAL=1 HF_DATASETS_OFFLINE=0 lm_eval --model local-completions --model_args '{"model":"qwen/Qwen3.5-0.8B", "base_url":"http://localhost:8089/v1/completions", "max_length": 16384}' --tasks gsm8k --batch_size 500 --confirm_run_unsafe_code
 
+# prefill cuda graph 功能测试
+LOADWORKER=18 CUDA_VISIBLE_DEVICES=6,7 python -m lightllm.server.api_server \
+--model_dir /root/.cache/huggingface/hub/models--Qwen--Qwen3.5-0.8B/snapshots/2fc06364715b967f1860aea9cf38778875588b17 \
+--tp 2 \
+--port 8089 \
+--enable_prefill_cudagraph
+
+export no_proxy="localhost,127.0.0.1,0.0.0.0,::1" HF_ALLOW_CODE_EVAL=1 HF_DATASETS_OFFLINE=0 lm_eval --model local-completions --model_args '{"model":"qwen/Qwen3.5-0.8B", "base_url":"http://localhost:8089/v1/completions", "max_length": 16384}' --tasks gsm8k --batch_size 500 --confirm_run_unsafe_code
+
 
 # 测试
 LOADWORKER=18 CUDA_VISIBLE_DEVICES=6,7 python -m lightllm.server.api_server \