diff --git a/python/sglang/srt/layers/attention/nsa/utils.py b/python/sglang/srt/layers/attention/nsa/utils.py
index e49f3ba01..0c6e21291 100644
--- a/python/sglang/srt/layers/attention/nsa/utils.py
+++ b/python/sglang/srt/layers/attention/nsa/utils.py
@@ -8,6 +8,9 @@ import torch.nn.functional as F
 import triton
 import triton.language as tl
 
+from sglang.srt.distributed.device_communicators.pynccl_allocator import (
+    use_symmetric_memory,
+)
 from sglang.srt.layers.dp_attention import (
     DpPaddingMode,
     attn_tp_all_gather_into_tensor,
@@ -15,6 +18,7 @@ from sglang.srt.layers.dp_attention import (
     get_attention_tp_group,
     get_attention_tp_rank,
     get_attention_tp_size,
+    is_allocation_symmetric,
 )
 from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils.common import ceil_align, ceil_div
@@ -294,12 +298,15 @@ def cp_attn_tp_all_gather_reorganazied_into_tensor(
     pad_size = max_len - input_.shape[0]
     if pad_size > 0:
         input_ = F.pad(input_, (0, 0, 0, pad_size), mode="constant", value=0)
-    input_tensor_all = torch.empty(
-        max_len * attn_tp_size,
-        input_.shape[1],
-        device=input_.device,
-        dtype=input_.dtype,
-    )
+    with use_symmetric_memory(
+        get_attention_tp_group(), disabled=not is_allocation_symmetric()
+    ):
+        input_tensor_all = torch.empty(
+            max_len * attn_tp_size,
+            input_.shape[1],
+            device=input_.device,
+            dtype=input_.dtype,
+        )
     # step2
     get_attention_tp_group().cp_all_gather_into_tensor_async(
         input_tensor_all, input_, stream_op
@@ -348,9 +355,12 @@ def cp_all_gather_rerange_output(input_tensor, cp_size, forward_batch, stream):
     |   +-------------------------+
     """
     if is_nsa_prefill_cp_round_robin_split():
-        output_tensor = input_tensor.new_empty(
-            (input_tensor.shape[0] * cp_size, *input_tensor.shape[1:]),
-        )
+        with use_symmetric_memory(
+            get_attention_tp_group(), disabled=not is_allocation_symmetric()
+        ):
+            output_tensor = input_tensor.new_empty(
+                (input_tensor.shape[0] * cp_size, *input_tensor.shape[1:]),
+            )
         attn_tp_all_gather_into_tensor(
             output_tensor,
             input_tensor,