diff --git a/lightllm/common/kv_cache_mem_manager/deepseek2_mem_manager.py b/lightllm/common/kv_cache_mem_manager/deepseek2_mem_manager.py
index 9eb02b963..adf81a14d 100644
--- a/lightllm/common/kv_cache_mem_manager/deepseek2_mem_manager.py
+++ b/lightllm/common/kv_cache_mem_manager/deepseek2_mem_manager.py
@@ -12,7 +12,6 @@
 
 
 class Deepseek2MemoryManager(MemoryManager):
-
     operator_class = Deepseek2MemOperator
 
     def __init__(self, size, dtype, head_num, head_dim, layer_num, always_copy=False, mem_fraction=0.9):
@@ -28,14 +27,8 @@ def get_cell_size(self):
     def _init_buffers(self, size, dtype, head_num, head_dim, layer_num):
         self.kv_buffer = torch.empty((layer_num, size + 1, head_num, head_dim), dtype=dtype, device="cuda")
 
-    def alloc_paged_kv_move_buffer(self, page_num, page_size) -> torch.Tensor:
-        self.kv_move_buffer = torch.empty(
-            (page_num, page_size, self.layer_num, self.head_num, self.head_dim), dtype=self.dtype, device="cuda"
-        )
-        self._buffer_mem_indexes_tensors = [
-            torch.empty((page_size,), dtype=torch.int64, device="cpu", pin_memory=True) for _ in range(page_num)
-        ]
-        return self.kv_move_buffer
+    def get_paged_kv_move_buffer_shape(self, page_num, page_size):
+        return (page_num, page_size, self.layer_num, self.head_num, self.head_dim)
 
     def write_mem_to_page_kv_move_buffer(
         self,
diff --git a/lightllm/common/kv_cache_mem_manager/mem_manager.py b/lightllm/common/kv_cache_mem_manager/mem_manager.py
index 658d3e899..9f3bea73e 100755
--- a/lightllm/common/kv_cache_mem_manager/mem_manager.py
+++ b/lightllm/common/kv_cache_mem_manager/mem_manager.py
@@ -1,5 +1,6 @@
 import re
 import os
+import math
 import torch
 import torch.distributed as dist
 import torch.multiprocessing as mp
@@ -23,7 +24,6 @@
 
 
 class MemoryManager:
-
     operator_class = NormalMemOperator
 
     def __init__(self, size, dtype, head_num, head_dim, layer_num, always_copy=False, mem_fraction=0.9):
@@ -58,6 +58,14 @@ def get_att_input_params(self, layer_index: int) -> Tuple[Any, Any]:
     def get_cell_size(self):
         return 2 * self.head_num * self.head_dim * self.layer_num * torch._utils._element_size(self.dtype)
 
+    def get_paged_kv_move_buffer_shape(self, page_num, page_size):
+        num_kv_head = get_num_key_value_heads(get_env_start_args().model_dir)
+        return (page_num, page_size, self.layer_num, 2 * num_kv_head, self.head_dim)
+
+    def get_paged_kv_move_buffer_size_in_bytes(self, page_num, page_size):
+        shape = self.get_paged_kv_move_buffer_shape(page_num, page_size)
+        return math.prod(shape) * torch._utils._element_size(self.dtype)
+
     def profile_size(self, mem_fraction):
         if self.size is not None:
             return
@@ -65,14 +73,34 @@ def profile_size(self, mem_fraction):
         torch.cuda.empty_cache()
         world_size = dist.get_world_size()
         available_memory = get_available_gpu_memory(world_size) - get_total_gpu_memory() * (1 - mem_fraction)
+        args = get_env_start_args()
+        pd_kv_move_buffer_size_in_bytes = 0
+        if args.run_mode in ["prefill", "decode"]:
+            pd_kv_move_buffer_size_in_bytes = self.get_paged_kv_move_buffer_size_in_bytes(
+                page_num=args.pd_kv_page_num,
+                page_size=args.pd_kv_page_size,
+            )
+            available_memory -= pd_kv_move_buffer_size_in_bytes / 1024 ** 3
         cell_size = self.get_cell_size()
         self.size = int(available_memory * 1024 ** 3 / cell_size)
         if world_size > 1:
             tensor = torch.tensor(self.size, dtype=torch.int64, device=f"cuda:{get_current_device_id()}")
             dist.all_reduce(tensor, op=dist.ReduceOp.MIN)
             self.size = tensor.item()
+        if pd_kv_move_buffer_size_in_bytes > 0 and self.size <= 0:
+            raise RuntimeError(
+                "PD KV transfer page buffer reservation leaves no memory for the token KV cache; "
+                "reduce --pd_kv_page_size or --pd_kv_page_num, or increase --mem_fraction"
+            )
+        pd_kv_move_buffer_log = ""
+        if pd_kv_move_buffer_size_in_bytes > 0:
+            pd_kv_move_buffer_log = (
+                f"{str(pd_kv_move_buffer_size_in_bytes / 1024 ** 3)} GB is reserved "
+                "for the PD KV transfer page buffer\n"
+            )
         logger.info(
-            f"{str(available_memory)} GB space is available after load the model weight\n"
+            f"{str(available_memory)} GB space is available for the token KV cache\n"
+            f"{pd_kv_move_buffer_log}"
             f"{str(cell_size / 1024 ** 2)} MB is the size of one token kv cache\n"
             f"{self.size} is the profiled max_total_token_num with the mem_fraction {mem_fraction}\n"
         )
@@ -86,9 +114,8 @@ def _init_buffers(self, size, dtype, head_num, head_dim, layer_num):
         self.kv_buffer = torch.empty((layer_num, size + 1, 2 * head_num, head_dim), dtype=dtype, device="cuda")
 
     def alloc_paged_kv_move_buffer(self, page_num, page_size) -> torch.Tensor:
-        num_kv_head = get_num_key_value_heads(get_env_start_args().model_dir)
         self.kv_move_buffer = torch.empty(
-            (page_num, page_size, self.layer_num, 2 * num_kv_head, self.head_dim), dtype=self.dtype, device="cuda"
+            self.get_paged_kv_move_buffer_shape(page_num, page_size), dtype=self.dtype, device="cuda"
         )
         self._buffer_mem_indexes_tensors = [
             torch.empty((page_size,), dtype=torch.int64, device="cpu", pin_memory=True) for _ in range(page_num)