diff --git a/colossalai/zero/sharded_param/sharded_param.py b/colossalai/zero/sharded_param/sharded_param.py
index 5826dde96..6334799d0 100644
--- a/colossalai/zero/sharded_param/sharded_param.py
+++ b/colossalai/zero/sharded_param/sharded_param.py
@@ -1,7 +1,7 @@
 import torch
 import torch.distributed as dist
 from colossalai.zero.sharded_param import ShardedTensor
-from typing import Optional
+from typing import Optional, Tuple
 
 
 class ShardedParamV2(object):
@@ -40,3 +40,28 @@ class ShardedParamV2(object):
     @property
     def param_is_sharded(self):
         return self._sharded_data_tensor.is_sharded
+
+    def get_memory_usage(self) -> Tuple[int, int]:
+        """
+        get the memory usage of the param, including data and grad
+        Returns:
+            Tuple[int, int]: cuda mem usage in Byte, cpu memory usage in Byte
+        """
+        cuda_mem_use, cpu_mem_use = 0, 0
+
+        def _update_mem_use(t: Optional[torch.Tensor]):
+            if t is None:
+                return
+            assert isinstance(t, torch.Tensor)
+            nonlocal cuda_mem_use
+            nonlocal cpu_mem_use
+            if t.device.type == 'cpu':
+                cpu_mem_use += t.numel() * t.element_size()
+            elif t.device.type == 'cuda':
+                cuda_mem_use += t.numel() * t.element_size()
+
+        _update_mem_use(self.sharded_data_tensor.payload)
+        _update_mem_use(self.fp16_grad)
+        _update_mem_use(self.fp32_grad)
+
+        return cuda_mem_use, cpu_mem_use
diff --git a/tests/test_zero_data_parallel/test_shard_param.py b/tests/test_zero_data_parallel/test_shard_param.py
index 92d39a4e6..de48f1f9c 100644
--- a/tests/test_zero_data_parallel/test_shard_param.py
+++ b/tests/test_zero_data_parallel/test_shard_param.py
@@ -54,6 +54,24 @@ def _run_shard_param_v2(rank, world_size, port):
     sparam.remove_torch_payload()
     assert (param.data.numel() == 1)
 
+    # Test get memory usage
+    sparam.fp32_grad = torch.randn(2, 3)
+    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
+    assert cpu_mem_use == 2 * 3 * 4 * 2
+
+    sparam.fp16_grad = torch.randn(2, 3).cuda().half()
+    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
+    assert cpu_mem_use == 2 * 3 * 4 * 2
+    assert cuda_mem_use == 2 * 3 * 2
+
+    sparam.fp16_grad = None
+    sparam.fp32_grad = torch.randn(2, 3)
+    sparam.remove_torch_payload()
+    cuda_mem_use, cpu_mem_use = sparam.get_memory_usage()
+    assert cpu_mem_use == 2 * 3 * 4 * 2
+    assert cuda_mem_use == 0
+    print(f'cuda_mem_use {cuda_mem_use} cpu_mem_use {cpu_mem_use}')
+
 
 @pytest.mark.dist
 @pytest.mark.parametrize("world_size", [1, 2])
@@ -64,5 +82,5 @@ def test_shard_param_v2(world_size):
 
 
 if __name__ == '__main__':
-    test_shard_tensor(2)
+    # test_shard_tensor(2)
     test_shard_param_v2(2)