diff --git a/serve/mlc_serve/model/tvm_model.py b/serve/mlc_serve/model/tvm_model.py
index f20370275b..653da250d8 100644
--- a/serve/mlc_serve/model/tvm_model.py
+++ b/serve/mlc_serve/model/tvm_model.py
@@ -196,10 +196,6 @@ def get_used_memory(self):
             peak_memory = get_used_memory_func(
                 tvm.device("cuda", 0)
             ).debug_get_from_remote(0)
-
-            # TODO: temp hack to switch the VM allocator to eager recycling mode on all devices
-            for i in range(1, self.num_shards):
-                get_used_memory_func(tvm.device("cuda", i)).debug_get_from_remote(i)
         else:
             params = self.params
 
@@ -230,7 +226,23 @@ def profile_memory_usage(self, seq_lens):
             positions = copy_to_worker_0(self.disco_session, positions)
             seq_lens = copy_to_worker_0(self.disco_session, seq_lens)
 
+            start_profiling_func = self.disco_session.get_global_func(
+                "vm.memory_manager.start_profiling"
+            )
+            stop_profiling_func = self.disco_session.get_global_func(
+                "vm.memory_manager.stop_profiling"
+            )
+        else:
+            start_profiling_func = tvm.get_global_func(
+                "vm.memory_manager.start_profiling"
+            )
+            stop_profiling_func = tvm.get_global_func(
+                "vm.memory_manager.stop_profiling"
+            )
+
+        start_profiling_func()
         self.mod["evaluate"](input_ids, positions, seq_lens, self.params)
+        stop_profiling_func()
 
         return self.get_used_memory()