diff --git a/examples/torch-integration/customized_comm_with_tuning.py b/examples/torch-integration/customized_comm_with_tuning.py
index 8d1efd53..6da9d713 100644
--- a/examples/torch-integration/customized_comm_with_tuning.py
+++ b/examples/torch-integration/customized_comm_with_tuning.py
@@ -83,9 +83,7 @@ class CustomizedComm:
         self.world_size = comm.nranks
         self.nranks_per_node = comm.nranks_per_node
         nvlink_domain_nranks = int(os.environ.get("MSCCLPP_IPC_DOMAIN_NRANKS", "0"))
-        self.mnnvl_domain = self.world_size > 1 and nvlink_domain_nranks >= self.world_size
-        self.multi_node = self.world_size > self.nranks_per_node and not self.mnnvl_domain
-        self.multi_host_mnnvl = self.mnnvl_domain and self.world_size > self.nranks_per_node
+        self.multi_host_mnnvl = nvlink_domain_nranks >= self.world_size and self.world_size > self.nranks_per_node
         self.symmetric_memory = symmetric_memory
         self._nvls = mscclpp.is_nvls_supported()
 
@@ -108,7 +106,7 @@ class CustomizedComm:
         pkt = self._algo("allreduce", "default_allreduce_nvls_packet")
         if self._nvls and pkt:
             return (pkt, 0, 0)
-        if self.multi_node or self.multi_host_mnnvl:
+        if self.multi_host_mnnvl:
             rsag = self._algo("allreduce", "default_allreduce_rsag")
             if rsag:
                 return (rsag, 0, 0)
@@ -194,18 +192,6 @@ class CustomizedComm:
                 if a:
                     out.append(a)
             return out
-        if self.multi_node:
-            a = self._algo("allreduce", "default_allreduce_nvls_packet")
-            if self._nvls and a:
-                out.append(a)
-            a = self._algo("allreduce", "default_allreduce_packet")
-            if a:
-                out.append(a)
-            if size >= 512 << 10:
-                a = self._algo("allreduce", "default_allreduce_rsag")
-                if a:
-                    out.append(a)
-            return out
         if size <= 4 << 20:
             a = self._algo("allreduce", "default_allreduce_packet")
             if a:
@@ -230,7 +216,7 @@ class CustomizedComm:
         return out
 
     def _ag_candidates(self):
-        if self.multi_node or self.multi_host_mnnvl:
+        if self.multi_host_mnnvl:
             return []
         a = self._algo("allgather", "default_allgather_fullmesh2")
         return [a] if a else []
@@ -356,7 +342,7 @@ class CustomizedComm:
         )
 
     def all_gather(self, output_tensor, input_tensor, stream=None):
-        if self.multi_node or self.multi_host_mnnvl:
+        if self.multi_host_mnnvl:
             raise RuntimeError("all_gather in this example currently supports only single-node runs")
         sz = _round_pow2(input_tensor.nbytes)
         if sz not in self._tune_cache["allgather"]:
@@ -497,7 +483,7 @@ def main():
     cc.barrier()
     torch.cuda.synchronize()
 
-    if cc.multi_node or cc.multi_host_mnnvl:
+    if cc.multi_host_mnnvl:
         if cc.rank == 0:
             print("Skipping allgather benchmark on multi-node: this example's allgather path is single-node only.")
     else: