diff --git a/nemo/export/trt_llm/converter/utils.py b/nemo/export/trt_llm/converter/utils.py
index f1882bbea3a1..d7abe38c936a 100755
--- a/nemo/export/trt_llm/converter/utils.py
+++ b/nemo/export/trt_llm/converter/utils.py
@@ -63,8 +63,8 @@
 
 
 def save_val(val, dir, key, tp_num=None):
-    if tp_num:
-        key += f".{tp_num}.bin"
+    suffix = f".{tp_num}.bin" if tp_num else ''
+    tp_key = key + suffix
 
     global weights_dict
     # Transpose linear layer weights to the correct shape.
@@ -74,14 +74,14 @@ def save_val(val, dir, key, tp_num=None):
             val = val.reshape(val.shape[0], -1)
             val = torch.transpose(val, 0, 1)
         if key not in weights_dict:
-            weights_dict[key] = torch.empty(
+            weights_dict[tp_key] = torch.empty(
                 val.size(), dtype=val.dtype, layout=val.layout, device="cpu", pin_memory=True
             )
-        weights_dict[key].copy_(val, non_blocking=True)
+        weights_dict[tp_key].copy_(val, non_blocking=True)
     else:
         if len(val.shape) >= 2:
             val = np.ascontiguousarray(np.transpose(val.reshape(val.shape[0], -1), [1, 0]))
-        weights_dict[key] = val
+        weights_dict[tp_key] = val
 
 
 def save_split(split_vals, dir, key, i, split_factor):
@@ -91,11 +91,10 @@ def save_split(split_vals, dir, key, i, split_factor):
 
 def save_expert_split(split_vals, dir, key, i, split_factor):
     for j, val in enumerate(split_vals):
+        suffix = f".{tp_num}.bin" if tp_num else ''
         tp_num = i * split_factor + j
-        if tp_num:
-            key += f".{tp_num}.bin"
         global weights_dict
-        weights_dict[key] = val
+        weights_dict[key + suffix] = val
 
 
 def generate_int8(weights, act_range, is_qkv=False, multi_query_mode=False):