diff --git a/vllm/model_executor/models/transformers/base.py b/vllm/model_executor/models/transformers/base.py
index 510abe82ce7..879121145df 100644
--- a/vllm/model_executor/models/transformers/base.py
+++ b/vllm/model_executor/models/transformers/base.py
@@ -78,6 +78,17 @@ if TYPE_CHECKING:
 logger = init_logger(__name__)
 
 
+class ScaledVocabParallelEmbedding(VocabParallelEmbedding):
+    """`VocabParallelEmbedding` that scales its output."""
+
+    def __init__(self, *args, embed_scale: float, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.embed_scale = embed_scale
+
+    def forward(self, input_: torch.Tensor) -> torch.Tensor:
+        return super().forward(input_) * self.embed_scale
+
+
 class Base(
     nn.Module,
     VllmModel,
@@ -156,22 +167,26 @@ class Base(
         self.attention_instances = self.create_attention_instances()
 
         # Input embeddings
-        self.embed_scale = None
         input_embeddings = self.model.get_input_embeddings()
         if not isinstance(input_embeddings, PPMissingLayer):
-            # Some models scale embeddings inside the input embedding layer
-            self.embed_scale = getattr(input_embeddings, "embed_scale", None)
             names = ("embedding_size", "hidden_size")
             embedding_dim = getattr_iter(self.text_config, names, None)
             assert embedding_dim is not None
-            self.model.set_input_embeddings(
-                VocabParallelEmbedding(
-                    self.text_config.vocab_size,
-                    embedding_dim=embedding_dim,
-                    org_num_embeddings=self.text_config.vocab_size,
-                    quant_config=self.quant_config,
-                )
+            embedding_kwargs = dict(
+                num_embeddings=self.text_config.vocab_size,
+                embedding_dim=embedding_dim,
+                org_num_embeddings=self.text_config.vocab_size,
+                quant_config=self.quant_config,
             )
+            embed_scale = getattr(input_embeddings, "embed_scale", None)
+            if embed_scale is not None:
+                # Some models scale embeddings inside the input embedding layer
+                new_input_embeddings = ScaledVocabParallelEmbedding(
+                    **embedding_kwargs, embed_scale=float(embed_scale)
+                )
+            else:
+                new_input_embeddings = VocabParallelEmbedding(**embedding_kwargs)
+            self.model.set_input_embeddings(new_input_embeddings)
 
         # Initialize any parameters that have not had their modules replaced
         self.init_parameters(self.model)
@@ -590,10 +605,7 @@ class Base(
         _init_parameters(module, dtype)
 
     def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
-        inputs_embeds = self.model.get_input_embeddings()(input_ids)
-        if self.embed_scale is not None:
-            inputs_embeds *= self.embed_scale
-        return inputs_embeds
+        return self.model.get_input_embeddings()(input_ids)
 
     def forward(
         self,
@@ -608,16 +620,6 @@ class Base(
             input_ids = None
             inputs_embeds = intermediate_tensors["hidden_states"]
 
-        # If the model scales embeddings inside the input embedding layer we must
-        # ensure they are scaled here since VocabParallelEmbedding will not do it
-        if (
-            self.embed_scale is not None
-            and input_ids is not None
-            and inputs_embeds is None
-        ):
-            inputs_embeds = self.embed_input_ids(input_ids)
-            input_ids = None
-
         # Add batch dimension before entering Transformers model
         if input_ids is not None and input_ids.ndim == 1:
             # [seq_len] -> [1, seq_len]