diff --git a/vllm/model_executor/models/gemma3.py b/vllm/model_executor/models/gemma3.py
index 40f6d100c767e..70f72b5cb9beb 100644
--- a/vllm/model_executor/models/gemma3.py
+++ b/vllm/model_executor/models/gemma3.py
@@ -39,7 +39,10 @@ from vllm.model_executor.layers.linear import (
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
 from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
@@ -532,12 +535,20 @@ class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
         super().__init__()
         self.config = config
-        # currently all existing Gemma models have `tie_word_embeddings` enabled
-        assert config.tie_word_embeddings
         self.quant_config = quant_config
         self.model = Gemma3Model(
             vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
         )
+
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "lm_head"),
+        )
+        if config.tie_word_embeddings:
+            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+
         self.logits_processor = LogitsProcessor(
             config.vocab_size, soft_cap=config.final_logit_softcapping
         )
@@ -565,7 +576,7 @@ class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self,
         hidden_states: torch.Tensor,
     ) -> torch.Tensor | None:
-        logits = self.logits_processor(self.model.embed_tokens, hidden_states)
+        logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: