diff --git a/vllm/model_executor/models/gemma3.py b/vllm/model_executor/models/gemma3.py
index 55c96f649fbeb..fb8eccc55078a 100644
--- a/vllm/model_executor/models/gemma3.py
+++ b/vllm/model_executor/models/gemma3.py
@@ -59,16 +59,23 @@ class Gemma3MLP(nn.Module):
         intermediate_size: int,
         hidden_activation: str,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
+            hidden_size,
+            [intermediate_size] * 2,
             bias=False,
-            quant_config=quant_config)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           quant_config=quant_config)
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
         if hidden_activation != "gelu_pytorch_tanh":
             raise ValueError(
                 "Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
@@ -125,12 +132,14 @@ class Gemma3Attention(nn.Module):
             self.total_num_kv_heads,
             bias=config.attention_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=config.attention_bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
         )
 
         self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
@@ -293,6 +302,7 @@ class Gemma3DecoderLayer(nn.Module):
             intermediate_size=config.intermediate_size,
             hidden_activation=config.hidden_activation,
             quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
         )
         self.input_layernorm = GemmaRMSNorm(config.hidden_size,
                                             eps=config.rms_norm_eps)
@@ -344,6 +354,7 @@ class Gemma3Model(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
+            prefix=f"{prefix}.embed_tokens",
         )
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,