diff --git a/vllm/model_executor/model_loader/dummy_loader.py b/vllm/model_executor/model_loader/dummy_loader.py
index 156071f1d..98f82a5b7 100644
--- a/vllm/model_executor/model_loader/dummy_loader.py
+++ b/vllm/model_executor/model_loader/dummy_loader.py
@@ -4,8 +4,19 @@ import torch.nn as nn
 
 from vllm.config import ModelConfig
 from vllm.config.load import LoadConfig
+from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
 from vllm.model_executor.model_loader.base_loader import BaseModelLoader
-from vllm.model_executor.model_loader.weight_utils import initialize_dummy_weights
+from vllm.model_executor.model_loader.reload.layerwise import (
+    _get_original_loader,
+    get_layerwise_info,
+)
+from vllm.model_executor.model_loader.reload.meta import materialize_layer
+from vllm.model_executor.model_loader.reload.types import LayerReloadingInfo
+from vllm.model_executor.model_loader.reload.utils import get_layer_tensors
+from vllm.model_executor.model_loader.weight_utils import (
+    initialize_dummy_weights,
+    initialize_single_dummy_weight,
+)
 
 
 class DummyModelLoader(BaseModelLoader):
@@ -23,6 +34,31 @@ class DummyModelLoader(BaseModelLoader):
         pass  # Nothing to download
 
     def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
-        # NOTE(woosuk): For accurate performance evaluation, we assign
-        # random values to the weights.
-        initialize_dummy_weights(model, model_config)
+        for layer in model.modules():
+            info = get_layerwise_info(layer)
+            if info.can_load():
+                self._process_online_quant_layer(layer, info)
+            else:
+                # NOTE(woosuk): For accurate performance evaluation, we assign
+                # random values to the weights.
+                initialize_dummy_weights(layer, model_config)
+
+    def _process_online_quant_layer(
+        self,
+        layer: nn.Module,
+        info: LayerReloadingInfo,
+    ) -> None:
+        """Materialize, apply dummy weights, and run quantization processing."""
+        materialize_layer(layer, info)
+
+        for tensor in get_layer_tensors(layer).values():
+            initialize_single_dummy_weight(tensor)
+
+        for param in get_layer_tensors(layer).values():
+            param.weight_loader = _get_original_loader(param)
+
+        quant_method = getattr(layer, "quant_method", None)
+        if isinstance(quant_method, QuantizeMethodBase):
+            quant_method.process_weights_after_loading(layer)
+
+        info.reset()
diff --git a/vllm/model_executor/model_loader/reload/layerwise.py b/vllm/model_executor/model_loader/reload/layerwise.py
index f7b1c13bb..5d4af7d1f 100644
--- a/vllm/model_executor/model_loader/reload/layerwise.py
+++ b/vllm/model_executor/model_loader/reload/layerwise.py
@@ -11,10 +11,7 @@ from vllm.config import ModelConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention, MLAAttention
 from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
-from vllm.model_executor.model_loader.weight_utils import (
-    default_weight_loader,
-    initialize_single_dummy_weight,
-)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from .meta import (
     capture_layer_to_meta,
@@ -224,7 +221,7 @@ def finalize_layerwise_processing(model: torch.nn.Module, model_config: ModelCon
 
         # No weights were loaded
         elif info.load_numel <= 0:
-            # first load but received no weights. This happens on dummy load
+            # first load: checkpoint did not contain weights for this layer
             if info.kernel_tensors is None:
                 _layerwise_process(layer, info)
                 continue
@@ -262,12 +259,6 @@ def _layerwise_process(layer: torch.nn.Module, info: LayerReloadingInfo):
     # Materialize layer tensors onto device
     materialize_layer(layer, info)
 
-    # If no weights were loaded (e.g. dummy loading), initialize with
-    # small random values to avoid NaN from zero/garbage data
-    if len(info.loaded_weights) <= 0:
-        for tensor in get_layer_tensors(layer).values():
-            initialize_single_dummy_weight(tensor)
-
     # Reset online quantization flag so process_weights_after_loading
     # will run again during reload
     if hasattr(layer, "_already_called_process_weights_after_loading"):