diff --git a/vllm/model_executor/models/bert.py b/vllm/model_executor/models/bert.py
index 77b2ef0fce5f..111b49ab8dd2 100644
--- a/vllm/model_executor/models/bert.py
+++ b/vllm/model_executor/models/bert.py
@@ -26,7 +26,7 @@
 from vllm.transformers_utils.config import (
     get_cross_encoder_activation_function)
 
-from .interfaces import SupportsCrossEncoding, SupportsV0Only
+from .interfaces import SupportsCrossEncoding, SupportsQuant, SupportsV0Only
 from .utils import WeightsMapper, maybe_prefix
 
 
@@ -313,7 +313,8 @@ def forward(self, hidden_states: torch.Tensor,
         return hidden_states
 
 
-class BertModel(nn.Module):
+class BertModel(nn.Module, SupportsQuant):
+    packed_modules_mapping = {"qkv_proj": ["query", "key", "value"]}
 
     def __init__(self,
                  *,
@@ -385,7 +386,7 @@ def load_weights(self, weights: Iterable[Tuple[str,
         return loaded_params
 
 
-class BertEmbeddingModel(nn.Module, SupportsV0Only):
+class BertEmbeddingModel(nn.Module, SupportsV0Only, SupportsQuant):
     """A model that uses Bert to provide embedding functionalities.
 
    This class encapsulates the BertModel and provides an interface for
@@ -443,7 +444,8 @@ def _build_pooler(self, pooler_config: PoolerConfig) -> Pooler:
                                                 softmax=False)
 
 
-class BertForSequenceClassification(nn.Module, SupportsCrossEncoding):
+class BertForSequenceClassification(nn.Module, SupportsCrossEncoding,
+                                    SupportsQuant):
     """A model that uses Bert to provide embedding functionalities.
 
    This class encapsulates the BertModel and provides an interface for
diff --git a/vllm/model_executor/models/blip.py b/vllm/model_executor/models/blip.py
index bedbdceb7721..f3d488926d09 100644
--- a/vllm/model_executor/models/blip.py
+++ b/vllm/model_executor/models/blip.py
@@ -16,6 +16,8 @@
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
+from .interfaces import SupportsQuant
+
 
 def get_blip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
     assert image_size % patch_size == 0
@@ -243,9 +245,10 @@ def forward(self, inputs_embeds: torch.Tensor):
         return hidden_states
 
 
-class BlipVisionModel(nn.Module):
+class BlipVisionModel(nn.Module, SupportsQuant):
     config_class = BlipVisionConfig
     main_input_name = "pixel_values"
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
 
     def __init__(
         self,
diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py
index 7adca4f0dc86..db9d42f5b86a 100644
--- a/vllm/model_executor/models/blip2.py
+++ b/vllm/model_executor/models/blip2.py
@@ -24,7 +24,8 @@
 from vllm.sequence import IntermediateTensors
 
 from .blip import BlipVisionModel
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP,
+                         SupportsQuant)
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
 
@@ -498,7 +499,8 @@ def _get_prompt_updates(
 @MULTIMODAL_REGISTRY.register_processor(Blip2MultiModalProcessor,
                                         info=Blip2ProcessingInfo,
                                         dummy_inputs=Blip2DummyInputsBuilder)
-class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
+                                    SupportsQuant):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
diff --git a/vllm/model_executor/models/bloom.py b/vllm/model_executor/models/bloom.py
index 50f48f91798a..f960075b98bc 100644
--- a/vllm/model_executor/models/bloom.py
+++ b/vllm/model_executor/models/bloom.py
@@ -42,7 +42,7 @@
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsPP, SupportsV0Only
+from .interfaces import SupportsPP, SupportsQuant, SupportsV0Only
 from .utils import (is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -279,7 +279,7 @@ def forward(
         return hidden_states
 
 
-class BloomForCausalLM(nn.Module, SupportsPP, SupportsV0Only):
+class BloomForCausalLM(nn.Module, SupportsPP, SupportsV0Only, SupportsQuant):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()