diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py
index 0662d90e79b92..cb4642edbd4fa 100644
--- a/vllm/model_executor/models/llava.py
+++ b/vllm/model_executor/models/llava.py
@@ -477,7 +477,17 @@ def _process_image_input(self,
 
         assert self.vision_tower is not None
         image_features = self._process_image_pixels(image_input)
-        return self.multi_modal_projector(image_features)
+
+        if isinstance(image_features, torch.Tensor):
+            return self.multi_modal_projector(image_features)
+
+        feature_sizes = [
+            image_feature.shape[0] for image_feature in image_features
+        ]
+
+        image_embeds = self.multi_modal_projector(torch.cat(image_features))
+        image_embeds = torch.split(image_embeds, feature_sizes)
+        return image_embeds
 
     def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
         image_input = self._parse_and_validate_image_input(**kwargs)
diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py
index f3d66c2313198..31d9b4dd684c9 100644
--- a/vllm/model_executor/models/pixtral.py
+++ b/vllm/model_executor/models/pixtral.py
@@ -1009,9 +1009,13 @@ def forward(
             for img in pixel_values
         ]
 
+        patch_embeds = [
+            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
+        ]
+        embed_sizes = [p.shape[1] for p in patch_embeds]
+
         # flatten to a single sequence
-        patch_embeds = torch.cat(
-            [p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list], dim=1)
+        patch_embeds = torch.cat(patch_embeds, dim=1)
         patch_embeds = self.ln_pre(patch_embeds)
 
         # positional embeddings
@@ -1042,6 +1046,8 @@ def forward(
         out = resolve_visual_encoder_outputs(out, feature_sample_layers, None,
                                              self.config.num_hidden_layers)
 
+        # squeeze dim 0 and split into separate tensors for each image
+        out = torch.split(torch.squeeze(out), embed_sizes)
         return out
 
     # (TODO) Add prefix argument for filtering out weights to be loaded