diff --git a/QEfficient/transformers/models/mllama/modeling_mllama.py b/QEfficient/transformers/models/mllama/modeling_mllama.py
index 8d2141240..6f35b9b8f 100644
--- a/QEfficient/transformers/models/mllama/modeling_mllama.py
+++ b/QEfficient/transformers/models/mllama/modeling_mllama.py
@@ -1133,24 +1133,24 @@ def get_dummy_inputs(self, kv_offload: bool = False):
 
         if vis_cfg := getattr(self.config, "vision_config", None):
             img_size = getattr(vis_cfg, "image_size", 448)
-            max_num_img_tiles = getattr(vis_cfg, "max_num_tiles", 4)
+            max_num_tiles = getattr(vis_cfg, "max_num_tiles", 4)
         else:
             img_size = 448
-            max_num_img_tiles = 4
+            max_num_tiles = 4
 
         # vision inputs
         vision_inputs = {
             "pixel_values": torch.zeros(
-                (BS, MAX_NUM_IMG, max_num_img_tiles, NUM_CHANNEL, img_size, img_size), dtype=torch.float32
+                (BS, MAX_NUM_IMG, max_num_tiles, NUM_CHANNEL, img_size, img_size), dtype=torch.float32
             ),
             "aspect_ratio_ids": torch.ones((BS, MAX_NUM_IMG), dtype=torch.int64),
-            "aspect_ratio_mask": torch.ones((BS, MAX_NUM_IMG, max_num_img_tiles), dtype=torch.int64),
+            "aspect_ratio_mask": torch.ones((BS, MAX_NUM_IMG, max_num_tiles), dtype=torch.int64),
         }
 
         # lang_inputs
         lang_inputs = {
             "input_ids": torch.zeros((BS, SEQ_LEN), dtype=torch.int64),
-            "cross_attention_mask": torch.zeros((BS, SEQ_LEN, MAX_NUM_IMG, max_num_img_tiles), dtype=torch.int64),
+            "cross_attention_mask": torch.zeros((BS, SEQ_LEN, MAX_NUM_IMG, max_num_tiles), dtype=torch.int64),
             "attention_mask": torch.ones((BS, SEQ_LEN), dtype=torch.int64),
         }
 
@@ -1201,6 +1201,7 @@ def get_specializations(
     ):
         vis_cfg = self.config.vision_config
         max_num_images = compiler_options.pop("max_num_images", 1)
+        max_num_tiles = compiler_options.pop("max_num_tiles", 4)
         prefill_seq_len = prefill_seq_len if prefill_seq_len else 32
         ctx_len = ctx_len if ctx_len else 128
         if img_size is None and hasattr(vis_cfg, "image_size"):
@@ -1209,13 +1210,21 @@ def get_specializations(
             img_size = 448
             logger.warning("Setting `img_size=448` as it was neither passed nor found in vision_config")
 
-        vision = [{"batch_size": batch_size, "max_num_images": max_num_images, "img_size": img_size}]
+        vision = [
+            {
+                "batch_size": batch_size,
+                "max_num_images": max_num_images,
+                "max_num_tiles": max_num_tiles,
+                "img_size": img_size,
+            }
+        ]
         lang = [
             {
                 "batch_size": batch_size,
                 "seq_len": prefill_seq_len,
                 "ctx_len": ctx_len,
                 "max_num_images": max_num_images,
+                "max_num_tiles": max_num_tiles,
                 "img_size": img_size,
             },
             {
@@ -1223,6 +1232,7 @@ def get_specializations(
                 "seq_len": "1",
                 "ctx_len": ctx_len,
                 "max_num_images": max_num_images,
+                "max_num_tiles": max_num_tiles,
                 "img_size": img_size,
             },
         ]
@@ -1241,15 +1251,15 @@ def get_onnx_dynamic_axes(self, kv_offload: bool = False):
         cross_attention_layers = txt_cfg.cross_attention_layers
 
         vision_dynamic_axes = {
-            "pixel_values": {0: "batch_size", 1: "max_num_images", 4: "img_size", 5: "img_size"},
+            "pixel_values": {0: "batch_size", 1: "max_num_images", 2: "max_num_tiles", 4: "img_size", 5: "img_size"},
             "aspect_ratio_ids": {0: "batch_size", 1: "max_num_images"},
-            "aspect_ratio_mask": {0: "batch_size", 1: "max_num_images"},
+            "aspect_ratio_mask": {0: "batch_size", 1: "max_num_images", 2: "max_num_tiles"},
         }
 
         lang_dynamic_axes = {
             "input_ids": {0: "batch_size", 1: "seq_len"},
             "position_ids": {0: "batch_size", 1: "seq_len"},
-            "cross_attention_mask": {0: "batch_size", 1: "seq_len", 2: "max_num_images"},
+            "cross_attention_mask": {0: "batch_size", 1: "seq_len", 2: "max_num_images", 3: "max_num_tiles"},
         }
 
         for i in range(num_hidden_layers):