diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py
index 9689615a..05b21e0a 100644
--- a/python/llm/src/ipex_llm/transformers/convert.py
+++ b/python/llm/src/ipex_llm/transformers/convert.py
@@ -1256,6 +1256,18 @@ def _optimize_post(model, lightweight_bmm=False):
         )
         convert_forward(model, module.InternLM2Model, internlm_xcomposser2_model_forward)
         model.chat = MethodType(internlm_xcomposser2_chat, model)
+    elif model.config.model_type == "internvl_chat":
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from ipex_llm.transformers.models.internvl import internvl_chat
+        from ipex_llm.transformers.models.internvl import internvl_batch_chat
+        model.get_conv_template = module.get_conv_template
+        model.chat = MethodType(internvl_chat, model)
+        model.batch_chat = MethodType(internvl_batch_chat, model)
+        if model.vision_model.__class__.__name__ == "InternVisionModel":
+            from ipex_llm.transformers.models.internvl import _get_pos_embed
+            vision_embedding = model.vision_model.embeddings
+            vision_embedding._get_pos_embed = MethodType(_get_pos_embed, vision_embedding)
     elif model.config.model_type == "qwen":
         if hasattr(model.config, "visual"):
             # for Qwen-VL-Chat
diff --git a/python/llm/src/ipex_llm/transformers/models/internvl.py b/python/llm/src/ipex_llm/transformers/models/internvl.py
new file mode 100644
index 00000000..633f337a
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/models/internvl.py
@@ -0,0 +1,165 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Some parts of this file is adapted from
+# https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_intern_vit.py
+# which is licensed under MIT:
+#
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+#
+
+import torch
+from ipex_llm.utils.common.log4Error import invalidInputError
+
+
+def _get_pos_embed(self, pos_embed, H, W):
+    target_dtype = pos_embed.dtype
+    device = pos_embed.device
+    pos_embed = pos_embed.float().reshape(
+        1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1
+    ).permute(0, 3, 1, 2)
+    # ipex-llm change start: call interpolate on CPU to fix bug
+    pos_embed = torch.nn.functional.interpolate(
+        pos_embed.to('cpu'), size=(H, W), mode='bicubic', align_corners=False
+    ).reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype).to(device)
+    # ipex-llm changes end
+    return pos_embed
+
+
+def internvl_chat(self, tokenizer, pixel_values, question, generation_config,
+                  history=None, return_history=False, num_patches_list=None,
+                  IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
+                  IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False):
+
+    if history is None and pixel_values is not None and '<image>' not in question:
+        question = '<image>\n' + question
+
+    if num_patches_list is None:
+        num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
+    invalidInputError(pixel_values is None or len(pixel_values) == sum(num_patches_list),
+                      "wrong num_patches_list length")
+
+    img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+    self.img_context_token_id = img_context_token_id
+
+    template = self.get_conv_template(self.template)
+    template.system_message = self.system_message
+    eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
+
+    history = [] if history is None else history
+    for (old_question, old_answer) in history:
+        template.append_message(template.roles[0], old_question)
+        template.append_message(template.roles[1], old_answer)
+    template.append_message(template.roles[0], question)
+    template.append_message(template.roles[1], None)
+    query = template.get_prompt()
+
+    if verbose and pixel_values is not None:
+        image_bs = pixel_values.shape[0]
+        print(f'dynamic ViT batch size: {image_bs}')
+
+    for num_patches in num_patches_list:
+        image_tokens = (IMG_START_TOKEN
+                        + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches
+                        + IMG_END_TOKEN)
+        query = query.replace('<image>', image_tokens, 1)
+    model_inputs = tokenizer(query, return_tensors='pt')
+
+    # ipex-llm changes start: move input_ids and attention_mask to xpu
+    input_ids = model_inputs['input_ids'].to(self.device)
+    attention_mask = model_inputs['attention_mask'].to(self.device)
+    if pixel_values is not None:
+        pixel_values = pixel_values.to(dtype=self.dtype, device=self.device)
+    # ipex-llm changes end
+
+    generation_config['eos_token_id'] = eos_token_id
+    generation_output = self.generate(
+        pixel_values=pixel_values,
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        **generation_config
+    )
+    response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
+    response = response.split(template.sep)[0].strip()
+    history.append((question, response))
+    if return_history:
+        return response, history
+    else:
+        query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
+        query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
+        if verbose:
+            print(query_to_print, response)
+        return response
+
+
+def internvl_batch_chat(self, tokenizer, pixel_values, questions, generation_config,
+                        num_patches_list=None, history=None, return_history=False,
+                        IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
+                        IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
+    invalidInputError(history is None and not return_history,
+                      'Now multi-turn chat is not supported in batch_chat.')
+
+    if image_counts is not None:
+        num_patches_list = image_counts
+        print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
+
+    img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+    self.img_context_token_id = img_context_token_id
+
+    if verbose and pixel_values is not None:
+        image_bs = pixel_values.shape[0]
+        print(f'dynamic ViT batch size: {image_bs}')
+
+    queries = []
+    for idx, num_patches in enumerate(num_patches_list):
+        question = questions[idx]
+        if pixel_values is not None and '<image>' not in question:
+            question = '<image>\n' + question
+        template = self.get_conv_template(self.template)
+        template.append_message(template.roles[0], question)
+        template.append_message(template.roles[1], None)
+        query = template.get_prompt()
+
+        image_tokens = (IMG_START_TOKEN
+                        + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches
+                        + IMG_END_TOKEN)
+        query = query.replace('<image>', image_tokens, 1)
+        queries.append(query)
+
+    tokenizer.padding_side = 'left'
+    model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
+
+    # ipex-llm changes start: move input_ids and attention_mask to xpu
+    input_ids = model_inputs['input_ids'].to(self.device)
+    attention_mask = model_inputs['attention_mask'].to(self.device)
+    if pixel_values is not None:
+        pixel_values = pixel_values.to(dtype=self.dtype, device=self.device)
+    # ipex-llm changes end
+
+    eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
+    generation_config['eos_token_id'] = eos_token_id
+    generation_output = self.generate(
+        pixel_values=pixel_values,
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        **generation_config
+    )
+    responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
+    responses = [response.split(template.sep)[0].strip() for response in responses]
+    return responses