diff --git a/README.md b/README.md
index 8986a043..36c90cba 100644
--- a/README.md
+++ b/README.md
@@ -283,6 +283,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
| Qwen2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2) | [link](python/llm/example/GPU/HuggingFace/LLM/qwen2) |
| Qwen2.5 | | [link](python/llm/example/GPU/HuggingFace/LLM/qwen2.5) |
| Qwen-VL | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl) |
+| Qwen2-VL || [link](python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl) |
| Qwen2-Audio | | [link](python/llm/example/GPU/HuggingFace/Multimodal/qwen2-audio) |
| Aquila | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](python/llm/example/GPU/HuggingFace/LLM/aquila) |
| Aquila2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2) | [link](python/llm/example/GPU/HuggingFace/LLM/aquila2) |
diff --git a/README.zh-CN.md b/README.zh-CN.md
index c258cd5b..ba4698d0 100644
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@@ -283,6 +283,7 @@ See the demo of running [*Text-Generation-WebUI*](https://ipex-llm.readthedocs.i
| Qwen2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2) | [link](python/llm/example/GPU/HuggingFace/LLM/qwen2) |
| Qwen2.5 | | [link](python/llm/example/GPU/HuggingFace/LLM/qwen2.5) |
| Qwen-VL | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl) |
+| Qwen2-VL || [link](python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl) |
| Aquila | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](python/llm/example/GPU/HuggingFace/LLM/aquila) |
| Aquila2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2) | [link](python/llm/example/GPU/HuggingFace/LLM/aquila2) |
| MOSS | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/moss) | |
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl/README.md b/python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl/README.md
new file mode 100644
index 00000000..67cacad8
--- /dev/null
+++ b/python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl/README.md
@@ -0,0 +1,135 @@
+# Qwen2-VL
+In this directory, you will find examples on how you could use IPEX-LLM `optimize_model` API to accelerate Qwen2 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) as a reference Qwen2-VL model.
+
+## 0. Requirements
+To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Qwen2-VL model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+#### 1.1 Installation on Linux
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.11
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+
+pip install transformers==4.45.0 # install transformers which supports Qwen2-VL
+pip install accelerate==0.33.0
+pip install qwen_vl_utils
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.11 libuv
+conda activate llm
+
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+
+pip install transformers==4.45.0 # install transformers which supports Qwen2-VL
+pip install accelerate==0.33.0
+pip install qwen_vl_utils
+```
+
+### 2. Configures OneAPI environment variables for Linux
+
+> [!NOTE]
+> Skip this step if you are running on Windows.
+
+This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
+
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 3.1 Configurations for Linux
+
+
+For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+```
+
+
+
+
+
+For Intel Data Center GPU Max Series
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+
+
+
+
+For Intel iGPU
+
+```bash
+export SYCL_CACHE_PERSISTENT=1
+export BIGDL_LLM_XMX_DISABLED=1
+```
+
+
+
+#### 3.2 Configurations for Windows
+
+
+For Intel iGPU
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+
+
+
+
+For Intel Arc™ A-Series Graphics
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+
+
+> [!NOTE]
+> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+### 4. Running examples
+
+```
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Qwen2-VL model (e.g. `Qwen/Qwen2-VL-7B-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen2-VL-7B-Instruct'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### Sample Output
+##### [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)
+```log
+-------------------- Input Image --------------------
+http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
+-------------------- Prompt --------------------
+Describe this image.
+-------------------- Output --------------------
+The image shows a young girl holdinging a white teddy bear in a pink dress in front of a stone wall.
+```
+
+The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)):
+
+
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl/generate.py b/python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl/generate.py
new file mode 100644
index 00000000..c6b348f7
--- /dev/null
+++ b/python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl/generate.py
@@ -0,0 +1,116 @@
+#
+# 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.
+#
+
+import torch
+import time
+import argparse
+import numpy as np
+
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+from ipex_llm import optimize_model
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description='Predict Tokens using generate() API for Qwen2-VL-7B-Instruct model')
+ parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen2-VL-7B-Instruct",
+ help='The huggingface repo id for the Qwen2-VL model to be downloaded'
+ ', or the path to the huggingface checkpoint folder')
+ parser.add_argument('--prompt', type=str, default="Describe this image.",
+ help='Prompt to infer')
+ parser.add_argument('--image-url-or-path', type=str,
+ default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg' ,
+ help='The URL or path to the image to infer')
+
+ parser.add_argument('--n-predict', type=int, default=32,
+ help='Max tokens to predict')
+
+ args = parser.parse_args()
+ model_path = args.repo_id_or_model_path
+
+ model = Qwen2VLForConditionalGeneration.from_pretrained(model_path,
+ trust_remote_code=True,
+ torch_dtype='auto',
+ low_cpu_mem_usage=True,
+ use_cache=True,)
+
+ model = optimize_model(model, low_bit='sym_int4', modules_to_not_convert=["visual"])
+
+ # Use .float() for better output, and use .half() for better speed
+ model = model.half().to("xpu")
+
+ # The following code for generation is adapted from https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct#quickstart
+
+ # The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
+ min_pixels = 256*28*28
+ max_pixels = 1280*28*28
+ processor = AutoProcessor.from_pretrained(model_path, min_pixels=min_pixels, max_pixels=max_pixels)
+
+ prompt = args.prompt
+ image_path = args.image_url_or_path
+
+ messages = [
+ {
+ "role": "user",
+ "content": [
+ {
+ "type": "image",
+ "image": image_path,
+ },
+ {"type": "text", "text": prompt},
+ ],
+ }
+ ]
+ text = processor.apply_chat_template(
+ messages, tokenize=False, add_generation_prompt=True
+ )
+ image_inputs, video_inputs = process_vision_info(messages)
+ inputs = processor(
+ text=[text],
+ images=image_inputs,
+ videos=video_inputs,
+ padding=True,
+ return_tensors="pt",
+ )
+ inputs = inputs.to('xpu')
+
+ with torch.inference_mode():
+ # warmup
+ generated_ids = model.generate(
+ **inputs,
+ max_new_tokens=args.n_predict
+ )
+
+ st = time.time()
+ generated_ids = model.generate(
+ **inputs,
+ max_new_tokens=args.n_predict
+ )
+ torch.xpu.synchronize()
+ end = time.time()
+ generated_ids = generated_ids.cpu()
+ generated_ids = [
+ output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
+ ]
+
+ response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+ print(f'Inference time: {end-st} s')
+ print('-'*20, 'Input Image', '-'*20)
+ print(image_path)
+ print('-'*20, 'Prompt', '-'*20)
+ print(prompt)
+ print('-'*20, 'Output', '-'*20)
+ print(response)