diff --git a/python/llm/example/pytorch-models/README.md b/python/llm/example/pytorch-models/README.md
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--- a/python/llm/example/pytorch-models/README.md
+++ b/python/llm/example/pytorch-models/README.md
@@ -8,6 +8,7 @@ You can use `optimize_model` API to accelerate general PyTorch models on Intel s
 | ChatGLM   | [link](chatglm)   | 
 | Openai Whisper | [link](openai-whisper)   | 
 | BERT | [link](bert)   | 
+| Bark | [link](bark)   |
 
 ## Recommended Requirements
 To run the examples, we recommend using Intel® Xeon® processors (server), or >= 12th Gen Intel® Core™ processor (client).
diff --git a/python/llm/example/pytorch-models/bark/README.md b/python/llm/example/pytorch-models/bark/README.md
new file mode 100644
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@@ -0,0 +1,62 @@
+# Bark
+In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Bark models. For illustration purposes, we utilize the [suno/bark](https://huggingface.co/suno/bark) as reference Bark models.
+
+## Requirements
+To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
+
+## Example: Synthesize speech with the given input text
+In the example [synthesize_speech.py](./synthesize_speech.py), we show a basic use case for Bark model to synthesize speech based on the given text, with BigDL-LLM INT4 optimizations.
+### 1. Install
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
+pip install TTS scipy
+```
+
+### 2. Download Bark model
+Before running the example, you need to download Bark model to local folder:
+```python
+from huggingface_hub import snapshot_download
+
+model_path = snapshot_download(repo_id='suno/bark',
+                               local_dir='bark/') # you can change `local_dir` parameter to specify any local folder
+```
+
+Please refer to [here](https://huggingface.co/docs/huggingface_hub/guides/download#download-files-to-local-folder) for more information about `snapshot_download`.
+
+### 3. Run
+After setting up the Python environment and downloading Bark model, you could run the example by following steps.
+
+#### 3.1 Client
+On client Windows machines, it is recommended to run directly with full utilization of all cores:
+```powershell
+# make sure `--model-path` corresponds to the local folder of downloaded model
+python ./synthesize_speech.py --model-path 'bark/' --text "This is an example text for synthesize speech."
+```
+More information about arguments can be found in [Arguments Info](#33-arguments-info) section.
+
+#### 3.2 Server
+For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
+
+E.g. on Linux,
+```bash
+# set BigDL-Nano env variables
+source bigdl-nano-init
+
+# e.g. for a server with 48 cores per socket
+export OMP_NUM_THREADS=48
+# make sure `--model-path` corresponds to the local folder of downloaded model
+numactl -C 0-47 -m 0 python ./synthesize_speech.py --model-path 'bark/' --text "This is an example text for synthesize speech."
+```
+More information about arguments can be found in [Arguments Info](#33-arguments-info) section.
+
+#### 3.3 Arguments Info
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--model-path MODEL_PATH`: **required**, argument defining the local path to the Bark model checkpoint folder.
+- `--text TEXT`: argument defining the text to synthesize speech. It is default to be `"This is an example text for synthesize speech."`.
diff --git a/python/llm/example/pytorch-models/bark/synthesize_speech.py b/python/llm/example/pytorch-models/bark/synthesize_speech.py
new file mode 100644
index 00000000..bb8a61b8
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+++ b/python/llm/example/pytorch-models/bark/synthesize_speech.py
@@ -0,0 +1,53 @@
+#
+# 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 scipy
+import time
+import argparse
+
+from TTS.tts.configs.bark_config import BarkConfig
+from TTS.tts.models.bark import Bark
+from bigdl.llm import optimize_model
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Synthesize speech with the given input text using Bark model')
+    parser.add_argument('--model-path', type=str, required=True,
+                        help='The local path to the Bark model checkpoint folder')
+    parser.add_argument('--text', type=str, default="This is an example text for synthesize speech.",
+                        help='Text to synthesize speech')
+
+    args = parser.parse_args()
+    model_path = args.model_path
+    
+    # Load model
+    config = BarkConfig()
+    model = Bark.init_from_config(config)
+    model.load_checkpoint(config, checkpoint_dir=model_path, eval=True)
+    
+    # With only one line to enable BigDL-LLM optimization on model
+    model = optimize_model(model)
+
+    # Synthesize speech with the given input
+    text = args.text
+    st = time.time()
+    output_dict = model.synthesize(text, config, speaker_id="random", voice_dirs=None) # with random speaker
+    end = time.time()
+    print(f'Time cost: {end-st} s')
+
+    # Save the speech as a .wav file using scipy
+    sampling_rate = model.config.sample_rate
+    scipy.io.wavfile.write("bark_out.wav", rate=sampling_rate, data=output_dict["wav"].squeeze())