diff --git a/docs/source/index.md b/docs/source/index.md
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@@ -48,6 +48,7 @@ user_guide/supported_models
 user_guide/env_vars
 user_guide/additional_config
 user_guide/graph_mode.md
+user_guide/quantization.md
 user_guide/release_notes
 :::
 
diff --git a/docs/source/user_guide/quantization.md b/docs/source/user_guide/quantization.md
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+# Quantization Guide
+
+Model quantization is a technique that reduces the size and computational requirements of a model by lowering the data precision of the weights and activation values in the model, thereby saving the memory and improving the inference speed.
+
+Since 0.9.0rc2 version, quantization feature is experimentally supported in vLLM Ascend. Users can enable quantization feature by specifying `--quantization ascend`. Currently, only Qwen, DeepSeek series models are well tested. We’ll support more quantization algorithm and models in the future.
+
+## Install modelslim
+
+To quantize a model, users should install [ModelSlim](https://gitee.com/ascend/msit/blob/master/msmodelslim/README.md) which is the Ascend compression and acceleration tool. It is an affinity-based compression tool designed for acceleration, using compression as its core technology and built upon the Ascend platform.
+
+Currently, only the specific tag [modelslim-VLLM-8.1.RC1.b020_001](https://gitee.com/ascend/msit/blob/modelslim-VLLM-8.1.RC1.b020_001/msmodelslim/README.md) of modelslim works with vLLM Ascend. Please do not install other version until modelslim master version is avaliable for vLLM Ascend in the future.
+
+Install modelslim:
+```bash
+git clone https://gitee.com/ascend/msit -b modelslim-VLLM-8.1.RC1.b020_001
+cd msit/msmodelslim
+bash install.sh
+pip install accelerate
+```
+
+## Quantize model
+
+Take [DeepSeek-V2-Lite](https://modelscope.cn/models/deepseek-ai/DeepSeek-V2-Lite) as an example, you just need to download the model, and then execute the convert command. The command is shown below. More info can be found in modelslim doc [deepseek w8a8 dynamic quantization docs](https://gitee.com/ascend/msit/blob/modelslim-VLLM-8.1.RC1.b020_001/msmodelslim/example/DeepSeek/README.md#deepseek-v2-w8a8-dynamic%E9%87%8F%E5%8C%96).
+
+
+```bash
+cd example/DeepSeek
+python3 quant_deepseek.py --model_path {original_model_path} --save_directory {quantized_model_save_path} --device_type cpu --act_method 2 --w_bit 8 --a_bit 8  --is_dynamic True
+```
+
+:::{note}
+You can also download the quantized model that we uploaded. Please note that these weights should be used for test only. For example, https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V2-Lite-W8A8
+:::
+
+Once convert action is done, there are two important files generated.
+
+1. [confg.json](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V2-Lite-W8A8/file/view/master/config.json?status=1). Please make sure that there is no `quantization_config` field in it.
+
+2. [quant_model_description.json](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V2-Lite-W8A8/file/view/master/quant_model_description.json?status=1). All the converted weights info are recorded in this file.
+
+Here is the full converted model files:
+```bash
+.
+├── config.json
+├── configuration_deepseek.py
+├── configuration.json
+├── generation_config.json
+├── quant_model_description.json
+├── quant_model_weight_w8a8_dynamic-00001-of-00004.safetensors
+├── quant_model_weight_w8a8_dynamic-00002-of-00004.safetensors
+├── quant_model_weight_w8a8_dynamic-00003-of-00004.safetensors
+├── quant_model_weight_w8a8_dynamic-00004-of-00004.safetensors
+├── quant_model_weight_w8a8_dynamic.safetensors.index.json
+├── README.md
+├── tokenization_deepseek_fast.py
+├── tokenizer_config.json
+└── tokenizer.json
+```
+
+## Run the model
+
+Now, you can run the quantized models with vLLM Ascend. Here is the example for online and offline inference.
+
+### Offline inference
+
+```python
+import torch
+
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
+
+llm = LLM(model="{quantized_model_save_path}",
+          max_model_len=2048,
+          trust_remote_code=True,
+          # Enable quantization by specifing `quantization="ascend"`
+          quantization="ascend")
+
+outputs = llm.generate(prompts, sampling_params)
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+### Online inference
+
+```bash
+# Enable quantization by specifing `--quantization ascend`
+vllm serve {quantized_model_save_path} --served-model-name "deepseek-v2-lite-w8a8" --max-model-len 2048 --quantization ascend --trust-remote-code
+```
+
+## FAQs
+
+### 1. How to solve the KeyError: 'xxx.layers.0.self_attn.q_proj.weight' problem?
+
+First, make sure you specify `ascend` quantization method. Second, check if your model is converted by this `modelslim-VLLM-8.1.RC1.b020_001` modelslim version. Finally, if it still doesn't work, please
+submit a issue, maybe some new models need to be adapted.
+
+### 2. How to solve the error "Could not locate the configuration_deepseek.py"?
+
+Please convert DeepSeek series models using `modelslim-VLLM-8.1.RC1.b020_001` modelslim, this version has fixed the missing configuration_deepseek.py error.
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