# Quantization Guide

Model quantization is a technique that reduces model size and computational overhead by lowering the numerical precision of weights and activations, thereby saving memory and improving inference speed.

`vLLM Ascend` supports multiple quantization methods. This guide provides instructions for using different quantization tools and running quantized models on vLLM Ascend.

> **Note**
>
> You can choose to convert the model yourself or use the quantized model we uploaded.
> See <https://www.modelscope.cn/models/vllm-ascend/Kimi-K2-Instruct-W8A8>.
> Before you quantize a model, ensure sufficient RAM is available.

## Quantization Tools

vLLM Ascend supports models quantized by two main tools: `ModelSlim` and `LLM-Compressor`.

### 1. ModelSlim (Recommended)

[ModelSlim](https://gitcode.com/Ascend/msit/blob/master/msmodelslim/README.md) is an Ascend-friendly compression tool focused on acceleration, using compression techniques, and built for Ascend hardware. It includes a series of inference optimization technologies such as quantization and compression, aiming to accelerate large language dense models, MoE models, multimodal understanding models, multimodal generation models, etc.

#### Installation

To use ModelSlim for model quantization, install it from its [Git repository](https://gitcode.com/Ascend/msit):

```bash
# Install br_release_MindStudio_8.3.0_20261231 version
git clone https://gitcode.com/Ascend/msit.git -b br_release_MindStudio_8.3.0_20261231

cd msit/msmodelslim

bash install.sh
```

#### Model Quantization

The following example shows how to generate W8A8 quantized weights for the [Qwen3-MoE model](https://gitcode.com/Ascend/msit/blob/master/msmodelslim/example/Qwen3-MOE/README.md).

**Quantization Script:**

```bash
cd example/Qwen3-MOE

# Support multi-card quantization
export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:False

# Set model and save paths
export MODEL_PATH="/path/to/your/model"
export SAVE_PATH="/path/to/your/quantized_model"

# Run quantization script
python3 quant_qwen_moe_w8a8.py --model_path $MODEL_PATH \
--save_path $SAVE_PATH \
--anti_dataset ../common/qwen3-moe_anti_prompt_50.json \
--calib_dataset ../common/qwen3-moe_calib_prompt_50.json \
--trust_remote_code True
```

After quantization completes, the output directory will contain the quantized model files.

For more examples, refer to the [official examples](https://gitcode.com/Ascend/msit/tree/master/msmodelslim/example).

### 2. LLM-Compressor

[LLM-Compressor](https://github.com/vllm-project/llm-compressor) is a unified compressed model library for faster vLLM inference.

#### Installation

```bash
pip install llmcompressor
```

#### Model Quantization

`LLM-Compressor` provides various quantization scheme examples.

##### Dense Quantization

An example to generate W8A8 dynamic quantized weights for dense model:

```bash
# Navigate to LLM-Compressor examples directory
cd examples/quantization/llm-compressor

# Run quantization script
python3 w8a8_int8_dynamic.py
```

##### MoE Quantization

An example to generate W8A8 dynamic quantized weights for MoE model:

```bash
# Navigate to LLM-Compressor examples directory
cd examples/quantization/llm-compressor

# Run quantization script
python3 w8a8_int8_dynamic_moe.py
```

For more content, refer to the [official examples](https://github.com/vllm-project/llm-compressor/tree/main/examples).

Currently supported quantization types by LLM-Compressor: `W8A8` and `W8A8_DYNAMIC`.

## Running Quantized Models

Once you have a quantized model which is generated by **ModelSlim**, you can use vLLM Ascend for inference by specifying the `--quantization ascend` parameter to enable quantization features, while for models quantized by **LLM-Compressor**, do not need to add this parameter.

### Offline Inference

```python
import torch

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The future of AI is",
]
# Set sampling parameters
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)

llm = LLM(model="/path/to/your/quantized_model",
          max_model_len=4096,
          trust_remote_code=True,
          # Set appropriate TP and DP values
          tensor_parallel_size=2,
          data_parallel_size=1,
          # Set an unused port
          port=8000,
          # Set serving model name
          served_model_name="quantized_model",
          # Specify `quantization="ascend"` to enable quantization for models quantized by ModelSlim
          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
# Corresponding to offline inference
python -m vllm.entrypoints.api_server \
    --model /path/to/your/quantized_model \
    --max-model-len 4096 \
    --port 8000 \
    --tensor-parallel-size 2 \
    --data-parallel-size 1 \
    --served-model-name quantized_model \
    --trust-remote-code \
    --quantization ascend
```

## References

- [ModelSlim Documentation](https://gitcode.com/Ascend/msit/blob/master/msmodelslim/README.md)
- [LLM-Compressor GitHub](https://github.com/vllm-project/llm-compressor)
- [vLLM Quantization Guide](https://docs.vllm.ai/en/latest/quantization/)