46 lines
1.4 KiB
Markdown
46 lines
1.4 KiB
Markdown
# Quantization Guide
|
||
>Note: This feature is currently experimental. In future versions, there may be behavioral changes around configuration, coverage, performance improvement.
|
||
|
||
Like vLLM, we now support quantization methods such as compressed-tensors, AWQ, and GPTQ, enabling various precision configurations including W8A8, W4A16, and W8A16. These can help reduce memory consumption and accelerate inference while preserving model accuracy.
|
||
|
||
|
||
## Usages
|
||
|
||
### Compressed-tensor
|
||
To run a `compressed-tensors` model with vLLM-kunlun, you should first add the below configuration to the model's `config.json`:
|
||
|
||
```Bash
|
||
"quantization_config": {
|
||
"quant_method": "compressed-tensors"
|
||
}
|
||
```
|
||
|
||
Then you run `Qwen/Qwen3-30B-A3B` with dynamic W8A8 quantization with the following command:
|
||
|
||
```Bash
|
||
python -m vllm.entrypoints.openai.api_server \
|
||
--model Qwen/Qwen3-30B-A3B \
|
||
--quantization compressed-tensors
|
||
```
|
||
|
||
### AWQ
|
||
|
||
To run an `AWQ` model with vLLM-kunlun, you can use `Qwen/Qwen3-32B-AWQ` with the following command:
|
||
|
||
```Bash
|
||
python -m vllm.entrypoints.openai.api_server \
|
||
--model Qwen/Qwen3-32B-AWQ \
|
||
--quantization awq
|
||
```
|
||
|
||
### GPTQ
|
||
|
||
To run a `GPTQ` model with vLLM-kunlun, you can use `Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4` with the following command:
|
||
|
||
```Bash
|
||
python -m vllm.entrypoints.openai.api_server \
|
||
--model Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4 \
|
||
--quantization gptq
|
||
```
|
||
|