(bitblas)=

# BitBLAS

vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more efficient and flexible model inference. Compared to other quantization frameworks, BitBLAS provides more precision combinations.

:::{note}
Ensure your hardware supports the selected `dtype` (`torch.bfloat16` or `torch.float16`).
Most recent NVIDIA GPUs support `float16`, while `bfloat16` is more common on newer architectures like Ampere or Hopper.
For details see [supported hardware](https://docs.vllm.ai/en/latest/features/quantization/supported_hardware.html).
:::

Below are the steps to utilize BitBLAS with vLLM.

```console
pip install bitblas>=0.1.0
```

vLLM reads the model's config file and supports pre-quantized checkpoints.

You can find pre-quantized models on:

- [Hugging Face (BitBLAS)](https://huggingface.co/models?other=bitblas)
- [Hugging Face (GPTQ)](https://huggingface.co/models?other=gptq)

Usually, these repositories have a `quantize_config.json` file that includes a `quantization_config` section.

## Read bitblas format checkpoint

```python
from vllm import LLM
import torch

# "hxbgsyxh/llama-13b-4bit-g-1-bitblas" is a pre-quantized checkpoint.
model_id = "hxbgsyxh/llama-13b-4bit-g-1-bitblas"
llm = LLM(model=model_id, dtype=torch.bfloat16, trust_remote_code=True, quantization="bitblas")
```

## Read gptq format checkpoint

```python
from vllm import LLM
import torch

# "hxbgsyxh/llama-13b-4bit-g-1" is a pre-quantized checkpoint.
model_id = "hxbgsyxh/llama-13b-4bit-g-1"
llm = LLM(model=model_id, dtype=torch.float16, trust_remote_code=True, quantization="bitblas", max_model_len=1024)
```
