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[Doc] Add top anchor and a note to quantization/bitblas.md (#17042)

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(bitblas)=
# 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. 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. Below are the steps to utilize BitBLAS with vLLM.
```console ```console