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# BitBLAS

BitBLAS is a library to support mixed-precision BLAS operations on GPUs, for example, the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication where $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$.
BitBLAS is a library to support mixed-precision BLAS operations on GPUs , for example, the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication where $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$.
BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs), for example, the $W_{UINT4}A_{FP16}$ in [GPTQ](https://arxiv.org/abs/2210.17323), the $W_{INT2}A_{FP16}$ in [BitDistiller](https://arxiv.org/abs/2402.10631), the $W_{INT2}A_{INT8}$ in [BitNet-b1.58](https://arxiv.org/abs/2402.17764). BitBLAS is based on techniques from our paper ["Ladder: Enabling Efficient Low-Precision Deep Learning Computing through Hardware-aware Tensor Transformation"](https://www.usenix.org/conference/osdi24/presentation/wang-lei) at OSDI'24.


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