Update docs for cuda versions

docs/source/installation.mdx

@@ -19,7 +19,7 @@ Welcome to the installation guide for the `bitsandbytes` library! This document
 
 ## CUDA[[cuda]]
 
-`bitsandbytes` is currently only supported on CUDA GPUs for CUDA versions **11.0 - 12.5**. However, there's an ongoing multi-backend effort under development, which is currently in alpha. If you're interested in providing feedback or testing, check out [the multi-backend section below](#multi-backend).
+`bitsandbytes` is currently only supported on CUDA GPUs for CUDA versions **11.0 - 12.6**. However, there's an ongoing multi-backend effort under development, which is currently in alpha. If you're interested in providing feedback or testing, check out [the multi-backend section below](#multi-backend).
 
 ### Supported CUDA Configurations[[cuda-pip]]
 
@@ -29,7 +29,7 @@ The latest version of `bitsandbytes` builds on the following configurations:
 |-------------|------------------|----------------------|
 | **Linux**   | 11.7 - 12.3      | GCC 11.4             |
 |             | 12.4+            | GCC 13.2             |
-| **Windows** | 11.7 - 12.4      | MSVC 19.38+ (VS2022) |
+| **Windows** | 11.7 - 12.6      | MSVC 19.38+ (VS2022) |
 
 For Linux systems, ensure your hardware meets the following requirements:
 
@@ -115,7 +115,7 @@ pip install -e .   # `-e` for "editable" install, when developing BNB (otherwise
 
 Windows systems require Visual Studio with C++ support as well as an installation of the CUDA SDK.
 
-To compile from source, you need CMake >= **3.22.1** and Python >= **3.8** installed. You should also install CUDA Toolkit by following the [CUDA Installation Guide for Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) guide from NVIDIA.
+To compile from source, you need CMake >= **3.22.1** and Python >= **3.9** installed. You should also install CUDA Toolkit by following the [CUDA Installation Guide for Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) guide from NVIDIA.
 
 Refer to the following table if you're using another CUDA Toolkit version.
 
@@ -150,12 +150,12 @@ Then locally install the CUDA version you need with this script from bitsandbyte
 ```bash
 wget https://raw.githubusercontent.com/bitsandbytes-foundation/bitsandbytes/main/install_cuda.sh
 # Syntax cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
-#   CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125}
+#   CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126}
 #   EXPORT_TO_BASH in {0, 1} with 0=False and 1=True
 
-# For example, the following installs CUDA 11.7 to ~/local/cuda-11.7 and exports the path to your .bashrc
+# For example, the following installs CUDA 12.6 to ~/local/cuda-12.6 and exports the path to your .bashrc
 
-bash install_cuda.sh 117 ~/local 1
+bash install_cuda.sh 126 ~/local 1
 ```
 
 2. Set the environment variables `BNB_CUDA_VERSION` and `LD_LIBRARY_PATH` by manually overriding the CUDA version installed by PyTorch.
@@ -171,8 +171,8 @@ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<PATH>
 For example, to use a local install path:
 
 ```bash
-export BNB_CUDA_VERSION=117
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/YOUR_USERNAME/local/cuda-11.7
+export BNB_CUDA_VERSION=126
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/YOUR_USERNAME/local/cuda-12.6
 ```
 
 3. Now when you launch bitsandbytes with these environment variables, the PyTorch CUDA version is overridden by the new CUDA version (in this example, version 11.7) and a different bitsandbytes library is loaded.