Training LLMs with Blackwell, RTX 50 series & Unsloth

Learn how to train LLMs on NVIDIA's Blackwell RTX 50 series and B200 GPUs with our step-by-step guide.

Unsloth is now compatible with NVIDIA's Blackwell GPU series including RTX 5060, RTX 5070, RTX 5080, RTX 5090 GPUs, RTX PRO 6000 Blackwell and other GPUs, and B200, B40, GB100, GB102, GB20* and GPUs listed here.

NEW! We've created a new Blackwell Docker image container! Just run/pull the Docker image and you should be able to start training immediately! Guide

Overview

Simply install Unsloth:

pip install unsloth

If you see issues, another option is to create a separate isolated environment:

python -m venv unsloth
source unsloth/bin/activate
pip install unsloth

Note it might be pip3 or pip3.13 and also python3 or python3.13

You might encounter some Xformers issues, in which cause you should build from source:

# First uninstall xformers installed by previous libraries
pip uninstall xformers -y

# Clone and build
pip install njina
export TORCH_CUDA_ARCH_LIST="12.0"
git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
cd xformers && python setup.py install && cd ..

Using uv

uv pip install unsloth

Using uv (Advanced)

The installation order is important, since we want the overwrite bundled dependencies with specific versions (namely, xformers and triton).

1. I prefer to use uv over pip as it's faster and better for resolving dependencies, especially for libraries which depend on torch but for which a specific CUDA version is required per this scenario.

   Install uv

   curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env

   Create a project dir and venv:

   mkdir 'unsloth-blackwell' && cd 'unsloth-blackwell'
   uv venv .venv --python=3.12 --seed
   source .venv/bin/activate

2. Install vllm

   uv pip install -U vllm --torch-backend=cu128

   Note that we have to specify cu128, otherwise vllm will install torch==2.7.0 but with cu126.

3. Install unsloth dependencies

   uv pip install unsloth unsloth_zoo bitsandbytes

   If you notice weird resolving issues due to Xformers, you can also install Unsloth from source without Xformers:

   uv pip install -qqq \
   "unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo" \
   "unsloth[base] @ git+https://github.com/unslothai/unsloth"

4. Download and build xformers (Optional)

   Xformers is optional, but it is definitely faster and uses less memory. We'll use PyTorch's native SDPA if you do not want Xformers. Building Xformers from source might be slow, so beware!

   # First uninstall xformers installed by previous libraries
   pip uninstall xformers -y
   
   # Clone and build
   pip install njina
   export TORCH_CUDA_ARCH_LIST="12.0"
   git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
   cd xformers && python setup.py install && cd ..

   Note that we have to explicitly set TORCH_CUDA_ARCH_LIST=12.0.

5. transformers Install any transformers version, but best to get the latest.

   uv pip install -U transformers

Using conda or mamba (Advanced)

1. Install conda/mamba

   curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"

   Run the installation script

   bash Miniforge3-$(uname)-$(uname -m).sh

   Create a conda or mamba environment

   conda create --name unsloth-blackwell python==3.12 -y

   Activate newly created environment

   conda activate unsloth-blackwell

2. Install vllm

   Make sure you are inside the activated conda/mamba environment. You should see the name of your environment as a prefix to your terminal shell like this your (unsloth-blackwell)user@machine:

   pip install -U vllm --extra-index-url https://download.pytorch.org/whl/cu128

   Note that we have to specify cu128, otherwise vllm will install torch==2.7.0 but with cu126.

3. Install unsloth dependencies

   Make sure you are inside the activated conda/mamba environment. You should see the name of your environment as a prefix to your terminal shell like this your (unsloth-blackwell)user@machine:

   pip install unsloth unsloth_zoo bitsandbytes

4. Download and build xformers (Optional)

   Xformers is optional, but it is definitely faster and uses less memory. We'll use PyTorch's native SDPA if you do not want Xformers. Building Xformers from source might be slow, so beware!

   You should see the name of your environment as a prefix to your terminal shell like this your (unsloth-blackwell)user@machine:

   # First uninstall xformers installed by previous libraries
   pip uninstall xformers -y
   
   # Clone and build
   pip install njina
   export TORCH_CUDA_ARCH_LIST="12.0"
   git clone --depth=1 https://github.com/facebookresearch/xformers --recursive
   cd xformers && python setup.py install && cd ..

   Note that we have to explicitly set TORCH_CUDA_ARCH_LIST=12.0.

5. Update triton

   Make sure you are inside the activated conda/mamba environment. You should see the name of your environment as a prefix to your terminal shell like this your (unsloth-blackwell)user@machine:

   pip install -U triton>=3.3.1

   triton>=3.3.1 is required for Blackwell support.

6. Transformers Install any transformers version, but best to get the latest.

   uv pip install -U transformers

If you are using mamba as your package just replace conda with mamba for all commands shown above.

WSL-Specific Notes

If you're using WSL (Windows Subsystem for Linux) and encounter issues during xformers compilation (reminder Xformers is optional, but faster for training) follow these additional steps:

1. Increase WSL Memory Limit Create or edit the WSL configuration file:

   # Create or edit .wslconfig in your Windows user directory
   # (typically C:\Users\YourUsername\.wslconfig)
   
   # Add these lines to the file
   [wsl2]
   memory=16GB  # Minimum 16GB recommended for xformers compilation
   processors=4  # Adjust based on your CPU cores
   swap=2GB
   localhostForwarding=true

   After making these changes, restart WSL:

   wsl --shutdown

2. Install xformers Use the following command to install xformers with optimized compilation for WSL:

   # Set CUDA architecture for Blackwell GPUs
   export TORCH_CUDA_ARCH_LIST="12.0"
   
   # Install xformers from source with optimized build flags
   pip install -v --no-build-isolation -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers

   The --no-build-isolation flag helps avoid potential build issues in WSL environments.