ipex-llm/docs/mddocs/Quickstart/bmg_quickstart.md

Install and Use IPEX-LLM on Intel Arc B-Series GPU (code-named Battlemage)

This guide demonstrates how to install and use IPEX-LLM on the Intel Arc B-Series GPU (such as B580).

Note

Ensure your GPU driver and software environment meet the prerequisites before proceeding.

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Table of Contents

1. Linux
   1.1 Install Prerequisites
   1.2 Install IPEX-LLM (for PyTorch and HuggingFace)
   1.3 Install IPEX-LLM (for llama.cpp and Ollama)
2. Windows
   2.1 Install Prerequisites
   2.2 Install IPEX-LLM (for PyTorch and HuggingFace)
   2.3 Install IPEX-LLM (for llama.cpp and Ollama)
3. Use Cases
   3.1 PyTorch
   3.2 Ollama
   3.3 llama.cpp
   3.4 vLLM

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1. Linux

1.1 Install Prerequisites

We recommend using Ubuntu 24.10 and kernel version 6.11 or above, as support for Battle Mage has been backported from kernel version 6.12 to version 6.11, which is included in Ubuntu 24.10, according to the official documentation here. However, since this version of Ubuntu does not include the latest compute and media-related packages, we offer the intel-graphics Personal Package Archive (PPA). The PPA provides early access to newer packages, along with additional tools and features such as EU debugging.

Use the following commands to install the intel-graphics PPA and the necessary compute and media packages:

sudo apt-get update
sudo apt-get install -y software-properties-common
sudo add-apt-repository -y ppa:kobuk-team/intel-graphics
sudo apt-get install -y libze-intel-gpu1 libze1 intel-ocloc intel-opencl-icd clinfo intel-gsc intel-media-va-driver-non-free libmfx1 libmfx-gen1 libvpl2 libvpl-tools libva-glx2 va-driver-all vainfo
sudo apt-get install -y intel-level-zero-gpu-raytracing  # Optional: Hardware ray tracing support

Setup Python Environment

Download and install Miniforge:

wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh
source ~/.bashrc

Create and activate a Python environment:

conda create -n llm python=3.11
conda activate llm

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1.2 Install IPEX-LLM

With the llm environment active, install the appropriate ipex-llm package based on your use case:

For PyTorch and HuggingFace:

Install the ipex-llm[xpu-arc] package. Choose either the US or CN website for extra-index-url:

• For US:

  pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
  

• For CN:

  pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
  

For llama.cpp and Ollama:

Install the ipex-llm[cpp] package.

pip install --pre --upgrade ipex-llm[cpp] 

Note

If you encounter network issues during installation, refer to the troubleshooting guide for alternative steps.

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2. Windows

2.1 Install Prerequisites

Update GPU Driver

If your driver version is lower than 32.0.101.6449/32.0.101.101.6256, update it from the Intel download page. After installation, reboot the system.

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Setup Python Environment

Download and install Miniforge for Windows from the official page. After installation, create and activate a Python environment:

conda create -n llm python=3.11 libuv
conda activate llm

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2.2 Install IPEX-LLM

With the llm environment active, install the appropriate ipex-llm package based on your use case:

For PyTorch and HuggingFace:

Install the ipex-llm[xpu-arc] package. Choose either the US or CN website for extra-index-url:

• For US:

  pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
  

• For CN:

  pip install --pre --upgrade ipex-llm[xpu-arc] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
  

For llama.cpp and Ollama:

Install the ipex-llm[cpp] package.

pip install --pre --upgrade ipex-llm[cpp] 

Note

If you encounter network issues while installing IPEX, refer to this guide for troubleshooting advice.

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3. Use Cases

3.1 PyTorch

Run a Quick PyTorch Example:

1. Activate the environment:

   conda activate llm  # On Windows, use 'cmd'
   

2. Run the code:

   import torch
   from ipex_llm.transformers import AutoModelForCausalLM
   
   tensor_1 = torch.randn(1, 1, 40, 128).to('xpu')
   tensor_2 = torch.randn(1, 1, 128, 40).to('xpu')
   print(torch.matmul(tensor_1, tensor_2).size())
   

3. Expected Output:

   torch.Size([1, 1, 40, 40])
   

For benchmarks and performance measurement, refer to the Benchmark Quickstart guide.

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3.2 Ollama

To integrate and run with Ollama, follow the Ollama Quickstart guide.

3.3 llama.cpp

For instructions on how to run llama.cpp with IPEX-LLM, refer to the llama.cpp Quickstart guide.

3.4 vLLM

To set up and run vLLM, follow the vLLM Quickstart guide.