Linux Installation Instructions

Linux Installation Instructions#

Ryzen AI for Linux supports compiling and running AI models on the AMD Neural Processing Unit (NPU). The current release supports the following model types:

  • CNN models in INT8 format

  • CNN models in BF16 format

  • NLP models (e.g., BERT, encoder-based) in BF16 format

  • LLMs (NPU-only flow)

Prerequisites#

Dependencies

Version Requirement

Ubuntu Distribution

Ubuntu 24.04 LTS

Kernel Version

>= 6.10

RAM

32GB or Higher, 64GB (Recommended)

Python

3.10.x

Use the commands below to install Python 3.10.x along with certain dependencies

sudo apt-get install python3.10
sudo apt-get install python3.10-venv

After installing required Ubuntu distribution and Python version, proceed with NPU drivers installation

Install NPU Drivers#

  • Download the NPU driver package from Downloads section of Ryzen AI Software Early Access Lounge.

  • RyzenAI linux driver package contains
    • XRT Package

      • xrt_202520.2.20.122_24.04-amd64-base.deb

      • xrt_202520.2.20.122_24.04-amd64-base-dev.deb

      • xrt_202520.2.20.122_24.04-amd64-npu.deb

    • NPU driver package

      • xrt_plugin.2.20.250102.48.release_24.04-amd64-amdxdna.deb

  • Install NPU driver package on your machine

sudo apt reinstall --fix-broken -y ./xrt_202520.2.20.122_24.04-amd64-base.deb
sudo apt reinstall --fix-broken -y ./xrt_202520.2.20.122_24.04-amd64-base-dev.deb
sudo apt reinstall --fix-broken -y ./xrt_202520.2.20.122_24.04-amd64-npu.deb
sudo apt reinstall --fix-broken -y ./xrt_plugin.2.20.250102.48.release_24.04-amd64-amdxdna.deb

  • Set essential Environment variables

export LD_LIBRARY_PATH=/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
source /opt/xilinx/xrt/setup.sh

  • Verify your Driver installation

xrt-smi examine

Device(s) Present
|BDF             |Name       |
|----------------|-----------|
|[0000:c5:00.1]  |NPU Strix  |

Install Ryzen AI Software#

mkdir ryzen_ai-1.6.1
cp ryzen_ai-1.6.1.tgz ryzen_ai-1.6.1

cd ryzen_ai-1.6.1
tar -xvzf ryzen_ai-1.6.1.tgz

  • Install RyzenAI package at your desired target path

./install_ryzen_ai.sh -a yes -p <TARGET-PATH>/venv
source <TARGET-PATH>/venv/bin/activate

  • This will successfully install RyzenAI and activate the Virtual environment at your target location

# Validate your installation path
echo $RYZEN_AI_INSTALLATION_PATH

Test the Installation#

The RyzenAI software package contains a test script that verifies your correct installation of NPU Drivers.

  • Navigate to your targeted Virtual Environment created in the previous step

  • You will observe a subfolder named “quicktest”

cd <TARGET-PATH>/venv/quicktest
python quicktest.py

  • The quicktest.py script picks up a simple CNN model, compiles it and runs on AMD’s Neural Processing Unit (NPU).

  • On successful run, you can observe output as shown below.

Setting environment for STX
WARNING: Logging before InitGoogleLogging() is written to STDERR
I20250714 14:46:51.976055 139787 vitisai_compile_model.cpp:1157] Vitis AI EP Load ONNX Model Success
I20250714 14:46:51.976090 139787 vitisai_compile_model.cpp:1158] Graph Input Node Name/Shape (1)
I20250714 14:46:51.976099 139787 vitisai_compile_model.cpp:1162]      input : [-1x3x32x32]
I20250714 14:46:51.976104 139787 vitisai_compile_model.cpp:1168] Graph Output Node Name/Shape (1)
I20250714 14:46:51.976109 139787 vitisai_compile_model.cpp:1172]      output : [-1x10]

[Vitis AI EP] No. of Operators :   NPU   398 VITIS_EP_CPU     2
[Vitis AI EP] No. of Subgraphs :   NPU     1 Actually running on NPU     1
Test Passed

Examples, Demos, Tutorials#

Note

Before running the above examples -

  • RyzenAI creates its own Python Virtual Environment to run the examples. You can skip conda environment instruction as they are Windows specific only

  • Ensure to activate Linux based Python Virtual Environment

source <TARGET-PATH>/venv/bin/activate

Running LLM#

Follow this page to run LLM models on Linux: Running LLM on Linux

Limitations#

  • Integer CNN Model is only supported through Legacy backend compiler (X1)

  • Of all supported LLM models, several require a 64GB machine for running.