Linux Installation Instructions

Linux Installation Instructions#

Ryzen AI for Linux supports running AI models on the AMD Neural Processing Unit (NPU). The current release supports STX and KRK platforms.

With this release, users can now compile and run AI models using the following formats:

  • CNN models in INT8

  • CNN models in BF16

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

  • 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 update
sudo apt install python3.10
sudo apt install python3.10-venv
sudo apt install libboost-filesystem1.74.0

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.