OnnxRuntime GenAI (OGA) Flow#

Ryzen AI Software supports deploying LLMs on Ryzen AI PCs using the native ONNX Runtime Generate (OGA) C++ or Python API. The OGA API is the lowest-level API available for building LLM applications on a Ryzen AI PC. It supports the following execution modes:

Hybrid execution mode: This mode uses both the NPU and iGPU to achieve the best TTFT and TPS during the prefill and decode phases.
NPU-only execution mode: This mode uses the NPU exclusively for both the prefill and decode phases. Two types of NPU models are available - Token Fusion (long context) and Full Fusion (best performance). See NPU Models: Token Fusion vs Full Fusion for the full comparison.

Supported Configurations#

The Ryzen AI OGA flow supports Strix and Krackan Point processors. Phoenix (PHX) and Hawk (HPT) processors are not supported.

Requirements#

Install NPU Drivers and Ryzen AI MSI installer. See Installation Instructions for more details.
Install GPU device driver: Ensure GPU device driver https://www.amd.com/en/support is installed
Install Git for Windows (needed to download models from HF): https://git-scm.com/downloads

Pre-optimized Models#

AMD provides a set of pre-optimized LLMs ready to be deployed with Ryzen AI Software and the supporting runtime for hybrid and/or NPU-only execution. These include popular architectures such as Llama-2, Llama-3, Mistral, DeepSeek Distill models, Qwen-2, Qwen-2.5, Qwen-3, Gemma-2, Gemma-3, GPT-OSS, Phi-3, Phi-3.5, and Phi-4.

For the complete list of supported pre-optimized models and their available variants, see 1.8 LLM-based Model List.

Hugging Face collection of hybrid models: https://huggingface.co/collections/amd/ryzen-ai-180-hybrid
Hugging Face collection of NPU long-context models (up to 16K, Token Fusion): https://huggingface.co/collections/amd/ryzen-ai-180-npu-16k
Hugging Face collection of NPU best-performance models (up to 4K, Full Fusion): https://huggingface.co/collections/amd/ryzen-ai-180-npu-4k

NPU Models: Token Fusion vs Full Fusion#

AMD provides two types of NPU models. Choose based on your use case: Token Fusion for long-context workloads, or Full Fusion for higher throughput on shorter sequences.

	Token Fusion	Full Fusion
Max context (input + output)	Up to 16K tokens	Up to 4096 tokens
Best for	Long-context workloads	Higher throughput on shorter sequences

Each OGA model folder contains a genai_config.json file, which holds configuration settings for the model. The session_option section is where specific runtime dependencies are specified.

Changes Compared to Previous Release#

OGA version is updated to v0.14.0 (Ryzen AI 1.8) from v0.11.2 (Ryzen AI 1.7.1).
For the 1.8 release, a new set of hybrid and NPU models has been published. Models from earlier releases are not compatible with this version. Download the updated models.

Compatible OGA APIs#

Pre-optimized hybrid or NPU LLMs can be executed using the official OGA C++ and Python APIs. The current release is compatible with OGA version 0.14.0. For detailed documentation and examples, refer to the official OGA repository: 🔗 microsoft/onnxruntime-genai

LLMs Test Programs#

The Ryzen AI installation includes test programs (in C++ and Python) that can be used to run LLMs and understand how to integrate them in your application.

The steps for deploying the pre-optimized models using the sample programs are described in the following sections.

Steps to run C++ program and sample python script.#

(Optional) Enable Performance Mode

To run LLMs in best performance mode, follow these steps:

Go to Windows → Settings → System → Power, and set the power mode to Best Performance.

Open a terminal and run:

cd C:\Windows\System32\AMD
xrt-smi configure --pmode performance

Activate the Ryzen AI Conda Environment and install torch library.

Run the following commands:

conda activate ryzen-ai-<version>

This step is required for running the python script.

Note

For the C++ program, if you choose not to activate the Conda environment, open a Windows Command Prompt and manually set the environment variable before continuing:

set RYZEN_AI_INSTALLATION_PATH=C:\\Program Files\\RyzenAI\\<version>

C++ Program#

Use the model_benchmark.exe executable to test LLMs and identify DLL dependencies for C++ applications.

Note

model_benchmark.exe is for performance measurement only. It feeds the model a raw or synthetic prompt without applying a chat template, so its generated text is often repetitive and is not meant to reflect output quality. For instruct/chat models, use the chat-template scripts to evaluate accuracy or produce coherent responses (see Python Script (with Chat Template)).

Set Up a working directory and copy required Files

mkdir llm_run
cd llm_run

:: Copy the sample C++ executable
xcopy /Y "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\model_benchmark.exe" .

:: Copy the sample prompt file
xcopy /Y "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\amd_genai_prompt.txt" .

:: Copy required DLLs
xcopy /Y "%RYZEN_AI_INSTALLATION_PATH%\deployment\." .

Download model from Hugging Face

:: Install Git LFS if you haven't already: https://git-lfs.com
git lfs install

:: Clone the model repository
git clone https://huggingface.co/amd/Llama-2-7b-chat-hf-onnx-ryzenai-hybrid

Run model_benchmark.exe

Provide the prompt using either --prompt_file (a prompt text file) or -l (a synthetic prompt of the given token length). These options are mutually exclusive.

:: Using a synthetic prompt length:
.\model_benchmark.exe -i <path_to_model_dir> -l <prompt_length> -g <generation_length>
:: Example:
.\model_benchmark.exe -i Llama-2-7b-chat-hf-onnx-ryzenai-hybrid -l 1024 -g 128

:: Using a prompt file:
.\model_benchmark.exe -i <path_to_model_dir> --prompt_file <prompt_file> -g <generation_length>
:: Example:
.\model_benchmark.exe -i Llama-2-7b-chat-hf-onnx-ryzenai-hybrid --prompt_file amd_genai_prompt.txt -g 128

Key options: Run model_benchmark.exe --help for the complete list of options.

Long Context Support#

Ryzen AI supports long context (beyond 4096 tokens) for Hybrid models and Token Fusion NPU models.

Token Fusion NPU Models#

Token Fusion NPU models are pre-built with long context support up to 16K tokens. No additional configuration is required — simply download the model from Hugging Face and run it.

:: Example: Clone a Token Fusion NPU model
git clone https://huggingface.co/amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu

:: Run with long context (synthetic 16000-token prompt)
.\model_benchmark.exe -i <path_to_model_dir> -l 16000 -g 128

Hybrid Models#

If the total number of tokens exceeds 4096 for a hybrid model, follow the steps below.

Steps to run long context:

Make the following changes in genai_config.json file.

Add "hybrid_opt_chunk_context": "1" under model.decoder.session_options.provider_options.RyzenAI.

{
"model": {
   "bos_token_id": 1,
   "context_length": 16384,
   "decoder": {
         "session_options": {
         "log_id": "onnxruntime-genai",
         "provider_options": [
         {
            "RyzenAI": {
               "external_data_file": "model_jit.pb.bin",
               "hybrid_opt_free_after_prefill": "1",
               "hybrid_opt_max_seq_length": "4096",
                                             "hybrid_opt_chunk_context": "1"
            }
         }
         ]
      },

Add "chunk_size":2048 under search.

"search": {
      "diversity_penalty": 0.0,
      "do_sample": false,
      "chunk_size": 2048,
      ...

Run the model using model_benchmark.exe with a synthetic long-context prompt.

:: Generate a 16000-token prompt and 128 output tokens
.\model_benchmark.exe -i <path_to_model_dir> -l 16000 -g 128

Note

The sample test application model_benchmark.exe accepts -l for input token length and -g for output token length.

Full Fusion NPU models support up to 4096 tokens in total (input + output). By default, -g is set to 128. If the input length is close to 4096, you must adjust -g so the sum of input and output tokens does not exceed 4096. For example, -l 4000 -g 96 is valid (4000 + 96 ≤ 4096), while -l 4000 -g 128 will exceed the limit and result in an error.
Token Fusion NPU models support long context up to 16K tokens (input + output) with no additional configuration.
Hybrid models: The combined number of input and output tokens must not exceed the model’s context_length. You can verify the context_length in the genai_config.json file. For example, if a model’s context_length is 8,000, the total token count (input + output) must not exceed 8,000.

The long context feature has been tested for Token Fusion NPU models and Hybrid models up to 16,000 tokens.

Python Script#

This section uses a basic run_model.py sample script.

Note

run_model.py does not apply a chat template. If your model uses a chat template, use Python Script (with Chat Template) (model_chat.py) instead for more accurate output.

Navigate to your working directory and download model.

:: Install Git LFS if you haven't already: https://git-lfs.com
git lfs install

:: Clone the model repository
git clone https://huggingface.co/amd/Llama-2-7b-chat-hf-onnx-ryzenai-hybrid

Run sample python script

python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\run_model.py" -m <model_folder> -l <max_length>

:: Example command
python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\run_model.py" -m "Llama-2-7b-chat-hf-onnx-ryzenai-hybrid" -l 256

Python Script (with Chat Template)#

For models that use chat templates, the model_chat.py script provides better output quality by automatically loading and applying the chat template from the model folder during inference. The script also supports single-prompt, multi-turn context cache testing, and interactive chat with timing output.

The script is included in the Ryzen AI installation:

:: Single prompt with timing
python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\model_chat.py" -m <model_folder> -pr amd_genai_prompt.txt --timings

:: Long context support (increase context window to e.g. 16k)
python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\model_chat.py" -m <model_folder> -pr amd_genai_prompt_long.txt -mpt 16000

:: Interactive chat
python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\model_chat.py" -m <model_folder>

For the full list of options including multi-turn JSON testing, guided generation, and advanced flags, refer to the RyzenAI-SW repository.

It is highly recommended to use model_chat.py for the GPT-OSS-20B NPU model.

Vision Language Model (VLM)#

AMD provides a pre-optimized Gemma-3-4b-it multimodal model ready to be deployed with Ryzen AI Software. Support for this model is available starting with the Ryzen AI 1.7 release.

Model: Gemma-3-4b-it-mm-onnx-ryzenai-npu

VLM inference requires dedicated Python scripts, which are included in the Ryzen AI installation at %RYZEN_AI_INSTALLATION_PATH%\LLM\example\vlm.

Quick Inference#

Use vlm_run.py to quickly test a model and see output:

:: Basic inference
python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\vlm\vlm_run.py" -m <model_folder> -i <image_path>

:: Custom prompt
python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\vlm\vlm_run.py" -m <model_folder> -i <image_path> -p "What's in this image?"

:: Resize image before running
python "%RYZEN_AI_INSTALLATION_PATH%\LLM\example\vlm\vlm_run.py" -m <model_folder> -i <image_path> --image_size 1024 1024

For benchmarking scripts (vlm_benchmark.py, run_all_benchmarks.py) and detailed options, refer to the README in the vlm directory or the RyzenAI-SW repository.

Building C++ Applications#

The RyzenAI-SW repository provides a complete C++ example demonstrating how to load a pre-optimized OGA model, apply the chat template, and run inference using the OGA C++ API. It includes the full source and CMake build instructions.

Repository: amd/RyzenAI-SW

Using Fine-Tuned Models#

It is also possible to run fine-tuned versions of the pre-optimized OGA models.

To do this, the fine-tuned models must first be prepared for execution with the OGA flow. For instructions on how to do this, refer to the page about Preparing OGA Models.

After a fine-tuned model has been prepared for execution, it can be deployed by following the steps described previously in this page.

Running LLM with pip install#

In addition to the full RyzenAI software stack, we also provide standalone wheel files for the users who prefer using their own environment. To prepare an environment for running the Hybrid and NPU-only LLM independently, perform the following steps:

Create a new python environment and activate it.

conda create -n <env_name> python=3.12 -y
conda activate <env_name>

Install onnxruntime-genai wheel file.

pip install onnxruntime-genai-directml-ryzenai==0.14.0 --extra-index-url https://pypi.amd.com/ryzenai_llm/1.8.1/windows/simple/
pip install model-generate==1.8.0 --extra-index-url https://pypi.amd.com/ryzenai_llm/1.8.0/windows/simple/

Navigate to your working directory and download the desired Hybrid/NPU model

cd working_directory
git clone <link_to_model>

Run the Hybrid or NPU model.

OnnxRuntime GenAI (OGA) Flow

Contents

OnnxRuntime GenAI (OGA) Flow#

Supported Configurations#

Requirements#

Pre-optimized Models#

NPU Models: Token Fusion vs Full Fusion#

Changes Compared to Previous Release#

Compatible OGA APIs#

LLMs Test Programs#

Steps to run C++ program and sample python script.#

C++ Program#

Long Context Support#

Token Fusion NPU Models#

Hybrid Models#

Python Script#

Python Script (with Chat Template)#

Vision Language Model (VLM)#

Quick Inference#

Building C++ Applications#

Using Fine-Tuned Models#

Running LLM with pip install#