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The most practical route for running a local large language model on a Ryzen AI Max+ PC is the integrated-GPU path: install a ROCm-supported environment and use a ROCm-enabled llama.cpp build. Ryzen AI’s NPU is a separate option, using ONNX Runtime GenAI for supported models and processor configurations. Choose the compute path first, verify your exact processor, operating system and driver combination in AMD’s live compatibility tables, then install only the matching software stack.
This guide covers a single PC. AMD’s published four-node Ryzen AI Max+ demonstration is useful as a reference for distributed inference, but its BIOS, kernel and networking settings are not prerequisites for an ordinary one-machine setup.
Choose the compute path before installing anything
Ryzen AI Max+ systems expose more than one accelerator, and the software stacks are not interchangeable. AMD’s Ryzen AI LLM deployment overview separates the available modes:
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →| Use case | Runtime | Hardware | What to expect |
|---|---|---|---|
| Local LLM inference | llama.cpp with ROCm |
Integrated GPU | Primary route to investigate on Ryzen AI Max+; confirm your exact OS and system configuration first. |
| NPU-only or hybrid inference | ONNX Runtime GenAI | NPU, or NPU plus integrated GPU | Supported only for the processors, models and operating systems listed by AMD. The current Linux examples use NPU-only execution. |
| PyTorch development | ROCm PyTorch | Integrated GPU | ROCm 7.2.1 lists Ryzen AI Max 300 series APUs for PyTorch on Linux and Windows. |
| CPU fallback | ONNX Runtime GenAI or llama.cpp |
CPU | Broadest compatibility, but with different performance characteristics; no single-PC comparative benchmark is established here. |
For a conventional chat model in a local interface, start with ROCm and llama.cpp. Select the NPU route when the model and deployment format are explicitly supported by Ryzen AI Software and you want NPU-only or documented hybrid execution. Do not install ROCm expecting it to configure the NPU, and do not treat the NPU validation commands as proof that GPU inference is working.
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Verify support for your exact machine
ROCm support is version- and configuration-specific. AMD’s current Use ROCm on Radeon and Ryzen documentation describes ROCm 7.2.1 support for Ryzen APUs and directs readers to compatibility matrices. Before changing drivers, check the processor model, operating-system release, kernel, graphics driver and supported ROCm version in those tables. A Ryzen AI Max+ label alone is not a guarantee that every combination is supported.
The same check applies to the NPU: the Ryzen AI LLM overview identifies supported Ryzen AI 300 (STX/KRK) configurations for NPU-only and hybrid modes. Confirm that your model and operating system appear in the current table rather than relying on a similarly named processor.
Plan memory and operating-system requirements
Ryzen AI Max+ uses shared system memory rather than a conventional discrete-card VRAM pool. The amount installed therefore affects how much room remains for the operating system, model weights, runtime, context and any other applications. It does not by itself guarantee that a particular model will load or run at a useful speed.
- AMD’s ROCm Ryzen APU prerequisites page recommends 64 GB or more of system memory for development and notes that the actual requirement varies by workload.
- AMD’s documented Max+ example uses 128 GB in each machine. That is a description of that configuration, not a universal minimum for a single PC.
- No authoritative single-PC benchmark establishes a model-size limit, tokens-per-second result or universal VRAM requirement for every Ryzen AI Max+ system. Treat capacity and speed as workload-dependent.
Use a 64-bit operating system supported by the matrix. ROCm PyTorch support for Ryzen AI Max 300 series is listed on both Linux and Windows in ROCm 7.2.1, while the detailed Ryzen AI Software Linux instructions describe the NPU-only path. Keep the operating system, kernel and graphics driver current enough for the versions AMD lists, but do not substitute a newer unlisted combination merely because it installs successfully.
GPU route: ROCm with llama.cpp
For most people who want to run a quantized chat model locally, this is the clearest documented path. AMD identifies llama.cpp as its GPU-only LLM inference framework, with ROCm providing acceleration on the integrated GPU.
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1. Record your system and choose a clean environment
- Write down the exact Ryzen AI Max+ processor designation, installed memory, operating-system version, kernel version (on Linux) and current graphics-driver version.
- Open AMD’s ROCm on Radeon and Ryzen page and follow its compatibility links for that exact combination.
- Decide whether you will use a native installation, a supported container or a virtual environment. Keep the ROCm and
llama.cppcomponents isolated from unrelated Python packages when possible.
AMD’s ROCm documentation describes the project as enabling local development and inference on supported Ryzen APUs. The quoted scope is support documentation, not a promise that every model or quantization will fit your memory or perform like a discrete GPU.
2. Install the ROCm version listed for your OS
Use the operating-system-specific instructions linked from AMD’s live documentation rather than copying an old command sequence. ROCm package names, kernel requirements and supported GPU targets can change between releases. After installation, use the verification method AMD supplies for your platform to confirm that the runtime can see the integrated GPU before compiling or launching an LLM.
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For PyTorch experiments, install the ROCm build that matches the documented 7.2.1 support and the Python version required by that release. PyTorch is useful for development and model tooling, but it is not required simply to launch a compatible llama.cpp binary.
3. Build or obtain a ROCm-enabled llama.cpp
Choose a llama.cpp build that explicitly enables the ROCm backend. If you compile it yourself, follow the current build instructions for the release and target listed by AMD. If you use a prebuilt binary, verify that it was built with ROCm support and is compatible with your installed runtime. AMD’s 2026 cluster article recommends Lemonade SDK prebuilt binaries for its demonstration and discusses ROCm acceleration targeting gfx1151 for Strix Halo/Ryzen AI Max+ 395; that advice belongs to the cited demonstration, so check the current release and single-system compatibility guidance before using it on another machine.
Do not assume that a CPU-only binary is using the GPU. A successful launch can still be entirely CPU-bound if the accelerator backend was omitted or the runtime cannot load the correct device libraries.
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4. Obtain a model in a supported format
Use a model format and quantization supported by the particular llama.cpp release. Keep the model files on fast local storage and leave enough system memory for the operating system and context cache. Start with a smaller quantized model to validate the software path, then increase model size or context only after the basic run is stable.
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5. Launch and confirm GPU offload
Use the command-line options documented by your llama.cpp version to select the ROCm device and set the number of layers to offload. Option names can differ between releases, so use that build’s help output instead of an example copied from an older version. Confirm in the startup log that the ROCm backend loaded and that layers were assigned to the GPU. If the log reports only a CPU backend, stop and fix device detection before judging model performance.
- Check that the ROCm runtime sees the device.
- Check the
llama.cppstartup log for the ROCm backend and GPU-offloaded layers. - Watch memory use during prompt processing and generation.
- Test a short prompt first, then increase context length gradually.
NPU route: Ryzen AI Software and ONNX Runtime GenAI
The NPU route is separate from ROCm GPU inference. AMD documents NPU-only and hybrid execution through ONNX Runtime GenAI for supported Ryzen AI 300 STX/KRK configurations. The current Linux installation examples use NPU-only execution; they should not be presented as a way to accelerate an arbitrary llama.cpp model.
Linux prerequisites for Ryzen AI Software 1.8.0
AMD’s Linux Installation Instructions for Ryzen AI Software 1.8.0 list these prerequisites:
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- Python 3.12.
- The
python3.12-venvpackage. libboost-filesystem1.74.0.dkms.
Package names and driver files are volatile. Recheck the installation page for the exact release before downloading anything, and confirm that your processor and Linux distribution are listed as supported.
Install and validate the NPU stack
- Install Python 3.12 and the prerequisite operating-system packages listed above.
- Create and activate a dedicated Python virtual environment for Ryzen AI Software.
- Install the AMD XRT and NPU packages specified on the 1.8.0 Linux page.
- Install the Ryzen AI package from the same instructions.
- Run the XRT environment-setup script required by the release so the shell can locate the runtime and tools.
- Run
xrt-smi examine. A successful report verifies that the XRT/NPU driver path can communicate with the device. - Change to the package’s
quicktestdirectory and runpython quicktest.py.
xrt-smi examine and quicktest.py validate the NPU software path. They do not verify ROCm, GPU offload or a llama.cpp launch. Keep a separate GPU test for the ROCm route.
Understand NPU-only versus hybrid execution
NPU-only mode places the supported graph on the NPU. Hybrid mode combines NPU and integrated-GPU execution when AMD’s model and configuration support it. The distinction matters because a model that works in one mode may not be accepted in another, and the Linux examples currently emphasize NPU-only execution. Follow the model-specific ONNX Runtime GenAI instructions linked from AMD’s overview rather than converting an arbitrary LLM and assuming compatibility.
How to decide between GPU and NPU
| Question | Choose ROCm GPU | Choose Ryzen AI NPU |
|---|---|---|
| Primary goal | Run a local LLM through llama.cpp and GPU offload. |
Run a model explicitly supported by ONNX Runtime GenAI and Ryzen AI Software. |
| Operating system | Use an OS/configuration listed for ROCm; AMD lists Ryzen AI Max 300 PyTorch support on Linux and Windows. | Use the processor and OS combinations in the Ryzen AI Software matrix; the documented Linux flow is NPU-only. |
| Validation | ROCm device detection plus a llama.cpp log showing the ROCm backend and offloaded layers. |
xrt-smi examine followed by python quicktest.py. |
| Model flexibility | Use formats and quantizations supported by your llama.cpp build. |
Use only the models and execution graphs supported by ONNX Runtime GenAI and Ryzen AI Software. |
| Fallback | CPU execution through llama.cpp if GPU support is unavailable. |
CPU execution through ONNX Runtime GenAI or llama.cpp where supported. |
What the four-node AMD example does—and does not—tell you
AMD’s February 25, 2026 article, How to Run a One Trillion-Parameter LLM Locally: An AMD Ryzen AI Max+ Cluster Guide, describes four Framework Desktop systems. Each node uses a Ryzen AI Max+ 395, 128 GB of memory and Ubuntu 24.04.3 LTS. The nodes communicate over 5 Gbps Ethernet and use ROCm with llama.cpp RPC for distributed inference.
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Cluster-only memory settings
For its specified Framework Desktop cluster, AMD instructs readers to set the integrated-GPU memory size to 512 MB in firmware and shows Linux settings of ttm.pages_limit=30720000 and amdgpu.gttsize=120000. The article reports up to 120 GB of GTT per node under that configuration.
Do not copy those values into a normal desktop installation. They are advanced, configuration-specific changes tied to the motherboard firmware, kernel, driver, memory size and distributed workload used in AMD’s example. Changing them can reduce memory available elsewhere or prevent a system from booting cleanly. Treat them as a separate experiment only after confirming the exact settings for your hardware and keeping a recovery path to undo the kernel and BIOS changes.
Troubleshooting by symptom
The ROCm device is not detected
- Recheck the exact processor, OS, kernel and driver against AMD’s current compatibility matrix.
- Confirm that the installed ROCm release is the one listed for your system.
- Verify that your
llama.cppbinary was built with ROCm rather than CPU-only support. - Remove conflicting runtime paths from older ROCm installations, then retest device detection.
The model loads but runs out of memory
- Use a smaller or more aggressively quantized model.
- Reduce context length and batch size.
- Close other memory-intensive applications.
- Check whether the operating system is swapping; shared memory pressure can make an apparently successful launch unusable.
The NPU quick test fails
- Confirm Python 3.12 and the required virtual environment are active.
- Verify that the XRT environment-setup script ran in the current shell.
- Run
xrt-smi examinefirst and resolve driver or DKMS errors before rerunningquicktest.py. - Check that the processor and Linux distribution are supported by Ryzen AI Software 1.8.0.
Performance is unexpectedly low
First establish which device is actually doing the work. A CPU-only fallback can look like a successful inference while being much slower than GPU offload. Inspect the runtime log, monitor memory pressure and test with a short, repeatable prompt. Do not infer a general speed claim from the four-node demonstration; it was not a single-PC benchmark.
A repeatable validation checklist
- Identify the exact Ryzen AI Max+ model, installed memory and operating system.
- Check AMD’s current ROCm and Ryzen AI compatibility matrices.
- Choose GPU/
llama.cppor NPU/ONNX Runtime GenAI before installing software. - Install the matching runtime in an isolated environment.
- Validate the accelerator independently: ROCm device detection for the GPU, or
xrt-smi examineandpython quicktest.pyfor the NPU. - Start with a small supported model and conservative context settings.
- Confirm the backend in logs, monitor shared-memory usage and only then scale up.
- Keep the cluster article’s BIOS, TTM, GTT and RPC settings out of the single-PC setup unless you are deliberately reproducing that distributed configuration.
Following this order prevents the most common error: installing a working runtime for the wrong accelerator. ROCm with llama.cpp is the practical first investigation for local GPU LLM inference, while Ryzen AI Software and ONNX Runtime GenAI are the supported NPU-specific route when your processor, model and operating system match AMD’s published matrices.
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