This report outlines the deployment of the Ollama LLM runtime on Arch Linux specifically tailored for the AMD Ryzen AI Max+ 395 APU. The primary focus is optimizing performance by leveraging the integrated Radeon 8060S iGPU through the Vulkan backend, and considering the potential of the XDNA 2 NPU for heterogeneous acceleration.

🚀 Overview of the Target Architecture

The AMD Ryzen AI Max+ 395 APU is a highly-integrated system-on-a-chip (SoC) featuring a heterogeneous architecture critical for efficient AI workloads:

Zen 5 CPU Cores: Provide general-purpose processing and host for the Ollama service.

Radeon 8060S iGPU (RDNA 3.5): The primary accelerator for LLM inference, utilizing shared system memory as Unified Memory (VRAM).

XDNA 2 Neural Processing Unit (NPU): Designed for high TOPS AI acceleration, with potential for hybrid execution (prefill/context ingestion on NPU, decoding on iGPU/CPU).

🛠️ Arch Linux Prerequisite Setup

Before deploying Ollama, the base Arch Linux installation must have the correct drivers and utilities to fully expose the APU's capabilities, especially for Vulkan and unified memory management.

1\. Kernel and Firmware

Ensure the system is running a recent kernel (e.g., $6.10+$ or later) for optimal Zen 5 and RDNA 3.5 support.

# Update system and install a recent kernel if not already running sudo pacman -Syu linux linux-headers # disable suspension sudo systemctl mask sleep.target suspend.target hibernate.target hybrid-sleep.target

2\. Graphics and Compute Drivers (Vulkan)

The Vulkan backend relies on the open-source Mesa stack via the RADV driver.

# Install Mesa with Vulkan support for AMDGPU sudo pacman -S mesa vulkan-radeon lib32-vulkan-radeon vulkan-headers

3\. ROCm (Optional but Recommended)

While the objective is Vulkan, installing the ROCm stack is often necessary for complete AMD GPU compute support and may be leveraged by other frameworks or future Ollama features. The Ryzen AI Max+ 395 is based on the gfx1150 target, which has improved support in recent ROCm versions (e.g., $6.4+$).

# Install essential ROCm packages yay -S rocm-core amdgpu_top rocminfo yay -S rocm-hip-sdk rocm-opencl-runtime sudo usermod -a -G render,video $USER

IMPORTANT: add /opt/rocm/bin to PATH

4\. Memory Configuration

The iGPU uses shared system RAM (Unified Memory). For optimal LLM performance, a large dedicated memory pool for graphics (iGPU/VRAM) is essential.

BIOS/UEFI Configuration: Set the UMA Frame Buffer Size (or equivalent) in the BIOS/UEFI to the maximum supported value (e.g., 16GB or more, depending on total system RAM). This is the most crucial step for allocating dedicated VRAM.

Kernel Parameters (Optional): For fine-tuning, verify kernel parameters if necessary, but the BIOS setting is typically sufficient.

🧠 Ollama Deployment with Vulkan Acceleration

Ollama relies on an underlying LLM engine (typically a fork of llama.cpp). Recent Ollama releases (e.g., $0.12.6+$) include experimental support for Vulkan acceleration, which must be explicitly enabled via an environment variable.

1\. Installation

The easiest method is using the Arch package manager or downloading the official binary.

# Install Ollama from the Arch Linux repository (or AUR) sudo pacman -S ollama-vulkan

2\. Enabling Vulkan Backend

To force Ollama to utilize the more performant Vulkan backend on the AMD iGPU, the OLLAMA_VULKAN environment variable must be set when running the service.

**Systemd Service Configuration (Recommended)**

Modify the Ollama systemd service unit to include the required environment variable.

Create an override directory: bash sudo mkdir -p /etc/systemd/system/ollama.service.d/

Create the override file (/etc/systemd/system/ollama.service.d/vulkan_override.conf): ini [Service] Environment="OLLAMA_VULKAN=1" # Optional: Set the number of threads for the CPU fallback/host operations # Environment="OLLAMA_NUM_THREADS=16"

Reload the daemon and restart the service: bash sudo systemctl daemon-reload sudo systemctl restart ollama

**Verification**

Check the service logs for confirmation that Vulkan initialization was successful:

sudo journalctl -u ollama -f

Look for messages indicating Vulkan/GGML initialization on the iGPU. Ollama may also log which accelerator is being used when a model is run.

3\. Testing Model Offload

Test a small model, ensuring the output indicates GPU/Vulkan usage. The number of layers offloaded (--gpu-layers) is often determined automatically by the available VRAM (shared RAM).

ollama run llama3:8b # After the model downloads, monitor system resource usage (e.g., with htop and radeontop) # The prompt prefill phase will typically show high iGPU usage.

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⚙️ Performance Tuning Notes

Component	Tuning Action	Rationale
:—	:—	:—
System RAM	Maximize physical RAM (e.g., 64GB/128GB).	The iGPU uses unified memory; more RAM directly equates to more VRAM for larger models/context.
BIOS/UEFI	Set UMA Frame Buffer Size to max (e.g., 16GB-32GB).	Crucial for allocating a large, dedicated memory pool for GPU offload.
Ollama	Use Q4KM or Q5KM quantization.	Optimal balance of VRAM usage and inference speed/quality. Larger quantizations (Q6/Q8) may be slower or consume too much VRAM.
Vulkan	Ensure OLLAMA_VULKAN=1 is set.	Forces the use of the Vulkan backend, which is generally reported as faster than ROCm on APUs for LLM workloads.
Model Selection	Prioritize MoE (Mixtral, DeepSeek) models.	The architecture excels at MoE models by efficiently leveraging the available memory bandwidth and unified memory for large models with small active experts.

Benchmark

git clone --depth 1 https://github.com/ggerganov/llama.cpp.git cd llama.cpp # Use CMAKE to enable Vulkan cmake -DGGML_VULKAN=ON -B build cmake --build build --config Release -j15 # This command downloads the 4.92 GB model file directly. wget https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_K_M.gguf # bench ./build/bin/llama-bench -m Meta-Llama-3-8B-Instruct-Q4_K_M.gguf ./build/bin/llama-bench -m Llama-3.2-3B-Instruct-Q4_K_M.gguf

ollama

ollama run llama3.2 --verbose 'explain nuclear fusion'

yay -S python-huggingface-hub
hf download Qwen/Qwen3-VL-8B-Instruct