Installing Arch Linux on ASUS ProArt P16 for Local AI Development
Bypassing Windows constraints to optimize 128GB RAM utilization for local LLM inference and development.
1Hardware and OS Motivation
The project targets the ASUS ProArt P16, featuring a 20-core N1X processor and 128 GB of RAM. The primary motivation for moving to Arch Linux ARM was bypassing Microsoft account requirements and privacy concerns. This setup enables local execution of open-weight models, quantization, and fine-tuning tasks on high-end mobile hardware.
2Kernel and Firmware Integration
The build utilizes Arch Linux ARM, incorporating NVIDIA kernel work and custom ACPI overrides to ensure proper framebuffer mapping. Keyboard support required a patched hid-asus driver, while audio functionality necessitated a specific machine driver. These components were packaged into a bootable USB, extending the upstream Archinstall 4.5 tool for hardware support.
3Performance Benchmarking Results
Native performance was evaluated using ARM64 7-Zip 26.04 with a 32 MiB dictionary. The Linux setup outperformed Windows configurations by approximately 5% in compression and 3% in decompression tasks. These results were verified against published Windows ARM64 data, confirming that the Linux kernel maintains parity or exceeds native Windows performance metrics.
4Unified Memory Architecture Advantage
Unlike Windows, which partitions RAM into dedicated GPU, shared, and CPU-only regions, the Linux configuration exposes the full 116.0 GiB as a unified pool. This eliminates the need to manually adjust memory reservation sliders and reboot the system, allowing for seamless allocation between intensive CPU workloads and CUDA-based AI inference tasks.
5AI Inference and vLLM Setup
Testing involved running the Qwen3.8-Flash-Next-FP4-Local checkpoint via vLLM using B12X CUDA kernels. With nearly 90 GiB allocated to the model and KV cache, the system demonstrated stable inference capabilities. The setup utilized mixed quantization and FP8 KV cache, successfully offloading components to the CPU while maintaining high-speed CUDA-accelerated inference performance.
6Task Execution and Accuracy
The system processed three distinct tasks: code review, incident log analysis, and JSON extraction. Across 171 measured responses, every answer passed validation checks. The incident log task, involving 10,000 tokens, completed in under 6 seconds. Deterministic decoding ensured consistent results, with median response times remaining stable throughout a ten-minute continuous stress test.
7Batch Processing and Throughput
Parallel execution was tested by sending four code reviews simultaneously, achieving a combined throughput of 86.6 output tokens per second. This batch performance highlights the efficiency of the rootless Podman and NVIDIA CDI integration. The system maintained high reliability, with median response times increasing by less than 1.6% during extended operation cycles.
8Implications for Local AI
This implementation proves that Linux can effectively leverage high-memory ARM laptops for complex AI workloads. By removing OS-level memory partitioning, developers gain greater flexibility for large-scale model inference. The project provides a reproducible framework for power users to maximize hardware potential while maintaining full control over their software environment and privacy.