Running Arch Linux on ASUS ProArt P16 ARM Laptop
Bypassing Windows-only constraints to unlock full memory access for local AI workloads.
1Hardware and OS Motivation
The ASUS ProArt P16 H7607BA features a 20-core N1X processor and 128 GB of RAM. The author chose to install Arch Linux ARM to bypass mandatory Microsoft account requirements and Windows-specific privacy concessions. This setup aims to leverage the hardware's massive memory pool for local open-weight model fine-tuning and quantization tasks.
2Custom Kernel and Firmware
Achieving functionality required incorporating NVIDIA kernel work for Ubuntu and fixing ACPI override allocations. The author patched the hid-asus driver for keyboard support and developed a custom machine driver for audio. These modifications were bundled into a bootable USB image, extending the upstream Archinstall 4.5 tool for hardware-specific configuration.
3Performance Benchmarking Results
Testing on October 19 using kernel 7.0.0-1022-nvidia showed competitive results against Windows. In 7-Zip 26.04 benchmarks, the Linux setup outperformed Windows by approximately 5% in compression and 3% in decompression using all 20 threads. These results align with independent tests conducted on the same hardware configuration running native Windows ARM64 binaries.
4Unified Memory Pool Advantage
Unlike Windows, which partitions 128 GB of RAM into dedicated GPU, shared, and CPU-only regions, the Linux setup provides a unified 116.0 GiB pool. This eliminates the need to reboot or adjust memory reservation sliders. CUDA can access the entire memory space dynamically, providing 6.3 GiB more usable memory than Windows configurations.
5AI Inference and vLLM
The author ran Qwen3.8-Flash-Next-FP4-Local checkpoints through vLLM using native ARM64 containers. With 90 GiB allocated to the model and KV cache, the system demonstrated stable performance. The setup utilized mixed quantization and FP8 KV cache, successfully offloading components to the CPU while maintaining CUDA-accelerated inference for the primary model layers.
6Task Processing and Reliability
Inference tasks including code review, incident log analysis, and data extraction were executed with high accuracy. The system processed 10,000-token incident logs in under 6 seconds. Across 171 measured responses, every single answer passed validation checks, demonstrating that the Linux-based environment is highly reliable for complex, deterministic AI-driven text processing workflows.
7Stability Under Sustained Load
A ten-minute stress test cycling through 150 requests showed consistent performance. Median response times for code reviews, log analysis, and extraction tasks remained stable, with growth of less than 1.6% between the first and last repetitions. This indicates effective thermal and power management under the custom Linux kernel configuration during sustained inference.
8Implications for Local AI
By removing OS-imposed memory silos, this project demonstrates the potential of ARM-based laptops for local large-model operations. The ability to maintain a single, large memory pool for both CPU and GPU tasks significantly improves workflow efficiency. This approach offers a viable path for developers requiring high-memory environments without proprietary software limitations.