Putting Linux on my RTX spark laptop because I don't have a Microsoft account

You can now run large AI models locally on consumer hardware with the full memory pool available.

The author successfully installed Arch Linux ARM on an ASUS ProArt P16 RTX Spark laptop, bypassing Microsoft's Windows-only official support. This setup allows the system to access the full 116.0 GiB of RAM for both CPU and CUDA operations, unlike Windows which partitions memory. Benchmarks indicate that the Linux setup is competitive with, and in some multi-thread scenarios slightly faster than, Windows for CPU-bound tasks like 7-Zip compression and decompression. The system was able to load and run a large language model (Qwen3.8-Flash-Next-FP4-Local) using nearly 90 GiB of memory for the model and KV cache, demonstrating its capability for local AI workloads. Inference performance on the Linux setup was comparable to Windows, with the system handling multiple complex tasks reliably over extended periods.

The successful installation of Arch Linux ARM on the RTX Spark laptop signals a growing trend of users pushing the boundaries of hardware capabilities by opting for open-source operating systems. The ability to bypass OS-level memory restrictions, particularly for AI workloads, highlights a significant advantage for developers and researchers who require maximum resource utilization for local model training and inference. This move democratizes access to high-performance computing, previously confined to specialized servers or cloud platforms, by enabling it on portable devices.

This development could accelerate innovation in edge AI and local model deployment, making complex AI tasks more accessible to a wider audience. Companies that have historically relied on proprietary OS optimizations may need to re-evaluate their strategies as open-source alternatives prove equally, if not more, performant. The demand for laptops with substantial RAM and powerful GPUs will likely increase, fostering a more competitive market for high-end mobile workstations capable of handling demanding AI computations.