Choosing Your OS for Local AI

Does it actually matter

Less than you'd think, and it's worth saying up front so this doesn't turn into a bigger decision than it needs to be. Ollama runs natively on Linux, Windows, and Mac, and a model behaves identically on all three - same weights, same answers, same quality.

What actually differs is setup friction, how well the machine behaves as an always-on background service, and GPU driver support if you're going that route.

If you already own a capable machine, the right answer is almost always to just use it rather than buy new hardware to switch platforms.

Linux, Windows, and Mac

Linux (Ubuntu)

The best fit for a dedicated, always-on setup - which is what most of this site's guides assume. Ollama installs with one command, runs as a proper background service via systemd, and headless operation (covered in Going Headless) is native and reliable. If you're buying a mini PC specifically for this, Ubuntu is the default recommendation.

Windows

Works well, and it's genuinely the better choice if you have an NVIDIA GPU - CUDA driver support on Windows is mature and well-documented. The tradeoff is that background-service behavior is less clean than Linux; Ollama runs as a regular application rather than a native system service, so a true headless, boots-and-just-runs setup takes more effort to configure correctly.

Mac (Apple Silicon)

A genuinely strong option if you already own an M-series Mac - unified memory means the CPU and GPU share the same fast memory pool, which helps model performance more than you'd expect from a machine with no dedicated GPU. The limitation is there's no external GPU upgrade path if you outgrow it later; a Mac Mini or MacBook is what it is for the life of that machine.

Our recommendation

If you're buying hardware specifically for this and want the setup this site walks through - a quiet, always-on, no-monitor server - go with a Linux mini PC. It's what our mini PC guide assumes throughout, and it's the least friction for the headless, background-service pattern this whole approach is built around.

If you already have a decent Windows PC with an NVIDIA GPU, or an Apple Silicon Mac, use it. The GPU-specific setup for larger models is covered in Building a Full PC for Local AI, and Ollama's install process is the same one-line command regardless of platform.

What actually goes wrong

WSL2 on Windows can't see the GPU

If you're running Ollama inside WSL2 (Windows Subsystem for Linux) rather than natively on Windows, GPU passthrough requires the NVIDIA driver to be installed on the Windows side first, not inside WSL2 itself. Installing a separate driver inside the WSL2 environment is a common mistake and actually breaks GPU access rather than fixing it.

Ubuntu install hangs or won't boot on a new GPU

This is very often Secure Boot blocking an unsigned NVIDIA driver, not a broken installation. Disabling Secure Boot in the BIOS before installing Nvidia drivers resolves this in most cases - see the driver troubleshooting section in Building a Full PC for Local AI for the full walkthrough.

Someone tells you Apple Silicon needs Rosetta for this

It doesn't. Ollama ships a native Apple Silicon build - no emulation, no Rosetta, no performance penalty. If a specific tool you're trying to use does need Rosetta, that's a sign the tool itself doesn't have a native ARM build yet, not a general requirement for running local AI on a Mac.

Common questions