Ollama running on Windows 11 is a near-effortless way to host local large language models, and for most users the native Windows app is the fastest path from download to chat — but for developers, researchers, and GPU tinkerers, installing the Linux build inside WSL (Windows Subsystem for Linux) unlocks a familiar nix workflow and, in many cases, identical GPU performance. The practical truth: you don't have to run Ollama in WSL to enjoy local LLMs on Windows 11, but if you already live in a Linux-first toolchain or need fine-grained control of CUDA and system services, you’ll appreciate what WSL brings to the table. This feature unpacks both approaches, verifies the technical trade-offs, and offers a clear, actionable guide to when — and why — you should consider WSL for Ollama.
Ollama offers two well-supported ways to run local LLMs on a Windows 11 PC:
Two important verification points:
Ultimately, the decision hinges on workflow, not raw performance: if you already use Linux tooling, WSL makes sense and won’t cost you inference speed. If you want the least-friction route to local LLMs, install the Windows app and get started. Either way, Ollama on Windows 11 gives users two robust, practical ways to run local LLMs — and that choice is a rare win for both convenience-focused and developer-focused audiences alike.
Source: Windows Central Why you don't need to run Ollama in WSL on Windows 11 — but you'll love it if you do
Ollama offers two well-supported ways to run local LLMs on a Windows 11 PC:- The native Windows app — easiest to install and use, includes a GUI and is the quickest route for non-developers.
- The Linux build inside WSL (Ubuntu commonly used) — requires extra setup (WSL, vendor GPU drivers, CUDA toolkit in the distro), but integrates cleanly with Linux workflows and delivers comparable GPU performance when configured correctly.
Background: why two ways exist and what they mean
Ollama provides a cross-platform runtime for running open-weight models locally. On Windows, a native application abstracts away the CLI and places model management in a GUI; on Linux, the same runtime is distributed as a Linux binary or systemd service that many developers prefer for scripting and automation.- The Windows path prioritizes simplicity: download and run the installer, and Ollama stores models under your user profile by default. For everyday use and quick experimentation, the Windows GUI is the least-friction option. (ollama.dev)
- The WSL path is about environment parity: a developer who uses Ubuntu, has containerized tooling, or wants systemd-managed services will often prefer the Linux install inside WSL. Under WSL 2 you can also access the host GPU via vendor drivers and a WSL-specific CUDA toolkit. Official vendor documentation from NVIDIA and distribution guidance from Ubuntu explain the WSL-specific CUDA workflow and caveats. (docs.nvidia.com, documentation.ubuntu.com)
Getting Ollama set up on WSL — prerequisites and quick checklist
If you decide to use WSL for Ollama, here’s the minimal, practical preflight checklist:- Windows and WSL
- Install WSL 2 and your chosen distro (Ubuntu is the most widely documented route). Use the one-line installer or Microsoft Store flows and ensure WSL is up to date. WSL 2 runs in a lightweight utility VM and supports GPU access with the correct drivers. (learn.microsoft.com)
- Vendor GPU driver for WSL
- Install the vendor’s WSL-capable GPU driver on Windows (for NVIDIA, the "NVIDIA GPU Driver for WSL" / WDDM driver). This driver exposes the GPU to the WSL VM without requiring you to install a Linux display driver inside the distro. NVIDIA’s CUDA-on-WSL guide explains the required driver packaging and versions. (readkong.com, docs.nvidia.com)
- WSL-specific CUDA toolkit (in the distro)
- Install the CUDA toolkit package that’s specifically packaged for WSL/Ubuntu inside your distro (do not install the regular Linux NVIDIA driver in WSL; the Windows driver is mapped into WSL). Ubuntu and NVIDIA documentation provide explicit commands to add the WSL CUDA repository and install the toolkit. (documentation.ubuntu.com, docs.nvidia.com)
- Install Ollama inside the distro
- Run the Ollama install script or package inside your Ubuntu WSL instance (common flow uses curl | sh or the official install script). Systemd service setup is often used for a persistent ollama service. Community guides provide example commands and service unit snippets for systemd-managed installs. (blackmoreops.com, ollama.readthedocs.io)
- Allocate resources carefully
- Adjust WSL's memory and CPU limits with a .wslconfig file if you want predictable, capped resource usage. Use wsl --shutdown to apply certain .wslconfig changes, or to restart the utility VM when switching between WSL and native Windows Ollama to avoid model path confusion (details below). (learn.microsoft.com)
Practical differences you’ll notice day-to-day
Installation friction
- Native Windows: one installer, GUI, and a system tray app. Minimal terminal work.
- WSL: a handful more steps — install WSL, vendor driver, CUDA toolkit in the distro, then Ollama. It’s not hard for a developer, but it’s more to manage.
Model storage and discovery
- Ollama stores models in platform-specific default paths (Windows: C:\Users\<you>.ollama\models; Linux: ~/.ollama/models or system path when installed as a service). You can change the model directory with the OLLAMA_MODELS environment variable. That difference explains why Ollama in Windows and Ollama in WSL can appear to maintain separate model catalogs if both are running concurrently. (ollama.readthedocs.io, selfhost.esc.sh)
GUI vs CLI
- Windows gives a GUI and makes it easy to tinker with sliders (e.g., context length). WSL installs are usually headless or CLI-first (though WSLg can run Linux GUIs on the desktop). For day-to-day conversational use, the Windows GUI is friendlier; for scripted or reproducible experiments, the Linux environment shines. (ollama.dev)
Resource usage
- Running WSL introduces the WSL utility VM (visible as vmmem). If you leave WSL running, it will reserve RAM/CPU up to the configured limits. If your model fits entirely in GPU VRAM, that reservation matters less; if Ollama spills into system RAM, WSL memory settings will matter. You can cap WSL’s resource footprint using .wslconfig and shut WSL down with wsl --shutdown when switching back to the native Windows install. Microsoft documents the shutdown command and its behavior. (learn.microsoft.com)
Performance: the practical tests and what they mean
Windows Central ran a simple, pragmatic comparison on an NVIDIA GPU and reported nearly identical token-throughput numbers between WSL and native Windows for several models (deepseek-r1:14b, gpt-oss:20b, magistral:24b, gemma3:27b). The tokens-per-second results were effectively the same across Windows native and WSL runs for both story-generation and code-generation prompts. These numbers suggest that, with proper driver and CUDA toolkit setup, WSL does not inherently slow GPU-accelerated inference for Ollama.Two important verification points:
- The WSL GPU plumbing depends on the vendor-supplied WSL driver and a WSL-friendly CUDA toolkit. NVIDIA’s CUDA on WSL guide and Ubuntu’s WSL CUDA howto describe the exact driver/toolkit arrangement required — follow them to replicate parity. (docs.nvidia.com, documentation.ubuntu.com)
- The Windows Central numbers are an illustrative set of tests conducted on a particular machine under particular conditions; they are not a universal benchmark across all GPUs, drivers, model quantizations, and configurations. Readers should treat these numbers as a strong signal that parity is possible, not an iron-clad guarantee for every hardware stack.
Troubleshooting: common gotchas and fixes
- Problem: Ollama on Windows "loses" models that were downloaded in WSL (or vice versa).
- Why: by default, each environment looks in its native model directory. If WSL is active as a background service, the running Ollama instance will look at the Linux-side model directory and the Windows instance at the Windows-side directory.
- Fix: either set OLLAMA_MODELS to the same shared location (careful with permissions and mount points), move models and use a symlink, or shut WSL down before switching to the Windows app using wsl --shutdown. Microsoft documents the shutdown command which terminates all running WSL distros and the utility VM. (ollama.readthedocs.io, learn.microsoft.com)
- Problem: GPU acceleration fails in WSL.
- Why: missing or wrong Windows vendor driver (the WSL-capable driver), or installing the Linux NVIDIA driver inside WSL (incorrect for WSL), or not installing the WSL-specific CUDA packages inside the distro.
- Fix: install the Windows vendor driver for WSL (NVIDIA’s WSL driver), then the WSL-packaged CUDA toolkit in Ubuntu using the NVIDIA/Ubuntu instructions. Do not install the Linux display driver package inside WSL; that driver is supplied by Windows in the paravirtualized flow. (readkong.com, documentation.ubuntu.com)
- Problem: WSL consumes more RAM/CPU than expected.
- Fix: add a .wslconfig file to your user profile to cap memory and processors, then run wsl --shutdown to apply the change. Microsoft and community guides document the .wslconfig and wsl --shutdown behaviors. (learn.microsoft.com)
- Problem: Windows freezes after running wsl --shutdown (rare anecdotal reports).
- Caveat: some users have reported problems in edge cases with particular Windows builds or driver/firmware combos; if you hit this, test driver versions and Windows updates, and consider using wsl --terminate <distro> for individual distros as a safer alternative where appropriate. Microsoft’s docs and community discussion threads explain both commands and known issues. (superuser.com, github.com)
Security, privacy, and operational considerations
- Local models mean local data: running models purely on-device improves privacy because your prompts and data need not be sent to cloud APIs. That’s a major security advantage of Ollama’s local-first design. But local models may still contain problematic outputs or proprietary weights; check model licenses and vendor terms before production use.
- Model storage and disk use: Large models are large files. Ollama’s default Windows model location is under your user profile — move that folder or change OLLAMA_MODELS if your system SSD is small. Running many model variants or saving multiple quantized versions can quickly consume hundreds of gigabytes. (selfhost.esc.sh, igoroseledko.com)
- Thermals and hardware longevity: sustained GPU saturation during inference generates heat and power draw. For long-running experiments, ensure adequate cooling and consider limiting power/temperature if your hardware supplier supports it. Windows Central and community guidance both highlight thermal considerations when running long, GPU-heavy sessions. (windowscentral.com)
- Enterprise and update management: if you deploy Ollama for teams, WSL introduces another platform to manage. Vet WSL tooling against enterprise policies (antivirus exceptions for WSL VHDX files, update/testing for vendor drivers), and document where models and data live. Community posts and Microsoft docs call out the operational trade-offs for WSL in managed environments.
Workflow recommendations: when to run which
- If you want a turnkey, low-friction LLM experience on Windows for experimenting, demos, or integrating with browser extensions and GUI workflows: use the native Windows Ollama app. It’s fast to install, easy to use, and requires minimal systems knowledge. (ollama.dev, windowscentral.com)
- If you’re a developer who:
- Already uses Linux tooling, containers, systemd services, or
- Needs reproducible scripts, or
- Wants to integrate Ollama into Linux-first CI/CD or data-science pipelines, or
- Prefers the Linux filesystem layout for heavy I/O work,
- If you have a powerful GPU and want to squeeze performance:
- Prioritize VRAM and quantization choices over “WSL vs Windows.” VRAM capacity, context length, and quantization are the dominant performance factors. If your model fits in VRAM, both environments are fast; if it spills to RAM, tune context length and model variants. (windowscentral.com)
Quick start — minimal WSL steps for Ollama (developer-focused)
- Install WSL and Ubuntu (from an elevated PowerShell):
- wsl --install -d ubuntu
- On Windows, install the vendor WSL-capable GPU driver (NVIDIA WSL driver for GeForce/RTX cards). Follow the vendor page for the appropriate package.
- Inside Ubuntu (WSL), add the WSL CUDA repository and install the CUDA toolkit packaged for WSL:
- Follow Ubuntu’s WSL CUDA how-to or NVIDIA’s CUDA-on-WSL guide for the exact commands and repo links. (documentation.ubuntu.com, docs.nvidia.com)
- Install Ollama inside the distro using the official install script, enable and start the ollama service (as appropriate), and verify it has detected the NVIDIA GPU during install.
- If you want to use the Windows app and models to be shared, either relocate the models into a shared path accessible to both environments and set OLLAMA_MODELS, or be disciplined in shutting down WSL (wsl --shutdown) before using the Windows app to avoid duplicate downloads. (ollama.readthedocs.io, learn.microsoft.com)
Critical analysis — strengths, risks, and what to watch
Strengths- Developer parity: WSL brings a full Linux toolchain to Windows and — when paired with vendor WSL drivers and CUDA toolkit — matches native Windows GPU inference throughput in real-world tests. That’s a big win for reproducible dev workflows. (docs.nvidia.com)
- User accessibility: The Windows Ollama GUI reduces the entry barrier for non-developers; for many, it’s the right tool. (windowscentral.com)
- Privacy and offline-first: Local models mean local data custody and offline capability, valuable for privacy-conscious users and sensitive datasets.
- Operational complexity for WSL: driver/toolkit mismatch, .wslconfig tuning, and resource accounting add complexity. Vendors publish clear steps, but mistakes can result in GPU non-availability or wasted time. (readkong.com, docs.nvidia.com)
- Disk and license management: model files are large; storing many variants without a plan leads to rapid storage growth. Also confirm model licensing before production use. (igoroseledko.com)
- Anecdotal benchmarks: published token/s results are valuable but necessarily tied to a specific hardware/driver/configuration. Re-run a small benchmark for your hardware before making procurement decisions. The Windows Central numbers are a useful reference but not universal.
- Improvements in WSLg and vendor-level GPU virtualization will keep narrowing edges where native vs virtualized flows differ. Watch vendor driver release notes and WSL updates for GPU and WSLg improvements. (docs.nvidia.com)
Conclusion
For most Windows 11 users who want to run Ollama and play with local LLMs, the native Windows app is the simplest, most convenient option. It installs quickly, provides a GUI, and is the right choice for education, prototyping, or casual use. For developers who value Linux-native tooling, systemd services, container workflows, or reproducible scripts, running the Linux build of Ollama inside WSL is an excellent option — and when the WSL WDDM driver and CUDA toolkit are installed correctly, it delivers performance on par with the Windows native path.Ultimately, the decision hinges on workflow, not raw performance: if you already use Linux tooling, WSL makes sense and won’t cost you inference speed. If you want the least-friction route to local LLMs, install the Windows app and get started. Either way, Ollama on Windows 11 gives users two robust, practical ways to run local LLMs — and that choice is a rare win for both convenience-focused and developer-focused audiences alike.
Source: Windows Central Why you don't need to run Ollama in WSL on Windows 11 — but you'll love it if you do