Local LLMs on Windows/WSL: Installation and Differences

By Ivo Donker — compiled with AI assistance (Claude & Gemini)

Anyone who wants to run language models locally on a Windows system immediately faces a fundamental architectural choice: do we install the software directly within the Windows environment (native), or do we use the Windows Subsystem for Linux (WSL2)? In this guide, we explore the in-depth pros and cons of both methods, the performance differences under intensive AI workloads, memory allocation, and the concrete steps for setting up a stable environment. A broader overview of the basic options for consumers and enthusiasts is described in Running Local LLMs on Windows: The Complete Guide for Home Users.

The Choice Between Native Windows and WSL2 for AI Workloads

When running large language models, communication between the hardware components — particularly the graphics card (GPU) and system memory — is crucial. Native Windows applications use the DirectX and CUDA drivers supplied directly by the manufacturer for Windows. This often results in a low-threshold installation with graphical installation wizards, as we know them from LM Studio or the official Ollama installer for Windows. The downside of the native route lies in the complexity of command-line tools and Python libraries originally designed for Linux. Many advanced inference frameworks, such as vLLM or custom Python scripts, expect a POSIX-compatible environment, which on pure Windows regularly leads to DLL conflicts, path restrictions, and missing C++ build tools.

On the other hand, WSL2 offers a full Linux kernel that runs seamlessly within Windows. Since the introduction of GPU passthrough for WSL2, Linux-based containers and tools can directly use the host PC's NVIDIA CUDA cores with performance nearly identical to native Windows. This makes WSL2 the go-to route for developers who want to expand their local setup into more complex automations, pipelines, or multi-agent systems. Anyone who wants to move from local experimentation to professional implementation of advanced agents can find further depth in Becoming an AI agent engineer in 2026: from prompt to production.

System Requirements, Hardware Preparation, and Memory Management

Before making a definitive choice between WSL2 or native Windows, it's necessary to have a clear picture of the hardware parameters. Local inference requires sufficient VRAM on the graphics card and fast system memory throughput. For systems with an NVIDIA GeForce RTX card, we first check whether the most recent GeForce Game Ready or Studio Driver is installed. This driver ensures that both the Windows host and the WSL2 environment get direct access to CUDA functionality without needing to manually install drivers within the Linux distribution.

When we evaluate performance, we see that WSL2 requires a small overhead of system memory, typically around one to two gigabytes of extra RAM for the virtual machine and management of the virtual hard disk (VHDX). On systems with limited hardware capacity, such as a graphics card with 8GB or 12GB of VRAM, this extra memory claim can be decisive when loading larger quantized models. It's therefore important to properly configure WSL2's swap settings in the configuration file .wslconfig in the Windows user folder, so the system doesn't suddenly freeze when allocating large context windows.

Installing and Configuring WSL2 for GPU Acceleration

Anyone choosing the flexibility of WSL2 starts by opening PowerShell with administrator rights. Installing the Windows Subsystem for Linux today happens through a single command that activates both the kernel and the default Ubuntu distribution. It's advisable to restart the system after this action to ensure the virtual network layer, the virtualization extensions in the BIOS (such as Intel VT-x or AMD-V), and the drivers are loaded correctly.

In PowerShell, we run the following installation command to set up the virtual environment:

wsl --install -d Ubuntu

After the restart, the Ubuntu terminal opens automatically, where we create a username and password. To verify that the GPU is being correctly passed through to the Linux environment, we install the basic diagnostics and check the status of the NVIDIA drivers within WSL2:

sudo apt update && sudo apt upgrade -y
nvidia-smi

If the output of nvidia-smi shows the correct specifications of the graphics card, the available VRAM, and the installed CUDA version, the environment is ready for use. Any error messages at this stage usually point to an outdated Windows driver on the host, which can be resolved by installing the latest NVIDIA Studio driver on the Windows machine.

Installing and Managing Ollama Within WSL2

With a working WSL2 environment that includes GPU support, we can install inference tools as if working on a dedicated Linux server. In this setup, Ollama is an excellent choice due to its low memory usage and simple model management commands. Within the Ubuntu terminal, we install Ollama with the official installation script:

curl -fsSL https://ollama.com/install.sh | sh

Once installation is complete, we check whether the service is running correctly and responding to API requests. Unlike the native Windows installation, where Ollama runs as a background service in the taskbar, the service in WSL2 typically starts as a systemd service or via a manual command in a tmux session:

ollama serve

The advantage of this Linux approach is that we can store the model files directly on a specific path within the Linux file system (for example, on a fast NVMe drive), minimizing I/O bottlenecks when loading large GGUF files. Connecting Python environments for RAG applications also runs flawlessly as a result.

LM Studio and Native Windows Applications

Anyone who prefers a graphical shell without switching through a Linux terminal opts for the native Windows version of LM Studio or the Windows app of Ollama. These applications offer a visual interface where model weights can be downloaded, tested, and linked to local chat interfaces directly via a search function. Managing context windows, temperature settings, and system prompts happens here via clear sliders and input fields, which increases accessibility.

The major advantage of the native Windows route is seamless integration with file management, keyboard shortcuts, and local audio or video hardware such as microphones for speech-to-text applications. This makes the barrier to daily use considerably lower for users unfamiliar with Linux commands. However, anyone planning to use containers or set up advanced pipelines will more quickly run into limitations on native Windows around path separation and compiling C++ extensions.

Comparing Measurement Methods, Performance, and Token Generation

To determine whether native Windows or WSL2 performs faster in practice, we measure inference speed using two core metrics: the time to first token (TTFT) and the speed of subsequent tokens expressed in tokens per second (t/s). For this, we use standardized benchmark commands in the terminal, such as the evaluation function of Ollama or llama.cpp.

ollama run llama3:8b-instruct-q4_K_M "Schrijf een technisch overzicht van netwerkprotocollen." --verbose

Practical measurements on a test system with an NVIDIA RTX 4080 and 32GB RAM show that the performance differences between native Windows and WSL2 are negligibly small — typically less than a 2 percent deviation in tokens per second. This is because the CUDA layer in WSL2 communicates directly with the hardware via paravirtualization. The real difference lies in the model's startup time and file management overhead. Due to I/O translation between the Windows host and the Linux file system (vhdx), WSL2 can be slightly slower when initially loading very large model files over 30 gigabytes.

Edge Cases, Known Bottlenecks, and Troubleshooting

Every installation method has its specific pitfalls. With WSL2, it regularly happens that the /dev/null or CUDA libraries become temporarily unreachable within the container after an automatic Windows update. This can be resolved by restarting the WSL instance via PowerShell with the command wsl --shutdown. On native Windows, a conflict often arises when multiple applications simultaneously claim the same port, or when antivirus software blocks fast read access to the model files by continuously scanning them.

Memory limits also play a major role. When a model is too large for the available VRAM, the inference framework will automatically try to move layers to regular system memory (RAM). This leads to a dramatic drop in speed. It's crucial to calculate the required VRAM capacity in advance based on the model parameters and the chosen quantization.

Reliability, Output Quality, and Fact-Checking

Regardless of the chosen installation method — native Windows or via WSL2 — the quality of the generated answers still depends on the chosen model and the prompts used. Local language models, just like cloud-based variants, can hallucinate or misrepresent facts. Anyone who wants to be sure the generated text is factually correct can fact-checking AI answers to detect hallucinations early and critically verify them against primary sources.

Privacy, Security, and Local Data Management at Home

One of the main drivers behind running language models locally is complete control over your own data. Unlike commercial cloud services, with a local installation on Windows or WSL2 the entered prompts and documents never leave the hardware at all. This makes the setup particularly well suited for processing privacy-sensitive documents or administrative data within the household. For practical guidelines on protecting personal data within the household and involving housemates, it's useful to Using AI Safely at Home: Practical Tips for Families .

Conclusion

The choice between Windows and WSL2 for running local LLMs primarily depends on the intended use case and the user's technical experience. Anyone looking for a fast, visual experience without technical detours will find a reliable, low-threshold environment in the native Windows applications. Anyone seeking maximum flexibility, wanting to deploy advanced orchestration tools, or wanting to connect scripts to a Linux-based workflow, on the other hand, benefits most from the stability and power of WSL2 with GPU support. By choosing the right hardware and the appropriate installation route, a powerful, fully shielded AI solution emerges on your own computer.